add workbooks for DL distribution rules and a new E2E model selection example with CIFAR10
diff --git a/community-artifacts/Deep-learning/.ipynb_checkpoints/Load-images-v1-checkpoint.ipynb b/community-artifacts/Deep-learning/.ipynb_checkpoints/Load-images-v1-checkpoint.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Load images into table\n",
+ "\n",
+ "This demonstrates different ways to load images into a database table.\n",
+ "\n",
+ "We use the script called <em>madlib_image_loader.py</em> located at https://github.com/apache/madlib-site/tree/asf-site/community-artifacts/Deep-learning which uses the Python Imaging Library so supports multiple formats http://www.pythonware.com/products/pil/\n",
+ "\n",
+ "## Table of contents\n",
+ "\n",
+ "<a href=\"#setup\">1. Setup image loader</a>\n",
+ "\n",
+ "<a href=\"#fetch_numpy\">2. Fetch images then load NumPy array into table</a>\n",
+ "\n",
+ "<a href=\"#file_system\">3. Load from file system into table</a>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/fmcquillan/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated since IPython 4.0. You should import from traitlets.config instead.\n",
+ " \"You should import from traitlets.config instead.\", ShimWarning)\n",
+ "/Users/fmcquillan/anaconda/lib/python2.7/site-packages/IPython/utils/traitlets.py:5: UserWarning: IPython.utils.traitlets has moved to a top-level traitlets package.\n",
+ " warn(\"IPython.utils.traitlets has moved to a top-level traitlets package.\")\n"
+ ]
+ }
+ ],
+ "source": [
+ "%load_ext sql"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "u'Connected: fmcquillan@madlib'"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Greenplum Database 5.x on GCP for deep learning (PM demo machine)\n",
+ "#%sql postgresql://gpadmin@35.239.240.26:5432/madlib\n",
+ " \n",
+ "# PostgreSQL local\n",
+ "%sql postgresql://fmcquillan@localhost:5432/madlib"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>version</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>MADlib version: 1.16, git revision: rc/1.16-rc1, cmake configuration time: Mon Jul 1 17:45:09 UTC 2019, build type: Release, build system: Darwin-16.7.0, C compiler: Clang, C++ compiler: Clang</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'MADlib version: 1.16, git revision: rc/1.16-rc1, cmake configuration time: Mon Jul 1 17:45:09 UTC 2019, build type: Release, build system: Darwin-16.7.0, C compiler: Clang, C++ compiler: Clang',)]"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%sql select madlib.version();\n",
+ "#%sql select version();"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"setup\"></a>\n",
+ "# 1. Set up image loader\n",
+ "\n",
+ "We use the script called <em>madlib_image_loader.py</em> located at https://github.com/apache/madlib-site/tree/asf-site/community-artifacts/Deep-learning"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Using TensorFlow backend.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Couldn't import dot_parser, loading of dot files will not be possible.\n"
+ ]
+ }
+ ],
+ "source": [
+ "import sys\n",
+ "import os\n",
+ "from keras.datasets import cifar10\n",
+ "\n",
+ "madlib_site_dir = '/Users/fmcquillan/Documents/Product/MADlib/Demos/data'\n",
+ "sys.path.append(madlib_site_dir)\n",
+ "\n",
+ "# Import image loader module\n",
+ "from madlib_image_loader import ImageLoader, DbCredentials\n",
+ "\n",
+ "# Specify database credentials, for connecting to db\n",
+ "#db_creds = DbCredentials(user='gpadmin',\n",
+ "# host='35.239.240.26',\n",
+ "# port='5432',\n",
+ "# password='')\n",
+ "\n",
+ "# Specify database credentials, for connecting to db\n",
+ "db_creds = DbCredentials(user='fmcquillan',\n",
+ " host='localhost',\n",
+ " port='5432',\n",
+ " password='')\n",
+ "\n",
+ "# Initialize ImageLoader (increase num_workers to run faster)\n",
+ "iloader = ImageLoader(num_workers=5, db_creds=db_creds)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"fetch_numpy\"></a>\n",
+ "# 2. Fetch images then load NumPy array into table\n",
+ "\n",
+ "<em>iloader.load_dataset_from_np(data_x, data_y, table_name, append=False)</em>\n",
+ "\n",
+ "- <em>data_x</em> contains image data in np.array format\n",
+ "\n",
+ "\n",
+ "- <em>data_y</em> is a 1D np.array of the image categories (labels).\n",
+ "\n",
+ "\n",
+ "- If the user passes a <em>table_name</em> while creating ImageLoader object, it will be used for all further calls to load_dataset_from_np. It can be changed by passing it as a parameter during the actual call to load_dataset_from_np, and if so future calls will load to that table name instead. This avoids needing to pass the table_name again every time, but also allows it to be changed at any time."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Load dataset into np array\n",
+ "(x_train, y_train), (x_test, y_test) = cifar10.load_data()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "MainProcess: Connected to madlib db.\n",
+ "Executing: CREATE TABLE cifar_10_train_data (id SERIAL, x REAL[], y TEXT)\n",
+ "CREATE TABLE\n",
+ "Created table cifar_10_train_data in madlib db\n",
+ "Spawning 5 workers...\n",
+ "Initializing PoolWorker-1 [pid 82412]\n",
+ "PoolWorker-1: Created temporary directory /tmp/madlib_Bt85aChbv0\n",
+ "Initializing PoolWorker-2 [pid 82413]\n",
+ "PoolWorker-2: Created temporary directory /tmp/madlib_cSyCSiEhHT\n",
+ "Initializing PoolWorker-3 [pid 82414]\n",
+ "PoolWorker-3: Created temporary directory /tmp/madlib_uvtHjGCU5S\n",
+ "PoolWorker-1: Connected to madlib db.\n",
+ "Initializing PoolWorker-4 [pid 82415]\n",
+ "PoolWorker-4: Created temporary directory /tmp/madlib_eJmkoDZTr8\n",
+ "PoolWorker-2: Connected to madlib db.\n",
+ "Initializing PoolWorker-5 [pid 82417]\n",
+ "PoolWorker-5: Created temporary directory /tmp/madlib_websbk05x2\n",
+ "PoolWorker-3: Connected to madlib db.\n",
+ "PoolWorker-4: Connected to madlib db.\n",
+ "PoolWorker-5: Connected to madlib db.\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_Bt85aChbv0/cifar_10_train_data0000.tmp\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_cSyCSiEhHT/cifar_10_train_data0000.tmp\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_uvtHjGCU5S/cifar_10_train_data0000.tmp\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_eJmkoDZTr8/cifar_10_train_data0000.tmp\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_websbk05x2/cifar_10_train_data0000.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-2: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-3: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-4: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-5: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_Bt85aChbv0/cifar_10_train_data0001.tmp\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_cSyCSiEhHT/cifar_10_train_data0001.tmp\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_uvtHjGCU5S/cifar_10_train_data0001.tmp\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_eJmkoDZTr8/cifar_10_train_data0001.tmp\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_websbk05x2/cifar_10_train_data0001.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-2: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-3: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-4: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-5: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_Bt85aChbv0/cifar_10_train_data0002.tmp\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_cSyCSiEhHT/cifar_10_train_data0002.tmp\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_uvtHjGCU5S/cifar_10_train_data0002.tmp\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_eJmkoDZTr8/cifar_10_train_data0002.tmp\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_websbk05x2/cifar_10_train_data0002.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-2: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-3: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-4: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-5: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_Bt85aChbv0/cifar_10_train_data0003.tmp\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_cSyCSiEhHT/cifar_10_train_data0003.tmp\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_uvtHjGCU5S/cifar_10_train_data0003.tmp\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_eJmkoDZTr8/cifar_10_train_data0003.tmp\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_websbk05x2/cifar_10_train_data0003.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-2: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-3: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-4: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-5: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_Bt85aChbv0/cifar_10_train_data0004.tmp\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_cSyCSiEhHT/cifar_10_train_data0004.tmp\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_uvtHjGCU5S/cifar_10_train_data0004.tmp\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_eJmkoDZTr8/cifar_10_train_data0004.tmp\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_websbk05x2/cifar_10_train_data0004.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-2: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-3: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-4: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-5: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_Bt85aChbv0/cifar_10_train_data0005.tmp\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_cSyCSiEhHT/cifar_10_train_data0005.tmp\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_uvtHjGCU5S/cifar_10_train_data0005.tmp\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_eJmkoDZTr8/cifar_10_train_data0005.tmp\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_websbk05x2/cifar_10_train_data0005.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-2: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-3: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-4: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-5: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_Bt85aChbv0/cifar_10_train_data0006.tmp\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_cSyCSiEhHT/cifar_10_train_data0006.tmp\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_uvtHjGCU5S/cifar_10_train_data0006.tmp\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_eJmkoDZTr8/cifar_10_train_data0006.tmp\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_websbk05x2/cifar_10_train_data0006.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-2: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-3: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-4: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-5: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_Bt85aChbv0/cifar_10_train_data0007.tmp\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_cSyCSiEhHT/cifar_10_train_data0007.tmp\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_uvtHjGCU5S/cifar_10_train_data0007.tmp\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_eJmkoDZTr8/cifar_10_train_data0007.tmp\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_websbk05x2/cifar_10_train_data0007.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-2: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-3: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-4: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-5: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_Bt85aChbv0/cifar_10_train_data0008.tmp\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_cSyCSiEhHT/cifar_10_train_data0008.tmp\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_uvtHjGCU5S/cifar_10_train_data0008.tmp\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_eJmkoDZTr8/cifar_10_train_data0008.tmp\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_websbk05x2/cifar_10_train_data0008.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-2: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-3: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-4: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-5: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_Bt85aChbv0/cifar_10_train_data0009.tmp\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_cSyCSiEhHT/cifar_10_train_data0009.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-2: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_Bt85aChbv0/cifar_10_train_data0010.tmp\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_cSyCSiEhHT/cifar_10_train_data0010.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-2: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_Bt85aChbv0/cifar_10_train_data0011.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-2: Removed temporary directory /tmp/madlib_cSyCSiEhHT\n",
+ "PoolWorker-3: Removed temporary directory /tmp/madlib_uvtHjGCU5S\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "PoolWorker-5: Removed temporary directory /tmp/madlib_websbk05x2\n",
+ "PoolWorker-4: Removed temporary directory /tmp/madlib_eJmkoDZTr8\n",
+ "PoolWorker-1: Removed temporary directory /tmp/madlib_Bt85aChbv0\n",
+ "Done! Loaded 50000 images in 24.2222080231s\n",
+ "5 workers terminated.\n",
+ "MainProcess: Connected to madlib db.\n",
+ "Executing: CREATE TABLE cifar_10_test_data (id SERIAL, x REAL[], y TEXT)\n",
+ "CREATE TABLE\n",
+ "Created table cifar_10_test_data in madlib db\n",
+ "Spawning 5 workers...\n",
+ "Initializing PoolWorker-6 [pid 82423]\n",
+ "PoolWorker-6: Created temporary directory /tmp/madlib_e615zVgkaE\n",
+ "Initializing PoolWorker-7 [pid 82424]\n",
+ "PoolWorker-7: Created temporary directory /tmp/madlib_iRi2oMNIFA\n",
+ "Initializing PoolWorker-8 [pid 82425]\n",
+ "PoolWorker-8: Created temporary directory /tmp/madlib_kkSktVCq3n\n",
+ "PoolWorker-6: Connected to madlib db.\n",
+ "Initializing PoolWorker-9 [pid 82426]\n",
+ "PoolWorker-7: Connected to madlib db.\n",
+ "PoolWorker-9: Created temporary directory /tmp/madlib_0To3XX96yI\n",
+ "Initializing PoolWorker-10 [pid 82428]\n",
+ "PoolWorker-8: Connected to madlib db.\n",
+ "PoolWorker-10: Created temporary directory /tmp/madlib_8zwK04IJsc\n",
+ "PoolWorker-9: Connected to madlib db.\n",
+ "PoolWorker-10: Connected to madlib db.\n",
+ "PoolWorker-6: Wrote 1000 images to /tmp/madlib_e615zVgkaE/cifar_10_test_data0000.tmp\n",
+ "PoolWorker-7: Wrote 1000 images to /tmp/madlib_iRi2oMNIFA/cifar_10_test_data0000.tmp\n",
+ "PoolWorker-8: Wrote 1000 images to /tmp/madlib_kkSktVCq3n/cifar_10_test_data0000.tmp\n",
+ "PoolWorker-9: Wrote 1000 images to /tmp/madlib_0To3XX96yI/cifar_10_test_data0000.tmp\n",
+ "PoolWorker-10: Wrote 1000 images to /tmp/madlib_8zwK04IJsc/cifar_10_test_data0000.tmp\n",
+ "PoolWorker-6: Loaded 1000 images into cifar_10_test_data\n",
+ "PoolWorker-7: Loaded 1000 images into cifar_10_test_data\n",
+ "PoolWorker-8: Loaded 1000 images into cifar_10_test_data\n",
+ "PoolWorker-10: Loaded 1000 images into cifar_10_test_data\n",
+ "PoolWorker-9: Loaded 1000 images into cifar_10_test_data\n",
+ "PoolWorker-6: Wrote 1000 images to /tmp/madlib_e615zVgkaE/cifar_10_test_data0001.tmp\n",
+ "PoolWorker-7: Wrote 1000 images to /tmp/madlib_iRi2oMNIFA/cifar_10_test_data0001.tmp\n",
+ "PoolWorker-8: Wrote 1000 images to /tmp/madlib_kkSktVCq3n/cifar_10_test_data0001.tmp\n",
+ "PoolWorker-9: Wrote 1000 images to /tmp/madlib_0To3XX96yI/cifar_10_test_data0001.tmp\n",
+ "PoolWorker-10: Wrote 1000 images to /tmp/madlib_8zwK04IJsc/cifar_10_test_data0001.tmp\n",
+ "PoolWorker-6: Loaded 1000 images into cifar_10_test_data\n",
+ "PoolWorker-7: Loaded 1000 images into cifar_10_test_data\n",
+ "PoolWorker-8: Loaded 1000 images into cifar_10_test_data\n",
+ "PoolWorker-9: Loaded 1000 images into cifar_10_test_data\n",
+ "PoolWorker-10: Loaded 1000 images into cifar_10_test_data\n",
+ "PoolWorker-10: Removed temporary directory /tmp/madlib_8zwK04IJsc\n",
+ "PoolWorker-8: Removed temporary directory /tmp/madlib_kkSktVCq3n\n",
+ "PoolWorker-7: Removed temporary directory /tmp/madlib_iRi2oMNIFA\n",
+ "PoolWorker-6: Removed temporary directory /tmp/madlib_e615zVgkaE\n",
+ "PoolWorker-9: Removed temporary directory /tmp/madlib_0To3XX96yI\n",
+ "Done! Loaded 10000 images in 4.6932258606s\n",
+ "5 workers terminated.\n"
+ ]
+ }
+ ],
+ "source": [
+ "%sql DROP TABLE IF EXISTS cifar_10_train_data, cifar_10_test_data;\n",
+ "\n",
+ "# Save images to temporary directories and load into database\n",
+ "iloader.load_dataset_from_np(x_train, y_train, 'cifar_10_train_data', append=False)\n",
+ "iloader.load_dataset_from_np(x_test, y_test, 'cifar_10_test_data', append=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>50000</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
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+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT COUNT(*) FROM cifar_10_train_data;"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>10000</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(10000L,)]"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT COUNT(*) FROM cifar_10_test_data;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"file_system\"></a>\n",
+ "# 3. Load from file system\n",
+ "\n",
+ "Uses the Python Imaging Library so supports multiple formats\n",
+ "http://www.pythonware.com/products/pil/\n",
+ "\n",
+ "<em>load_dataset_from_disk(root_dir, table_name, num_labels='all', append=False)</em>\n",
+ "\n",
+ "- Calling this function will look in <em>root_dir</em> on the local disk of wherever this is being run. It will skip over any files in that directory, but will load images contained in each of its subdirectories. The images should be organized by category/class, where the name of each subdirectory is the label for the images contained within it.\n",
+ "\n",
+ "\n",
+ "- The <em>table_name</em> and <em>append</em> parameters are the same as above The parameter <em>num_labels</em> is an optional parameter which can be used to restrict the number of labels (image classes) loaded, even if more are found in <em>root_dir</em>. For example, for a large dataset you may have hundreds of labels, but only wish to use a subset of that containing a few dozen.\n",
+ "\n",
+ "For example, if we put the CIFAR-10 training data is in 10 subdirectories under directory <em>cifar10</em>, with one subdirectory for each class:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "MainProcess: Connected to madlib db.\n",
+ "Executing: CREATE TABLE cifar_10_train_data_filesystem (id SERIAL, x REAL[], y TEXT, img_name TEXT)\n",
+ "CREATE TABLE\n",
+ "Created table cifar_10_train_data_filesystem in madlib db\n",
+ ".DS_Store is not a directory, skipping\n",
+ "number of labels = 10\n",
+ "Found 10 image labels in /Users/fmcquillan/tmp/cifar10\n",
+ "Spawning 5 workers...\n",
+ "Initializing PoolWorker-11 [pid 82438]\n",
+ "PoolWorker-11: Created temporary directory /tmp/madlib_aEC1lF2HqL\n",
+ "Initializing PoolWorker-12 [pid 82439]\n",
+ "PoolWorker-12: Created temporary directory /tmp/madlib_70qpwFzzqW\n",
+ "Initializing PoolWorker-13 [pid 82440]\n",
+ "PoolWorker-13: Created temporary directory /tmp/madlib_r2u4Zo5bPt\n",
+ "PoolWorker-11: Connected to madlib db.\n",
+ "Initializing PoolWorker-14 [pid 82441]\n",
+ "PoolWorker-12: Connected to madlib db.\n",
+ "PoolWorker-14: Created temporary directory /tmp/madlib_aTPESoNjVi\n",
+ "Initializing PoolWorker-15 [pid 82443]\n",
+ "PoolWorker-13: Connected to madlib db.\n",
+ "PoolWorker-15: Created temporary directory /tmp/madlib_rhVwjLTbWI\n",
+ "PoolWorker-14: Connected to madlib db.\n",
+ "PoolWorker-15: Connected to madlib db.\n",
+ "PoolWorker-13: Wrote 1000 images to /tmp/madlib_r2u4Zo5bPt/cifar_10_train_data_filesystem0000.tmp\n",
+ "PoolWorker-14: Wrote 1000 images to /tmp/madlib_aTPESoNjVi/cifar_10_train_data_filesystem0000.tmp\n",
+ "PoolWorker-12: Wrote 1000 images to /tmp/madlib_70qpwFzzqW/cifar_10_train_data_filesystem0000.tmp\n",
+ "PoolWorker-11: Wrote 1000 images to /tmp/madlib_aEC1lF2HqL/cifar_10_train_data_filesystem0000.tmp\n",
+ "PoolWorker-15: Wrote 1000 images to /tmp/madlib_rhVwjLTbWI/cifar_10_train_data_filesystem0000.tmp\n",
+ "PoolWorker-13: Loaded 1000 images into cifar_10_train_data_filesystem\n",
+ "PoolWorker-14: Loaded 1000 images into cifar_10_train_data_filesystem\n",
+ "PoolWorker-11: Loaded 1000 images into cifar_10_train_data_filesystem\n",
+ "PoolWorker-12: Loaded 1000 images into cifar_10_train_data_filesystem\n",
+ "PoolWorker-15: Loaded 1000 images into cifar_10_train_data_filesystem\n",
+ "PoolWorker-14: Wrote 1000 images to /tmp/madlib_aTPESoNjVi/cifar_10_train_data_filesystem0001.tmp\n",
+ "PoolWorker-11: Wrote 1000 images to /tmp/madlib_aEC1lF2HqL/cifar_10_train_data_filesystem0001.tmp\n",
+ "PoolWorker-13: Wrote 1000 images to /tmp/madlib_r2u4Zo5bPt/cifar_10_train_data_filesystem0001.tmp\n",
+ "PoolWorker-15: Wrote 1000 images to /tmp/madlib_rhVwjLTbWI/cifar_10_train_data_filesystem0001.tmp\n",
+ "PoolWorker-12: Wrote 1000 images to /tmp/madlib_70qpwFzzqW/cifar_10_train_data_filesystem0001.tmp\n",
+ "PoolWorker-14: Loaded 1000 images into cifar_10_train_data_filesystem\n",
+ "PoolWorker-11: Loaded 1000 images into cifar_10_train_data_filesystem\n",
+ "PoolWorker-13: Loaded 1000 images into cifar_10_train_data_filesystem\n",
+ "PoolWorker-15: Loaded 1000 images into cifar_10_train_data_filesystem\n",
+ "PoolWorker-12: Loaded 1000 images into cifar_10_train_data_filesystem\n",
+ "PoolWorker-14: Wrote 1000 images to /tmp/madlib_aTPESoNjVi/cifar_10_train_data_filesystem0002.tmp\n",
+ "PoolWorker-15: Wrote 1000 images to /tmp/madlib_rhVwjLTbWI/cifar_10_train_data_filesystem0002.tmp\n",
+ "PoolWorker-13: Wrote 1000 images to /tmp/madlib_r2u4Zo5bPt/cifar_10_train_data_filesystem0002.tmp\n",
+ "PoolWorker-11: Wrote 1000 images to /tmp/madlib_aEC1lF2HqL/cifar_10_train_data_filesystem0002.tmp\n",
+ "PoolWorker-12: Wrote 1000 images to /tmp/madlib_70qpwFzzqW/cifar_10_train_data_filesystem0002.tmp\n",
+ "PoolWorker-14: Loaded 1000 images into cifar_10_train_data_filesystem\n",
+ "PoolWorker-11: Loaded 1000 images into cifar_10_train_data_filesystem\n",
+ "PoolWorker-15: Loaded 1000 images into cifar_10_train_data_filesystem\n",
+ "PoolWorker-13: Loaded 1000 images into cifar_10_train_data_filesystem\n",
+ "PoolWorker-12: Loaded 1000 images into cifar_10_train_data_filesystem\n",
+ "PoolWorker-15: Wrote 1000 images to /tmp/madlib_rhVwjLTbWI/cifar_10_train_data_filesystem0003.tmp\n",
+ "PoolWorker-14: Wrote 1000 images to /tmp/madlib_aTPESoNjVi/cifar_10_train_data_filesystem0003.tmp\n",
+ "PoolWorker-13: Wrote 1000 images to /tmp/madlib_r2u4Zo5bPt/cifar_10_train_data_filesystem0003.tmp\n",
+ "PoolWorker-12: Wrote 1000 images to /tmp/madlib_70qpwFzzqW/cifar_10_train_data_filesystem0003.tmp\n",
+ "PoolWorker-11: Wrote 1000 images to /tmp/madlib_aEC1lF2HqL/cifar_10_train_data_filesystem0003.tmp\n",
+ "PoolWorker-15: Loaded 1000 images into cifar_10_train_data_filesystem\n",
+ "PoolWorker-14: Loaded 1000 images into cifar_10_train_data_filesystem\n",
+ "PoolWorker-13: Loaded 1000 images into cifar_10_train_data_filesystem\n",
+ "PoolWorker-11: Loaded 1000 images into cifar_10_train_data_filesystem\n",
+ "PoolWorker-12: Loaded 1000 images into cifar_10_train_data_filesystem\n",
+ "PoolWorker-15: Wrote 1000 images to /tmp/madlib_rhVwjLTbWI/cifar_10_train_data_filesystem0004.tmp\n",
+ "PoolWorker-14: Wrote 1000 images to /tmp/madlib_aTPESoNjVi/cifar_10_train_data_filesystem0004.tmp\n",
+ "PoolWorker-12: Wrote 1000 images to /tmp/madlib_70qpwFzzqW/cifar_10_train_data_filesystem0004.tmp\n",
+ "PoolWorker-13: Wrote 1000 images to /tmp/madlib_r2u4Zo5bPt/cifar_10_train_data_filesystem0004.tmp\n",
+ "PoolWorker-11: Wrote 1000 images to /tmp/madlib_aEC1lF2HqL/cifar_10_train_data_filesystem0004.tmp\n",
+ "PoolWorker-14: Loaded 1000 images into cifar_10_train_data_filesystem\n",
+ "PoolWorker-15: Loaded 1000 images into cifar_10_train_data_filesystem\n",
+ "PoolWorker-12: Loaded 1000 images into cifar_10_train_data_filesystem\n",
+ "PoolWorker-11: Loaded 1000 images into cifar_10_train_data_filesystem\n",
+ "PoolWorker-13: Loaded 1000 images into cifar_10_train_data_filesystem\n",
+ "PoolWorker-15: Wrote 1000 images to /tmp/madlib_rhVwjLTbWI/cifar_10_train_data_filesystem0005.tmp\n",
+ "PoolWorker-14: Wrote 1000 images to /tmp/madlib_aTPESoNjVi/cifar_10_train_data_filesystem0005.tmp\n",
+ "PoolWorker-12: Wrote 1000 images to /tmp/madlib_70qpwFzzqW/cifar_10_train_data_filesystem0005.tmp\n",
+ "PoolWorker-11: Wrote 1000 images to /tmp/madlib_aEC1lF2HqL/cifar_10_train_data_filesystem0005.tmp\n",
+ "PoolWorker-13: Wrote 1000 images to /tmp/madlib_r2u4Zo5bPt/cifar_10_train_data_filesystem0005.tmp\n",
+ "PoolWorker-14: Loaded 1000 images into cifar_10_train_data_filesystem\n",
+ "PoolWorker-15: Loaded 1000 images into cifar_10_train_data_filesystem\n",
+ "PoolWorker-12: Loaded 1000 images into cifar_10_train_data_filesystem\n",
+ "PoolWorker-13: Loaded 1000 images into cifar_10_train_data_filesystem\n",
+ "PoolWorker-11: Loaded 1000 images into cifar_10_train_data_filesystem\n",
+ "PoolWorker-15: Wrote 1000 images to /tmp/madlib_rhVwjLTbWI/cifar_10_train_data_filesystem0006.tmp\n",
+ "PoolWorker-14: Wrote 1000 images to /tmp/madlib_aTPESoNjVi/cifar_10_train_data_filesystem0006.tmp\n",
+ "PoolWorker-12: Wrote 1000 images to /tmp/madlib_70qpwFzzqW/cifar_10_train_data_filesystem0006.tmp\n",
+ "PoolWorker-13: Wrote 1000 images to /tmp/madlib_r2u4Zo5bPt/cifar_10_train_data_filesystem0006.tmp\n",
+ "PoolWorker-11: Wrote 1000 images to /tmp/madlib_aEC1lF2HqL/cifar_10_train_data_filesystem0006.tmp\n",
+ "PoolWorker-15: Loaded 1000 images into cifar_10_train_data_filesystem\n",
+ "PoolWorker-14: Loaded 1000 images into cifar_10_train_data_filesystem\n",
+ "PoolWorker-12: Loaded 1000 images into cifar_10_train_data_filesystem\n",
+ "PoolWorker-11: Loaded 1000 images into cifar_10_train_data_filesystem\n",
+ "PoolWorker-13: Loaded 1000 images into cifar_10_train_data_filesystem\n",
+ "PoolWorker-15: Wrote 1000 images to /tmp/madlib_rhVwjLTbWI/cifar_10_train_data_filesystem0007.tmp\n",
+ "PoolWorker-14: Wrote 1000 images to /tmp/madlib_aTPESoNjVi/cifar_10_train_data_filesystem0007.tmp\n",
+ "PoolWorker-12: Wrote 1000 images to /tmp/madlib_70qpwFzzqW/cifar_10_train_data_filesystem0007.tmp\n",
+ "PoolWorker-13: Wrote 1000 images to /tmp/madlib_r2u4Zo5bPt/cifar_10_train_data_filesystem0007.tmp\n",
+ "PoolWorker-11: Wrote 1000 images to /tmp/madlib_aEC1lF2HqL/cifar_10_train_data_filesystem0007.tmp\n",
+ "PoolWorker-15: Loaded 1000 images into cifar_10_train_data_filesystem\n",
+ "PoolWorker-14: Loaded 1000 images into cifar_10_train_data_filesystem\n",
+ "PoolWorker-12: Loaded 1000 images into cifar_10_train_data_filesystem\n",
+ "PoolWorker-13: Loaded 1000 images into cifar_10_train_data_filesystem\n",
+ "PoolWorker-11: Loaded 1000 images into cifar_10_train_data_filesystem\n",
+ "PoolWorker-14: Wrote 1000 images to /tmp/madlib_aTPESoNjVi/cifar_10_train_data_filesystem0008.tmp\n",
+ "PoolWorker-15: Wrote 1000 images to /tmp/madlib_rhVwjLTbWI/cifar_10_train_data_filesystem0008.tmp\n",
+ "PoolWorker-12: Wrote 1000 images to /tmp/madlib_70qpwFzzqW/cifar_10_train_data_filesystem0008.tmp\n",
+ "PoolWorker-13: Wrote 1000 images to /tmp/madlib_r2u4Zo5bPt/cifar_10_train_data_filesystem0008.tmp\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "PoolWorker-11: Wrote 1000 images to /tmp/madlib_aEC1lF2HqL/cifar_10_train_data_filesystem0008.tmp\n",
+ "PoolWorker-14: Loaded 1000 images into cifar_10_train_data_filesystem\n",
+ "PoolWorker-15: Loaded 1000 images into cifar_10_train_data_filesystem\n",
+ "PoolWorker-12: Loaded 1000 images into cifar_10_train_data_filesystem\n",
+ "PoolWorker-13: Loaded 1000 images into cifar_10_train_data_filesystem\n",
+ "PoolWorker-11: Loaded 1000 images into cifar_10_train_data_filesystem\n",
+ "PoolWorker-14: Wrote 1000 images to /tmp/madlib_aTPESoNjVi/cifar_10_train_data_filesystem0009.tmp\n",
+ "PoolWorker-15: Wrote 1000 images to /tmp/madlib_rhVwjLTbWI/cifar_10_train_data_filesystem0009.tmp\n",
+ "PoolWorker-12: Wrote 1000 images to /tmp/madlib_70qpwFzzqW/cifar_10_train_data_filesystem0009.tmp\n",
+ "PoolWorker-13: Wrote 1000 images to /tmp/madlib_r2u4Zo5bPt/cifar_10_train_data_filesystem0009.tmp\n",
+ "PoolWorker-11: Wrote 1000 images to /tmp/madlib_aEC1lF2HqL/cifar_10_train_data_filesystem0009.tmp\n",
+ "PoolWorker-14: Loaded 1000 images into cifar_10_train_data_filesystem\n",
+ "PoolWorker-12: Loaded 1000 images into cifar_10_train_data_filesystem\n",
+ "PoolWorker-15: Loaded 1000 images into cifar_10_train_data_filesystem\n",
+ "PoolWorker-13: Loaded 1000 images into cifar_10_train_data_filesystem\n",
+ "PoolWorker-11: Loaded 1000 images into cifar_10_train_data_filesystem\n",
+ "PoolWorker-12: Removed temporary directory /tmp/madlib_70qpwFzzqW\n",
+ "PoolWorker-13: Removed temporary directory /tmp/madlib_r2u4Zo5bPt\n",
+ "PoolWorker-15: Removed temporary directory /tmp/madlib_rhVwjLTbWI\n",
+ "PoolWorker-11: Removed temporary directory /tmp/madlib_aEC1lF2HqL\n",
+ "PoolWorker-14: Removed temporary directory /tmp/madlib_aTPESoNjVi\n",
+ "Done! Loaded 10 image categories in 27.9927430153s\n",
+ "5 workers terminated.\n"
+ ]
+ }
+ ],
+ "source": [
+ "%sql drop table if exists cifar_10_train_data_filesystem;\n",
+ "# Load images from file system\n",
+ "iloader.load_dataset_from_disk('/Users/fmcquillan/tmp/cifar10', 'cifar_10_train_data_filesystem', num_labels='all', append=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>50000</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(50000L,)]"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT COUNT(*) FROM cifar_10_train_data_filesystem;"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.16"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/community-artifacts/Deep-learning/.ipynb_checkpoints/MADlib-Keras-cifar10-cnn-v3-checkpoint.ipynb b/community-artifacts/Deep-learning/.ipynb_checkpoints/MADlib-Keras-cifar10-cnn-v3-checkpoint.ipynb
new file mode 100644
index 0000000..987ff4b
--- /dev/null
+++ b/community-artifacts/Deep-learning/.ipynb_checkpoints/MADlib-Keras-cifar10-cnn-v3-checkpoint.ipynb
@@ -0,0 +1,1297 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# CNN Using Keras and MADlib\n",
+ "\n",
+ "E2E classification example using MADlib calling a Keras CNN. Based on model architecture in https://keras.io/examples/cifar10_cnn/\n",
+ "\n",
+ "To load images into tables we use the script called <em>madlib_image_loader.py</em> located at https://github.com/apache/madlib-site/tree/asf-site/community-artifacts/Deep-learning which uses the Python Imaging Library so supports multiple formats\n",
+ "http://www.pythonware.com/products/pil/\n",
+ "\n",
+ "\n",
+ "## Table of contents\n",
+ "<a href=\"#import_libraries\">1. Import libraries</a>\n",
+ "\n",
+ "<a href=\"#load_and_prepare_data\">2. Load dataset into table</a>\n",
+ "\n",
+ "<a href=\"#image_preproc\">3. Call image preprocessor</a>\n",
+ "\n",
+ "<a href=\"#define_and_load_model\">4. Define and load model architecture</a>\n",
+ "\n",
+ "<a href=\"#train\">5. Train</a>\n",
+ "\n",
+ "<a href=\"#plot\">6. Plots by iteration and time</a>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The CIFAR-10 dataset consists of 60,000 32x32 colour images in 10 classes, with 6,000 images per class. There are 50,000 training images and 10,000 test images. \n",
+ "\n",
+ "https://www.cs.toronto.edu/~kriz/cifar.html"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/jpeg": 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\n",
+ "text/plain": [
+ "<IPython.core.display.Image object>"
+ ]
+ },
+ "execution_count": 1,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from IPython.display import Image\n",
+ "Image(\"../../images/cifar10.jpg\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/fmcquillan/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated since IPython 4.0. You should import from traitlets.config instead.\n",
+ " \"You should import from traitlets.config instead.\", ShimWarning)\n",
+ "/Users/fmcquillan/anaconda/lib/python2.7/site-packages/IPython/utils/traitlets.py:5: UserWarning: IPython.utils.traitlets has moved to a top-level traitlets package.\n",
+ " warn(\"IPython.utils.traitlets has moved to a top-level traitlets package.\")\n"
+ ]
+ }
+ ],
+ "source": [
+ "%load_ext sql"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "u'Connected: gpadmin@madlib'"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Greenplum Database 5.x on GCP (PM demo machine) - direct external IP access\n",
+ "#%sql postgresql://gpadmin@34.67.65.96:5432/madlib\n",
+ "\n",
+ "# Greenplum Database 5.x on GCP - via tunnel\n",
+ "%sql postgresql://gpadmin@localhost:8000/madlib\n",
+ " \n",
+ "# PostgreSQL local\n",
+ "#%sql postgresql://fmcquillan@localhost:5432/madlib"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>version</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>MADlib version: 1.17-dev, git revision: rel/v1.16-54-gec5614f, cmake configuration time: Wed Dec 18 17:08:05 UTC 2019, build type: release, build system: Linux-3.10.0-1062.4.3.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'MADlib version: 1.17-dev, git revision: rel/v1.16-54-gec5614f, cmake configuration time: Wed Dec 18 17:08:05 UTC 2019, build type: release, build system: Linux-3.10.0-1062.4.3.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5',)]"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%sql select madlib.version();\n",
+ "#%sql select version();"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"import_libraries\"></a>\n",
+ "# 1. Import libraries\n",
+ "From https://keras.io/examples/mnist_transfer_cnn/ import libraries and define some params"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Using TensorFlow backend.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Couldn't import dot_parser, loading of dot files will not be possible.\n"
+ ]
+ }
+ ],
+ "source": [
+ "from __future__ import print_function\n",
+ "import keras\n",
+ "from keras.datasets import cifar10\n",
+ "from keras.preprocessing.image import ImageDataGenerator\n",
+ "from keras.models import Sequential\n",
+ "from keras.layers import Dense, Dropout, Activation, Flatten\n",
+ "from keras.layers import Conv2D, MaxPooling2D\n",
+ "import os\n",
+ "\n",
+ "batch_size = 32\n",
+ "num_classes = 10\n",
+ "epochs = 100"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Others needed in this workbook"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import sys\n",
+ "import os\n",
+ "from matplotlib import pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"load_and_prepare_data\"></a>\n",
+ "# 2. Set up image loader and load dataset into table\n",
+ "\n",
+ "First set up image loader using the script called <em>madlib_image_loader.py</em> located at https://github.com/apache/madlib-site/tree/asf-site/community-artifacts/Deep-learning"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "madlib_site_dir = '/Users/fmcquillan/Documents/Product/MADlib/Demos/data'\n",
+ "sys.path.append(madlib_site_dir)\n",
+ "\n",
+ "# Import image loader module\n",
+ "from madlib_image_loader import ImageLoader, DbCredentials\n",
+ "\n",
+ "# Specify database credentials, for connecting to db\n",
+ "db_creds = DbCredentials(user='fmcquillan',\n",
+ " host='localhost',\n",
+ " port='5432',\n",
+ " password='')\n",
+ "\n",
+ "# Specify database credentials, for connecting to db\n",
+ "#db_creds = DbCredentials(user='gpadmin', \n",
+ "# db_name='madlib',\n",
+ "# host='35.239.240.26',\n",
+ "# port='5432',\n",
+ "# password='')\n",
+ "\n",
+ "# Initialize ImageLoader (increase num_workers to run faster)\n",
+ "iloader = ImageLoader(num_workers=5, db_creds=db_creds)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "First load CIFAR-10 data from Keras consisting of 50,000 32x32 color training images, labeled over 10 categories, and 10,000 test images."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "MainProcess: Connected to madlib db.\n",
+ "Executing: CREATE TABLE cifar_10_train_data (id SERIAL, x REAL[], y TEXT)\n",
+ "CREATE TABLE\n",
+ "Created table cifar_10_train_data in madlib db\n",
+ "Spawning 5 workers...\n",
+ "Initializing PoolWorker-1 [pid 66874]\n",
+ "PoolWorker-1: Created temporary directory /tmp/madlib_rrRCbykUw0\n",
+ "Initializing PoolWorker-2 [pid 66875]\n",
+ "PoolWorker-2: Created temporary directory /tmp/madlib_w1bgWZpxCn\n",
+ "Initializing PoolWorker-3 [pid 66876]\n",
+ "PoolWorker-3: Created temporary directory /tmp/madlib_q2Xgd1TDVg\n",
+ "PoolWorker-1: Connected to madlib db.\n",
+ "Initializing PoolWorker-4 [pid 66877]\n",
+ "PoolWorker-2: Connected to madlib db.\n",
+ "PoolWorker-4: Created temporary directory /tmp/madlib_NJDD5nxkcT\n",
+ "Initializing PoolWorker-5 [pid 66879]\n",
+ "PoolWorker-3: Connected to madlib db.\n",
+ "PoolWorker-5: Created temporary directory /tmp/madlib_rQ5Tgpwhkw\n",
+ "PoolWorker-4: Connected to madlib db.\n",
+ "PoolWorker-5: Connected to madlib db.\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_rrRCbykUw0/cifar_10_train_data0000.tmp\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_w1bgWZpxCn/cifar_10_train_data0000.tmp\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_q2Xgd1TDVg/cifar_10_train_data0000.tmp\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_NJDD5nxkcT/cifar_10_train_data0000.tmp\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_rQ5Tgpwhkw/cifar_10_train_data0000.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-2: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-3: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-4: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-5: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_rrRCbykUw0/cifar_10_train_data0001.tmp\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_w1bgWZpxCn/cifar_10_train_data0001.tmp\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_q2Xgd1TDVg/cifar_10_train_data0001.tmp\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_NJDD5nxkcT/cifar_10_train_data0001.tmp\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_rQ5Tgpwhkw/cifar_10_train_data0001.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-2: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-3: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-4: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-5: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_rrRCbykUw0/cifar_10_train_data0002.tmp\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_w1bgWZpxCn/cifar_10_train_data0002.tmp\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_q2Xgd1TDVg/cifar_10_train_data0002.tmp\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_NJDD5nxkcT/cifar_10_train_data0002.tmp\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_rQ5Tgpwhkw/cifar_10_train_data0002.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-2: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-3: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-4: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-5: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_rrRCbykUw0/cifar_10_train_data0003.tmp\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_w1bgWZpxCn/cifar_10_train_data0003.tmp\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_q2Xgd1TDVg/cifar_10_train_data0003.tmp\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_NJDD5nxkcT/cifar_10_train_data0003.tmp\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_rQ5Tgpwhkw/cifar_10_train_data0003.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-2: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-3: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-4: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-5: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_rrRCbykUw0/cifar_10_train_data0004.tmp\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_w1bgWZpxCn/cifar_10_train_data0004.tmp\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_q2Xgd1TDVg/cifar_10_train_data0004.tmp\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_NJDD5nxkcT/cifar_10_train_data0004.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_rQ5Tgpwhkw/cifar_10_train_data0004.tmp\n",
+ "PoolWorker-2: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-3: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-4: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-5: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_rrRCbykUw0/cifar_10_train_data0005.tmp\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_q2Xgd1TDVg/cifar_10_train_data0005.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_w1bgWZpxCn/cifar_10_train_data0005.tmp\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_NJDD5nxkcT/cifar_10_train_data0005.tmp\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_rQ5Tgpwhkw/cifar_10_train_data0005.tmp\n",
+ "PoolWorker-3: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-2: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_rrRCbykUw0/cifar_10_train_data0006.tmp\n",
+ "PoolWorker-4: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-5: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-1: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_q2Xgd1TDVg/cifar_10_train_data0006.tmp\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_w1bgWZpxCn/cifar_10_train_data0006.tmp\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_rQ5Tgpwhkw/cifar_10_train_data0006.tmp\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_NJDD5nxkcT/cifar_10_train_data0006.tmp\n",
+ "PoolWorker-3: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_rrRCbykUw0/cifar_10_train_data0007.tmp\n",
+ "PoolWorker-2: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-5: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-4: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-1: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_q2Xgd1TDVg/cifar_10_train_data0007.tmp\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_w1bgWZpxCn/cifar_10_train_data0007.tmp\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_rQ5Tgpwhkw/cifar_10_train_data0007.tmp\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_NJDD5nxkcT/cifar_10_train_data0007.tmp\n",
+ "PoolWorker-3: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_rrRCbykUw0/cifar_10_train_data0008.tmp\n",
+ "PoolWorker-2: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-5: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-4: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-1: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_q2Xgd1TDVg/cifar_10_train_data0008.tmp\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_w1bgWZpxCn/cifar_10_train_data0008.tmp\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_rQ5Tgpwhkw/cifar_10_train_data0008.tmp\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_NJDD5nxkcT/cifar_10_train_data0008.tmp\n",
+ "PoolWorker-3: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_rrRCbykUw0/cifar_10_train_data0009.tmp\n",
+ "PoolWorker-2: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-5: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-4: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-1: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_q2Xgd1TDVg/cifar_10_train_data0009.tmp\n",
+ "PoolWorker-3: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_rrRCbykUw0/cifar_10_train_data0010.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_q2Xgd1TDVg/cifar_10_train_data0010.tmp\n",
+ "PoolWorker-3: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_rrRCbykUw0/cifar_10_train_data0011.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-4: Removed temporary directory /tmp/madlib_NJDD5nxkcT\n",
+ "PoolWorker-3: Removed temporary directory /tmp/madlib_q2Xgd1TDVg\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "PoolWorker-2: Removed temporary directory /tmp/madlib_w1bgWZpxCn\n",
+ "PoolWorker-5: Removed temporary directory /tmp/madlib_rQ5Tgpwhkw\n",
+ "PoolWorker-1: Removed temporary directory /tmp/madlib_rrRCbykUw0\n",
+ "Done! Loaded 50000 images in 24.2264728546s\n",
+ "5 workers terminated.\n",
+ "MainProcess: Connected to madlib db.\n",
+ "Executing: CREATE TABLE cifar_10_test_data (id SERIAL, x REAL[], y TEXT)\n",
+ "CREATE TABLE\n",
+ "Created table cifar_10_test_data in madlib db\n",
+ "Spawning 5 workers...\n",
+ "Initializing PoolWorker-6 [pid 66886]\n",
+ "PoolWorker-6: Created temporary directory /tmp/madlib_us7NnIzs4Z\n",
+ "Initializing PoolWorker-7 [pid 66887]\n",
+ "PoolWorker-7: Created temporary directory /tmp/madlib_YhsJADC7Iq\n",
+ "Initializing PoolWorker-8 [pid 66888]\n",
+ "PoolWorker-8: Created temporary directory /tmp/madlib_izbrkdZU7f\n",
+ "PoolWorker-6: Connected to madlib db.\n",
+ "Initializing PoolWorker-9 [pid 66889]\n",
+ "PoolWorker-7: Connected to madlib db.\n",
+ "PoolWorker-9: Created temporary directory /tmp/madlib_GgLgXeKpOp\n",
+ "Initializing PoolWorker-10 [pid 66891]\n",
+ "PoolWorker-10: Created temporary directory /tmp/madlib_JfRd66eGln\n",
+ "PoolWorker-8: Connected to madlib db.\n",
+ "PoolWorker-9: Connected to madlib db.\n",
+ "PoolWorker-10: Connected to madlib db.\n",
+ "PoolWorker-6: Wrote 1000 images to /tmp/madlib_us7NnIzs4Z/cifar_10_test_data0000.tmp\n",
+ "PoolWorker-7: Wrote 1000 images to /tmp/madlib_YhsJADC7Iq/cifar_10_test_data0000.tmp\n",
+ "PoolWorker-8: Wrote 1000 images to /tmp/madlib_izbrkdZU7f/cifar_10_test_data0000.tmp\n",
+ "PoolWorker-9: Wrote 1000 images to /tmp/madlib_GgLgXeKpOp/cifar_10_test_data0000.tmp\n",
+ "PoolWorker-10: Wrote 1000 images to /tmp/madlib_JfRd66eGln/cifar_10_test_data0000.tmp\n",
+ "PoolWorker-6: Loaded 1000 images into cifar_10_test_data\n",
+ "PoolWorker-7: Loaded 1000 images into cifar_10_test_data\n",
+ "PoolWorker-8: Loaded 1000 images into cifar_10_test_data\n",
+ "PoolWorker-10: Loaded 1000 images into cifar_10_test_data\n",
+ "PoolWorker-9: Loaded 1000 images into cifar_10_test_data\n",
+ "PoolWorker-7: Wrote 1000 images to /tmp/madlib_YhsJADC7Iq/cifar_10_test_data0001.tmp\n",
+ "PoolWorker-6: Wrote 1000 images to /tmp/madlib_us7NnIzs4Z/cifar_10_test_data0001.tmp\n",
+ "PoolWorker-8: Wrote 1000 images to /tmp/madlib_izbrkdZU7f/cifar_10_test_data0001.tmp\n",
+ "PoolWorker-10: Wrote 1000 images to /tmp/madlib_JfRd66eGln/cifar_10_test_data0001.tmp\n",
+ "PoolWorker-9: Wrote 1000 images to /tmp/madlib_GgLgXeKpOp/cifar_10_test_data0001.tmp\n",
+ "PoolWorker-7: Loaded 1000 images into cifar_10_test_data\n",
+ "PoolWorker-6: Loaded 1000 images into cifar_10_test_data\n",
+ "PoolWorker-8: Loaded 1000 images into cifar_10_test_data\n",
+ "PoolWorker-10: Loaded 1000 images into cifar_10_test_data\n",
+ "PoolWorker-9: Loaded 1000 images into cifar_10_test_data\n",
+ "PoolWorker-8: Removed temporary directory /tmp/madlib_izbrkdZU7f\n",
+ "PoolWorker-6: Removed temporary directory /tmp/madlib_us7NnIzs4Z\n",
+ "PoolWorker-7: Removed temporary directory /tmp/madlib_YhsJADC7Iq\n",
+ "PoolWorker-10: Removed temporary directory /tmp/madlib_JfRd66eGln\n",
+ "PoolWorker-9: Removed temporary directory /tmp/madlib_GgLgXeKpOp\n",
+ "Done! Loaded 10000 images in 5.36254000664s\n",
+ "5 workers terminated.\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Load dataset into np array\n",
+ "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
+ "\n",
+ "%sql DROP TABLE IF EXISTS cifar_10_train_data, cifar_10_test_data;\n",
+ "\n",
+ "# Save images to temporary directories and load into database\n",
+ "iloader.load_dataset_from_np(x_train, y_train, 'cifar_10_train_data', append=False)\n",
+ "iloader.load_dataset_from_np(x_test, y_test, 'cifar_10_test_data', append=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>50000</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(50000L,)]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%sql select count(*) from cifar_10_train_data;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"image_preproc\"></a>\n",
+ "# 3. Call image preprocessor\n",
+ "\n",
+ "Transforms from one image per row to multiple images per row for batch optimization. Also normalizes and one-hot encodes.\n",
+ "\n",
+ "Training data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
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+ " <th>independent_varname</th>\n",
+ " <th>dependent_vartype</th>\n",
+ " <th>class_values</th>\n",
+ " <th>buffer_size</th>\n",
+ " <th>normalizing_const</th>\n",
+ " <th>num_classes</th>\n",
+ " <th>distribution_rules</th>\n",
+ " <th>__internal_gpu_config__</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>cifar_10_train_data</td>\n",
+ " <td>cifar_10_train_data_packed</td>\n",
+ " <td>y</td>\n",
+ " <td>x</td>\n",
+ " <td>text</td>\n",
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+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS cifar_10_train_data_packed, cifar_10_train_data_packed_summary;\n",
+ "\n",
+ "SELECT madlib.training_preprocessor_dl('cifar_10_train_data', -- Source table\n",
+ " 'cifar_10_train_data_packed', -- Output table\n",
+ " 'y', -- Dependent variable\n",
+ " 'x', -- Independent variable\n",
+ " 1000, -- Buffer size\n",
+ " 255 -- Normalizing constant\n",
+ " );\n",
+ "\n",
+ "SELECT * FROM cifar_10_train_data_packed_summary;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Test data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>source_table</th>\n",
+ " <th>output_table</th>\n",
+ " <th>dependent_varname</th>\n",
+ " <th>independent_varname</th>\n",
+ " <th>dependent_vartype</th>\n",
+ " <th>class_values</th>\n",
+ " <th>buffer_size</th>\n",
+ " <th>normalizing_const</th>\n",
+ " <th>num_classes</th>\n",
+ " <th>distribution_rules</th>\n",
+ " <th>__internal_gpu_config__</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>cifar_10_test_data</td>\n",
+ " <td>cifar_10_test_data_packed</td>\n",
+ " <td>y</td>\n",
+ " <td>x</td>\n",
+ " <td>text</td>\n",
+ " <td>[u'0', u'1', u'2', u'3', u'4', u'5', u'6', u'7', u'8', u'9']</td>\n",
+ " <td>1000</td>\n",
+ " <td>255.0</td>\n",
+ " <td>10</td>\n",
+ " <td>all_segments</td>\n",
+ " <td>all_segments</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'cifar_10_test_data', u'cifar_10_test_data_packed', u'y', u'x', u'text', [u'0', u'1', u'2', u'3', u'4', u'5', u'6', u'7', u'8', u'9'], 1000, 255.0, 10, 'all_segments', 'all_segments')]"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS cifar_10_test_data_packed, cifar_10_test_data_packed_summary;\n",
+ "\n",
+ "SELECT madlib.validation_preprocessor_dl('cifar_10_test_data', -- Source table\n",
+ " 'cifar_10_test_data_packed', -- Output table\n",
+ " 'y', -- Dependent variable\n",
+ " 'x', -- Independent variable\n",
+ " 'cifar_10_train_data_packed', -- Training preproc table\n",
+ " 1000 -- Buffer size\n",
+ " );\n",
+ "\n",
+ "SELECT * FROM cifar_10_test_data_packed_summary;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"define_and_load_model\"></a>\n",
+ "# 4. Define and load model architecture\n",
+ "\n",
+ "Model architecture from https://keras.io/examples/cifar10_cnn/"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "_________________________________________________________________\n",
+ "Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ "conv2d_5 (Conv2D) (None, 32, 32, 32) 896 \n",
+ "_________________________________________________________________\n",
+ "activation_7 (Activation) (None, 32, 32, 32) 0 \n",
+ "_________________________________________________________________\n",
+ "conv2d_6 (Conv2D) (None, 30, 30, 32) 9248 \n",
+ "_________________________________________________________________\n",
+ "activation_8 (Activation) (None, 30, 30, 32) 0 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_3 (MaxPooling2 (None, 15, 15, 32) 0 \n",
+ "_________________________________________________________________\n",
+ "dropout_4 (Dropout) (None, 15, 15, 32) 0 \n",
+ "_________________________________________________________________\n",
+ "conv2d_7 (Conv2D) (None, 15, 15, 64) 18496 \n",
+ "_________________________________________________________________\n",
+ "activation_9 (Activation) (None, 15, 15, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "conv2d_8 (Conv2D) (None, 13, 13, 64) 36928 \n",
+ "_________________________________________________________________\n",
+ "activation_10 (Activation) (None, 13, 13, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_4 (MaxPooling2 (None, 6, 6, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "dropout_5 (Dropout) (None, 6, 6, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "flatten_2 (Flatten) (None, 2304) 0 \n",
+ "_________________________________________________________________\n",
+ "dense_3 (Dense) (None, 512) 1180160 \n",
+ "_________________________________________________________________\n",
+ "activation_11 (Activation) (None, 512) 0 \n",
+ "_________________________________________________________________\n",
+ "dropout_6 (Dropout) (None, 512) 0 \n",
+ "_________________________________________________________________\n",
+ "dense_4 (Dense) (None, 10) 5130 \n",
+ "_________________________________________________________________\n",
+ "activation_12 (Activation) (None, 10) 0 \n",
+ "=================================================================\n",
+ "Total params: 1,250,858\n",
+ "Trainable params: 1,250,858\n",
+ "Non-trainable params: 0\n",
+ "_________________________________________________________________\n"
+ ]
+ }
+ ],
+ "source": [
+ "model = Sequential()\n",
+ "model.add(Conv2D(32, (3, 3), padding='same',\n",
+ " input_shape=x_train.shape[1:]))\n",
+ "model.add(Activation('relu'))\n",
+ "model.add(Conv2D(32, (3, 3)))\n",
+ "model.add(Activation('relu'))\n",
+ "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+ "model.add(Dropout(0.25))\n",
+ "\n",
+ "model.add(Conv2D(64, (3, 3), padding='same'))\n",
+ "model.add(Activation('relu'))\n",
+ "model.add(Conv2D(64, (3, 3)))\n",
+ "model.add(Activation('relu'))\n",
+ "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+ "model.add(Dropout(0.25))\n",
+ "\n",
+ "model.add(Flatten())\n",
+ "model.add(Dense(512))\n",
+ "model.add(Activation('relu'))\n",
+ "model.add(Dropout(0.5))\n",
+ "model.add(Dense(num_classes))\n",
+ "model.add(Activation('softmax'))\n",
+ "\n",
+ "model.summary()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Load into model architecture table using psycopg2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>model_id</th>\n",
+ " <th>name</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>CNN from Keras docs for CIFAR-10</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(1, u'CNN from Keras docs for CIFAR-10')]"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import psycopg2 as p2\n",
+ "#conn = p2.connect('postgresql://gpadmin@35.239.240.26:5432/madlib')\n",
+ "#conn = p2.connect('postgresql://fmcquillan@localhost:5432/madlib')\n",
+ "conn = p2.connect('postgresql://gpadmin@localhost:8000/madlib')\n",
+ "cur = conn.cursor()\n",
+ "\n",
+ "%sql DROP TABLE IF EXISTS model_arch_library;\n",
+ "query = \"SELECT madlib.load_keras_model('model_arch_library', %s, NULL, %s)\"\n",
+ "cur.execute(query,[model.to_json(), \"CNN from Keras docs for CIFAR-10\"])\n",
+ "conn.commit()\n",
+ "\n",
+ "# check model loaded OK\n",
+ "%sql SELECT model_id, name FROM model_arch_library;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"train\"></a>\n",
+ "# 5. Train"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>madlib_keras_fit</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td></td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[('',)]"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS cifar_10_model, cifar_10_model_summary;\n",
+ "\n",
+ "SELECT madlib.madlib_keras_fit('cifar_10_train_data_packed', -- source table\n",
+ " 'cifar_10_model', -- model output table\n",
+ " 'model_arch_library', -- model arch table\n",
+ " 1, -- model arch id\n",
+ " $$ loss='categorical_crossentropy', optimizer='rmsprop(lr=0.0001, decay=1e-6)', metrics=['accuracy']$$, -- compile_params\n",
+ " $$ batch_size=32, epochs=3 $$, -- fit_params\n",
+ " 3, -- num_iterations\n",
+ " FALSE, -- use GPUs\n",
+ " 'cifar_10_test_data_packed', -- validation dataset\n",
+ " 2 -- metrics compute frequency\n",
+ " );"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "View the model summary:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>source_table</th>\n",
+ " <th>model</th>\n",
+ " <th>dependent_varname</th>\n",
+ " <th>independent_varname</th>\n",
+ " <th>model_arch_table</th>\n",
+ " <th>model_id</th>\n",
+ " <th>compile_params</th>\n",
+ " <th>fit_params</th>\n",
+ " <th>num_iterations</th>\n",
+ " <th>validation_table</th>\n",
+ " <th>metrics_compute_frequency</th>\n",
+ " <th>name</th>\n",
+ " <th>description</th>\n",
+ " <th>model_type</th>\n",
+ " <th>model_size</th>\n",
+ " <th>start_training_time</th>\n",
+ " <th>end_training_time</th>\n",
+ " <th>metrics_elapsed_time</th>\n",
+ " <th>madlib_version</th>\n",
+ " <th>num_classes</th>\n",
+ " <th>class_values</th>\n",
+ " <th>dependent_vartype</th>\n",
+ " <th>normalizing_const</th>\n",
+ " <th>metrics_type</th>\n",
+ " <th>training_metrics_final</th>\n",
+ " <th>training_loss_final</th>\n",
+ " <th>training_metrics</th>\n",
+ " <th>training_loss</th>\n",
+ " <th>validation_metrics_final</th>\n",
+ " <th>validation_loss_final</th>\n",
+ " <th>validation_metrics</th>\n",
+ " <th>validation_loss</th>\n",
+ " <th>metrics_iters</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>cifar_10_train_data_packed</td>\n",
+ " <td>cifar_10_model</td>\n",
+ " <td>y</td>\n",
+ " <td>x</td>\n",
+ " <td>model_arch_library</td>\n",
+ " <td>1</td>\n",
+ " <td> loss='categorical_crossentropy', optimizer='rmsprop(lr=0.0001, decay=1e-6)', metrics=['accuracy']</td>\n",
+ " <td> batch_size=32, epochs=3 </td>\n",
+ " <td>3</td>\n",
+ " <td>cifar_10_test_data_packed</td>\n",
+ " <td>2</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>4886.20019531</td>\n",
+ " <td>2019-12-18 21:46:34.610075</td>\n",
+ " <td>2019-12-18 22:01:06.754356</td>\n",
+ " <td>[568.416245222092, 872.144206047058]</td>\n",
+ " <td>1.17-dev</td>\n",
+ " <td>10</td>\n",
+ " <td>[u'0', u'1', u'2', u'3', u'4', u'5', u'6', u'7', u'8', u'9']</td>\n",
+ " <td>text</td>\n",
+ " <td>255.0</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.612959980965</td>\n",
+ " <td>1.09410715103</td>\n",
+ " <td>[0.563260018825531, 0.612959980964661]</td>\n",
+ " <td>[1.22403216362, 1.09410715103149]</td>\n",
+ " <td>0.597299993038</td>\n",
+ " <td>1.12304246426</td>\n",
+ " <td>[0.55460000038147, 0.597299993038177]</td>\n",
+ " <td>[1.23818647861481, 1.12304246425629]</td>\n",
+ " <td>[2, 3]</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'cifar_10_train_data_packed', u'cifar_10_model', u'y', u'x', u'model_arch_library', 1, u\" loss='categorical_crossentropy', optimizer='rmsprop(lr=0.0001, decay=1e-6)', metrics=['accuracy']\", u' batch_size=32, epochs=3 ', 3, u'cifar_10_test_data_packed', 2, None, None, u'madlib_keras', 4886.20019531, datetime.datetime(2019, 12, 18, 21, 46, 34, 610075), datetime.datetime(2019, 12, 18, 22, 1, 6, 754356), [568.416245222092, 872.144206047058], u'1.17-dev', 10, [u'0', u'1', u'2', u'3', u'4', u'5', u'6', u'7', u'8', u'9'], u'text', 255.0, [u'accuracy'], 0.612959980965, 1.09410715103, [0.563260018825531, 0.612959980964661], [1.22403216362, 1.09410715103149], 0.597299993038, 1.12304246426, [0.55460000038147, 0.597299993038177], [1.23818647861481, 1.12304246425629], [2, 3])]"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT * FROM cifar_10_model_summary;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Evaluate using test data (same values as last iteration from the fit output summary above)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>loss</th>\n",
+ " <th>metric</th>\n",
+ " <th>metrics_type</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1.12304246426</td>\n",
+ " <td>0.597299993038</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(1.12304246425629, 0.597299993038177, [u'accuracy'])]"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS cifar10_validate;\n",
+ "\n",
+ "SELECT madlib.madlib_keras_evaluate('cifar_10_model', -- model\n",
+ " 'cifar_10_test_data_packed', -- test table\n",
+ " 'cifar10_validate' -- output table\n",
+ " );\n",
+ "\n",
+ "SELECT * FROM cifar10_validate;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"plot\"></a>\n",
+ "# 6. Plots by iteration and by time\n",
+ "Accuracy by iteration"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 100,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.legend.Legend at 0x1195ef9d0>"
+ ]
+ },
+ "execution_count": 100,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# get accuracy and iteration number\n",
+ "iters_proxy = %sql SELECT metrics_iters FROM cifar_10_model_summary;\n",
+ "train_accuracy_proxy = %sql SELECT training_metrics FROM cifar_10_model_summary;\n",
+ "test_accuracy_proxy = %sql SELECT validation_metrics FROM cifar_10_model_summary;\n",
+ "\n",
+ "# get number of points\n",
+ "num_points_proxy = %sql SELECT array_length(metrics_iters,1) FROM cifar_10_model_summary;\n",
+ "num_points = num_points_proxy[0]\n",
+ "\n",
+ "# reshape to np arrays\n",
+ "iters = np.array(iters_proxy).reshape(num_points)\n",
+ "train_accuracy = np.array(train_accuracy_proxy).reshape(num_points)\n",
+ "test_accuracy = np.array(test_accuracy_proxy).reshape(num_points)\n",
+ "\n",
+ "#plot\n",
+ "plt.title('CIFAR-10 accuracy by iteration')\n",
+ "plt.xlabel('Iteration number')\n",
+ "plt.ylabel('Accuracy')\n",
+ "plt.grid(True)\n",
+ "plt.plot(iters, train_accuracy, 'g.-', label='Train')\n",
+ "plt.plot(iters, test_accuracy, 'r.-', label='Test')\n",
+ "plt.legend()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Loss by iteration"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 101,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.legend.Legend at 0x119279910>"
+ ]
+ },
+ "execution_count": 101,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# get loss\n",
+ "train_loss_proxy = %sql SELECT training_loss FROM cifar_10_model_summary;\n",
+ "test_loss_proxy = %sql SELECT validation_loss FROM cifar_10_model_summary;\n",
+ "\n",
+ "# reshape to np arrays\n",
+ "train_loss = np.array(train_loss_proxy).reshape(num_points)\n",
+ "test_loss = np.array(test_loss_proxy).reshape(num_points)\n",
+ "\n",
+ "#plot\n",
+ "plt.title('CIFAR-10 loss by iteration')\n",
+ "plt.xlabel('Iteration number')\n",
+ "plt.ylabel('Loss')\n",
+ "plt.grid(True)\n",
+ "plt.plot(iters, train_loss, 'g.-', label='Train')\n",
+ "plt.plot(iters, test_loss, 'r.-', label='Test')\n",
+ "plt.legend()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Accuracy by time"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 108,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.legend.Legend at 0x119664410>"
+ ]
+ },
+ "execution_count": 108,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# get time\n",
+ "time_proxy = %sql SELECT metrics_elapsed_time FROM cifar_10_model_summary;\n",
+ "\n",
+ "# reshape to np arrays\n",
+ "time = np.array(time_proxy).reshape(num_points)/60.0\n",
+ "\n",
+ "#plot\n",
+ "plt.title('CIFAR-10 accuracy by time')\n",
+ "plt.xlabel('Time (min)')\n",
+ "plt.ylabel('Accuracy')\n",
+ "plt.grid(True)\n",
+ "plt.plot(time, train_accuracy, 'g.-', label='Train')\n",
+ "plt.plot(time, test_accuracy, 'r.-', label='Test')\n",
+ "plt.legend()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Time to achieve a given accuracy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 109,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.legend.Legend at 0x119628690>"
+ ]
+ },
+ "execution_count": 109,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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07iUxYsQI2rdvT/369X0dojHBLzkZGjWCjh29jsQzufm5jFk8hm5Nu1Gzck2vwymS1UAgYMtkJiUlcfHFF9O2bVvuv/9+8vPzyc3NpW/fvrRu3ZpWrVrx5ptvkpyczIIFC+jZs+dp11yMCXl798I338Ctt0JE+H5Ffbv6W3Yc2BG0zVdQ3msgjzwCC049nTsZGbBwIeTnO/9YL7zQmTbhZNq2hddPfzr3xYsX8/nnnzNz5kwqVKjAwIEDGT9+PK1atWLXrl0sWrQIcEaex8bG8tZbb/H222/Ttm3b076WMSHtiy+cCRSt+YozKp/BtecH7wzE4Zvej8rIcJIHOI8ZGX65zNSpU5kzZw4dOnSgbdu2TJ8+nbVr13Leeefx66+/8tBDDzFlyhRqnCp5GRMOkpOhSRO46CKvI/FM5uFMvlj+BT1b9gyqqUuOV75rICWpKaSmOqNcc3KcZTJHj/bLSmeqyoABA3jmmWcKth1dE33hwoV8/fXXvPPOO0yYMIEPP/zQ59c3JiR8/TV8+y306QNBNmgukF6c8SLZudm0rR/cLRBWA0lIcJbH9PMymVdeeSXjxo1jl7uu8+7du9m4cSM7d+5EVbnlllsYPnw48+bNAyAmJob9+/f7JRZjglJqKnTvDqowbpzf+ySDVerGVF74+QXAWVXVn0ttl1X5roGUVEKC39dXbt26NcOGDePKK68kPz+fihUr8sorr5Cdnc1dd92FqiIivPjiiwDceeed/OlPf6Jy5crMnj37mIWljCmXUlKcvg9w1kBPSQnLdc8n/jqxYJXUnLwcUtaleLru+alYAvGjwtO5A/Tu3ZvevXsXvD7ahDV//vwT3nvrrbdy663Bee+3MX6RmOjcyJKf7zQnJyZ6HZEnDuUeAiBSIomKjCIxLtHbgE7BEogxJjgkJEDdus70Je+9F5a1D4DUTak0PaMpd7S9I6CrpJaGJRBjTHDIzYVdu2DAgLBNHqv3rGb25tm8eOWLPH7Z416HU6xy2Ymuql6HEFDhVl5TTm3Z4iSRs8/2OhLPJC9JBqBXq9BYedRvCURERojIDhFZXGjbSyKyXEQWisjnIhJbaN8TIrJKRH4VkVIvfhwdHc3u3bvD5ktVVdm9ezfR0dFeh2JM2axf7zzGxXkahpfGLB7DZY0v46waZ3kdSon4swnrE+BtYGShbd8BT6hqroi8CDwBDBaRFkAvoCVwJjBVRJqqat7pXrRRo0Zs2rSJnTt3lrkA/padne2TL/7o6GgaNWrkg4iM8dC6dc5jmNZAFu9YzOIdi3nrmre8DqXE/JZAVPVHEYk7btu3hV7+AvRwn3cHxqrqYWCtiKwCLgZO+wboihUr0qRJk1LFHGgpKSm0a9fO6zCMCQ5HayBnhcZf3742dvFYIiSCW1rc4nUoJeZlH8gA4Gv3eUNgY6F9m9xtxphwsX69cwdW5cpeRxJwqsqYxWPo0qQL9arV8zqcEvPkLiwReRLIBUaX4r0DgYEA9erVIyUlxbfBBVBWVlZIx19S4VBOK2PZXTh/PhVq1mSex79HLz7L5ZnLWbN3DTfXuTmk/h0FPIGIyB1AN6CL/tbTvRloXOiwRu62E6jqh8CHAB06dNDEEB5slJKSQijHX1LhUE4row9kZkK7dp7/Hr34LCdOmUhUZBRDbxxKbHRs8W8IEgFtwhKRq4HHgetV9WChXROBXiJSSUSaAOcDswMZmzHGQ/n5sGFDWHag52s+yUuSuea8a0IqeYAfayAiMgZIBGqLyCZgGM5dV5WA78SZafMXVb1XVZeIyDhgKU7T1gOluQPLGBOitm+Hw4fDMoH8tP4ntuzfEjJjPwrz511YtxWx+eNTHP8s8Ky/4jHGBLEwHgMyZvEYqlSswnVNr/M6lNNWLkeiG2NCTJiOATmSd4TxS8fTvVl3qkZV9Tqc02YJxBjjvaM1kDBLIFPXTGX3od0h2XwFlkCMMcFg/Xo44wyIifE6koAau2QssdGxXHVuqWdv8pQlEGOM99atC7vax6Ejh/h82efc1PwmKlWo5HU4pWIJxBjjvfXrw64DffLKyezP2c9trYu63yg0WAIxxnhLNSxrIGOXjKVe1Xp0juvsdSilZgnEGOOt3bvh4MGwqoFkHs5k0opJ3NLiFiIjIr0Op9QsgRhjvPXVV87joUPexhFAL898mezcbFrXa+11KGViCcQY453UVLjnHuf50087r8u5mRtm8uxPzpjpR755hNSNoVtmSyDGGO+MGwdHjjjPc3MhhGaiLa03Z79JvuYDkJOXQ8q6FG8DKgNPpnM3xhgOH4ZJk5znkZEQFQXlfEbjrJwspq2dhiBESARRkVEkxiV6HVapWQIxxnjjySdh1Sp46SWnFpKYCAkJXkflV8/++Cy7Du7ig24fsPvgbhLjEkloHLpltgRijAm8qVPhlVfg/vvhsce8jiYgVu5eySupr9CvTT8Gxg/0OhyfsD4QY0xg7d4N/frBBRc4tY8w8ciUR4iuEM2LV77odSg+YzUQY0zgqMLdd8OuXTB5MlSp4nVEATFpxSQmr5zMy11fpn61+l6H4zOWQIwxgfPxx/D5507No21br6MJiOzcbB755hGa127Og5c86HU4PmUJxBgTGCtWwMMPwxVXwKBBXkcTMK+mvsrqvav5ts+3REVGeR2OT1kfiDHG/44cgdtvh0qVYORIiAiPr56NGRt59qdnubH5jXQ9t6vX4fic1UCMMf739NMwdy6MHw8NG3odTcD89bu/kq/5vHrVq16H4hfh8WeAMcY7P/4Izz8Pd90FN9/sdTQBk7IuheQlyQy+bDBxsXFeh+MXlkCMMf6zbx/06QPnnguvv+51NAGTm5/LQ18/xNk1zmbwZYO9DsdvrAnLGOMfqnDvvbB1K8ycCdWqeR1RwLw35z0W7VjEhFsnULliZa/D8RtLIMYY//jPfyA5GZ59Fi66yOtoAmbngZ38PeXvXHnOldzY/Eavw/ErvzVhicgIEdkhIosLbTtDRL4TkZXuY013u4jImyKySkQWikh7f8VljAmANWvggQfgd7+DweW3CacoQ6cNJSsnizevfhMR8Tocv/JnH8gnwNXHbRsCTFPV84Fp7muAa4Dz3Z+BwHt+jMsY40+5uU6/R0QEjBrlzLQbJuZumcvH8z/moYsf4oI6F3gdjt/5LYGo6o/AnuM2dweS3OdJwA2Fto9Uxy9ArIg08Fdsxhg/evZZZ2Go998Pq3XO8zWfP0/+M3Wr1mVY4jCvwwmIQPeB1FPVre7zbUA993lDYGOh4za527ZyHBEZiFNLoV69eqSE8AI0WVlZIR1/SYVDOa2MjuqLF9Nu+HC2d+3K8vr1Q3KBqNJ+lt9s+4ZZm2cxpNkQ5qXO831gwUhV/fYDxAGLC73ed9z+ve7jJKBToe3TgA7FnT8+Pl5D2Q8//OB1CAERDuW0MqpqRoZqkybOT0ZGQGLyh9J8lvsO7dO6L9XVhP9L0Lz8PN8H5WPAXPXBd3ygayDbRaSBqm51m6h2uNs3A40LHdfI3WaMCRUPPgjr18NPP0H16l5HE1D/mP4Pdh7Yyde3f02EhM/wukCXdCLQ333eH/iy0PZ+7t1YlwIZ+ltTlzEmmKWmQq9ezhxXTz0FHTt6HVFAjV44mtd/eZ3rm15P+wbhdQOp32ogIjIGSARqi8gmYBjwAjBORO4C1gO3uodPBq4FVgEHgTv9FZcxxodSU53ZdbOznbuuunTxOqKAmrFhBv2+6IeiTFkzhdSNqSG9RO3p8lsCUdXbTrLrhH9hbpvcA/6KxRjjJykpcPiw81wEZsxwxn6EiSFTh5Cv+QAcyTtCyrqUsEog4dNYZ4zxvcTE36Zmj4pyXoeJd+e8y88bf6ZCRAUiJZKoyCgS4xK9DiugbCoTY0zpXXIJVK0K550Hb78NCeHx1/fklZN58OsHua7pdTx+2eP8tP4nEuMSw6r2AZZAjDFlsWQJZGbCQw+FTfJI35ZOz/E9aVOvDZ/e/CnVoqrR6axOXoflCWvCMsaU3tEBd5df7mkYgbI5czN//PSPxEbHMqn3JKpFhc8Mw0WxGogxpvRSUiAuzvkp57JysrhuzHVkHM5gxp0zODPmTK9D8pzVQIwxpZOfD9Onh0XHeV5+HrdNuI307emM6zGONvXbeB1SUChRDUREOgC/A84EDgGLge9Uda8fYzPGBLMlS2D37rBIIIOmDGLSikm8e+27XHP+NV6HEzROWQMRkTtFZB7wBFAZ+BVn+pFOwFQRSRKRs/wfpjEm6IRJ/8dbs97izdlv8pdL/8J9F93ndThBpbgaSBXgMlU9VNROEWmLs4bHBl8HZowJcmHQ/zFpxSQemfII3Zt156WuL3kdTtA5ZQJR1XeK2b/At+EYY0LC0f6P667zOhK/mb91Pr3G96Jd/XaMvmk0kRHhszBWSZW0D6QOcDfO9OwF71HVAf4JyxgT1Mp5/8emzE10G9ONMyqfwVe3fUXVqKpehxSUSnob75fAT8BUIM9/4RhjQkI57v84mHuQbp92Y//h/fw84GcaxNjiqCdT0gRSRVUH+zUSY0zoKKf9H7n5uQxfNpzFexfzv97/o3W91l6HFNRKOg5kkohc69dIjDGhoZyO/1BVHv76YWbtmcU7177DVedd5XVIQa+kCeRhnCRySEQyRWS/iGT6MzBjTJAqp/0fb8x6g3fnvkvPRj25p8M9XocTEkrUhKWqMf4OxBgTIsph/8eXy79k0JRB3HTBTQysM9DrcELGKROIiDRX1eUiUuQ6jao6zz9hGWOCVjnr/0jbkkbvz3rT4cwOjLpxFLN/nu11SCGjuBrIIGAg8EoR+xS4wucRGWOCVzkb/7EhYwPdxnSjdpXaTLxtIlUqVvE6pJBS3EDCge5j58CEY4wJauWo/yPzcCbdPu3GwSMH+a7vd9SvVt/rkEJOSQcSRgJ/5MSBhK/6JyxjTFD697+dx5jQ7hb9acNP3PXlXazes5pv+nxDq7qtvA4pJJV0HMhXQDawCMj3XzjGmGBVfckSeOMN50WfPjBtWkiuQpi6MZUrPrmCXM2lYkTFsF8UqixKmkAaqeqFfo3EGBPU6vzwg9MHApCT43Smh2ACGbVwFLmaC0C+5pOyLiXs1jL3lZKOA/laRP7gq4uKyF9EZImILBaRMSISLSJNRGSWiKwSkWQRifLV9YwxZaRKjUWLnOeRkRAVFZL9IKrKjA0zAIiUSKIio0iMS/Q2qBBW0hrIL8DnIhIBHAEEUFWtfroXFJGGwENAC1U9JCLjgF7AtcBrqjpWRN4H7gLeO93zG2P84LPPqL5iBQwaBLVrO8kjBGsf45eOZ9GORQy5bAjVK1UnMS7Rah9lUNIE8iqQACxSVfXRdSuLyBGcNUe24twS3NvdnwQ8jSUQY7x38CAMGkTWOedQ7cUXoUJJvzaCy+HcwwyeOpjWdVvzzyv+adOz+0BJ/yVsBBb7Inmo6mYReRlnEapDwLdAGrBP1W2YhE1Aw6LeLyIDccamUK9ePVKOjooNQVlZWSEdf0mFQznLcxnjPvmEuA0bWPjcc+TMmOF1OKWWvDGZtfvW8lLrl/jpx59Oelx5/ix9raQJZA2QIiJfA4ePbizNbbwiUhPoDjQB9gH/Ba4u6ftV9UPgQ4AOHTpoYgi2wx6VkpJCKMdfUuFQznJbxnXrIDkZevYkJyEhZMu488BOPv3lU649/1oeu+mxUx5bbj9LPyhpJ/paYBoQBcQU+imNK4G1qrpTVY8AnwGXAbEicjShNQI2l/L8xhhfeewxEIGXQns5139M/wcHcg7YsrQ+VtLJFP/hw2tuAC4VkSo4TVhdgLnAD0APYCzQH2cRK2OMV6ZNgwkT4JlnoHFjWL3a64hKZdnOZbw/933uib+HFnVaeB1OuXLKGoiIfCQiRa6oIiJVRWSAiNx+OhdU1VnAeGAezsDECJwmqcHAIBFZBdQCPj6d8xpjfOjIEXjoIWjSxKmFhLDHpz5O1aiqPJ34tNehlDvF1UDeAZ5yk8hiYCcQDZwPVAdGAKNP96KqOgwYdtzmNcDFp3suY4wfvPsuLF0KX3wB0dFeR1NqU9dMZdKKSbx45YvUqVrH63DKneImU1wA3Coi1YAOQAOcZqdlqvprAOIzxgTajh2ErqlqAAAgAElEQVQwbBhcdRVcf73X0ZRaXn4ej377KHGxcTx0yUNeh1MulbQPJAtI8W8oxpigMHQoHDgAr7/udKCHqKT0JBZuX0hyj2SiK4RuLSqYlfQuLGNMOJgzB0aMgIcfhubNvY6m1LJysnjy+ydJaJTALS1u8Tqccis0h5QaY3wvPx8efBDq1oW//93raMrkXz//i21Z2/i85+dICNeigt1pJRARqaKqB/0VjDHGQ6NGwaxZ8MknUP20p7kLGpsyN/HyzJfp1aoXlza61OtwyrUSNWGJSEcRWQosd1+3EZF3/RqZMSZwMjNh8GC49FLo29fraMrkye+fJF/zeb7L816HUu6VtA/kNeAqYDeAqqYDv/dXUMaYABs+3Ln76s03ISJ0u0bTtqQxMn0kj1z6CHGxcV6HU+6V+F+Kqm48blOej2Mxxnhh+XJnpcEBA+Cii7yOptRUlUe/fZQ6VerwRKcnvA4nLJR4Nl4R6QioiFQEHgaW+S8sY0xAqDp3XFWtCs8953U0ZfLlr18yff103vvje9SIruF1OGGhpAnkXuANnCnWN+NMwf6Av4IyxgTIV1/Bt986Yz7q1vU6mlLLycvhr9/9lRZ1WvCn9n/yOpywUdKBhLuA05rzyhgT5FJS4I47nPmu7r/f62hKLXVjKs/+9Cyr9qxicu/JVIiw0QmBUqLftIg0AR4E4gq/R1VDd54DY8JZaip07Qq5uc6Kg3PnhuQStakbU7li5BVk52YTIRHUqGRNV4FU0lT9Bc7suF8B+f4LxxgTEK+95iQPcB5TUkIygXyQ9gHZudkACML09dPpeFZHj6MKHyVNINmq+qZfIzHGBMbYsTB+vHO7rghERUGIrcCXk5fDE1OfICk9CUGIkAiiIqNIjEv0OrSwUtIE8oaIDMPpPC+8pO08v0RljPGPCROgTx/43e/g6afhl1+c5BFCtY+Vu1dy24TbSNuaxgMXPUCPFj1I3ZhKYlwiCY1DpxzlQUkTSGugL3AFvzVhqfvaGBMKvvoKevWCSy6BSZMgJgY6d/Y6qtMyKn0U90++n6jIKL7o+QXdm3cHsJqHR0qaQG4BzlHVHH8GY4zxk6+/hh49oH17mDzZSR4hZP/h/dw/+X7+s/A//P7s3zP6ptE0qt7I67DCXkkTyGIgFtjhx1iMMf4wdSrceCO0bAnffAM1QutOpblb5tJrfC/W7lvL8MThDP3dUCIjIr0Oy1DyBBILLBeRORzbB2K38RoTzKZPd1YVbNoUvvsOatb0OqISy9d8Xkt9jSemPUH9avWZfsd0Op3VyeuwTCElTSDHr19ujAl2P/8Mf/yjM1Bw6lSoVcvriEpse9Z27vjyDr5Z9Q03XXATH133EWdUPsPrsMxxSjoSfbq/AzHG+NCsWXDNNdCwIUybFlLTlHy7+lv6fd6PjMMZvP/H9xkYP9AWhQpSp0wgIjJDVTuJyH6cu64KdgGqqqG76owx5VVaGlx1lZM0vv8e6tf3OqISycnL4anvn+JfM/9FyzotmdpvKq3qtvI6LHMKxdVAqgKoamjdsmFMuEpPhz/8wenr+P57pwYSAlbvWc1tE25jzpY53Bt/L69e9SqVK1b2OixTjOISiBazv1REJBb4P6CVe40BwK9AMs58W+uAW1V1rz+ub0y5tGQJXHklVKniJI+zzvI6ohL5dNGn3DvpXiIjIhl/y3hubnGz1yGZEiougdQVkUEn26mqr5byum8A36hqDxGJAqoAQ4FpqvqCiAwBhgCDS3l+Y8LL8uXQpQtUrOgkjyZNvI6oWFk5Wfx58p9JSk+i01mdGH3TaM6qERpJzziKSyCRQDWcPg+fEJEaOMvh3gHgDk7MEZHuQKJ7WBKQgiUQY4q3ahVccYWzONT338P553sdUbHmbZ1Hr/G9WL13NX///d956vKnbBr2ECSqJ2+lEpF5qtrepxcUaQt8CCwF2gBpOCscblbVWPcYAfYefX3c+wcCAwHq1asXP3bsWF+GF1BZWVlUq1bN6zD8LhzK6VUZo7dupe0jjxBx+DDpr73GAT/WPHxRRlVlwuYJfLDmA2IrxvLkBU/SNratjyL0jXD499q5c+c0Ve1Q5hOp6kl/gPmn2l+aH6ADkAtc4r5+A3gG2HfccXuLO1d8fLyGsh9++MHrEAIiHMrpSRnXr1eNi1OtWVN1wQK/X66sZdyRtUOvHX2t8jTafUx33XVgl28C87Fw+PcKzFUffJ9HFJNfupQ5Q51oE7BJVWe5r8cD7YHtItIAwH20aVOMOZnNm51mq717nRHmbdp4HdEpTVszjTbvt2Hammm8fc3bfN7zc2pVCZ2BjaZop0wgqrrH1xdU1W3ARhFp5m7qgtOcNRHo727rD3zp62sbUy5s2+Ykjx07YMoUiI/3OqKTOpJ3hKHThtJ1VFdio2OZffdsHrj4ARsYWE541Wv1IDDavQNrDXAnTjIbJyJ3AeuBWz2KzZjgtXOnc7fV5s3OxIiXXOJ1RCe1du9aen/Wm182/cLd7e/mtateo2pUVa/DMj7kSQJR1QU4fSHH80eTmTHlw+7dzjiPtWudKdk7Be/EgsmLkxk4aSCCMK7HOG5peYvXIRk/sPvmjAl2qanOeh7JybB+vbMwVBAuQZu6MZVvV3/L3K1zmbRiEgmNEvj05k+Ji43zOjTjJ5ZAjAlmqalOf0d2tvP65Zeha1dvYypC6sZUOid15nCes9pD/zb9+ei6j6gYWdHjyIw/FXcXljHGK6rw6qu/JY+ICMgJvkVB1+9bz73/u7cgeURIBM1qNbPkEQYsgRgTjNatg6uvhvHjncQRGQmVKgVV01VGdgZDpg6h2dvNWL5rORUiKhApkVSKrGRrlIcJa8IyJpjk5cE778DQoSACb70F7drBjz86ySMhwesIyc3P5cO0DxmWMoxdB3fRr00/nr3iWTZmbCRlXQqJcYkkNPY+TuN/lkCMCRZLl8Jdd8Evvzi1j/ffh7PPdvZddpm3seHMWjFpxST++t1fWb5rOYlxibzyh1do38CZ7ahR9UaWOMKMJRBjvJaTAy+8AM8+C9WqwahRcPvtTg0kSCzYtoBHFz7K/B/n07RWU77s9SXXNb3OBgSGOUsgxnhp9myn1rF4MfTqBW+8EVTLz27O3MzffvgbSQuSiKkQw1vXvMU98fdYB7kBLIEY440DB+Dvf4fXX4cGDWDiRLjuOq+jKnAg5wAvzXyJl2a+RG5+Lo8mPMrlEZfT7eJuXodmgoglEGMCbdo0uPtuZ0T5vfc6zVc1angdFQB5+XkkpSfxt+//xtasrdza8lZe6PICTWo2ISUlxevwTJCxBGJMoOzdC489BiNGOIs+paTA5Zd7HVWBqWum8ui3j7Jw+0IubXQpE26dYJ3i5pQsgRgTCJ99Bg884EyGOHgwDBsGlSt7HRUAS3cu5a/f/ZXJKyfTJLYJyT2SuaXFLdZBboplCcQYf9q6Ff78ZyeBtG0L//sftPfpIp+ltuPADob9MIyP5n1EtahqvNT1JR68+EEqVajkdWgmRFgCMcYfVOHf/4ZHH4VDh+D5553nFb2/e+nQkUO8/svrPD/jeQ4eOch9He5jWOIwalep7XVoJsRYAjHG19asgXvugalT4fe/h48+gqZNvY6KfM1n7OKxPDHtCTZkbOD6Ztfzryv/RbPazYp/szFFsARijK/k5cGbb8Lf/ubMXfXeezBwoDOXlcdmbJjBoCmDmLNlDu3qt+OT7p/QuUlnr8MyIc4SiDE+UHXtWqdzfPZs6NbNSR6NGnkdFqv2rGLw1MF8tuwzGsY0JOmGJPpc2IcI8T6pmdBnCcSYsjh8GJ57jvjnnoPYWBgzBnr29Hwakj2H9vDM9Gd4Z847REVGMTxxOI92fJQqFat4GpcpXyyBGFNaqanONCTLlrGja1fqf/op1Pa2IzonL4d3Zr/DMz8+Q8bhDAa0HcDwzsNpENPA07hM+WQJxJjTlZUFTz7pTLXeqBFMnszyypWp72HyUFU+W/YZg6cOZvXe1fzh3D/wcteXaV2vtWcxmfLPGkKNOR3ffgutWjnJ44EHYMkSuOYaT0Oas3kOv//k9/T4bw+iK0Tz9e1fM6XPFEsexu+sBmJMSezZA4MGQVISNG8OP/3k+Rod6/etZ+j3Q/l00afUrVqXD7p9wIB2A6gQYf+tTWB49i9NRCKBucBmVe0mIk2AsUAtIA3oq6rBtwC0CS8zZ8Lbb8M338D+/U7T1d/+BtHRnoU0dc1U/vnjP5m5cSaREZE8+bsnGXzZYGIqxXgWkwlPXv6p8jCwDKjuvn4ReE1Vx4rI+8BdwHteBWcMH34I990H+fnOXVWffAL9+nkWTvq2dJ6f8TzJS5IBiJRIxt00jhsuuMGzmEx486QPREQaAX8E/s99LcAVwHj3kCTA/lcYb/z4I3Tt6owmz893tkVEwObNAQ9le9Z2Xk19lbbvt6XtB23579L/HrN/2a5lAY/JmKO8qoG8DjwOHK1z1wL2qWqu+3oT0LCoN4rIQGAgQL169UJ6jYKsrKyQjr+kQqKcqsQuWEBcUhKx6enk1KzJjhtvpMH//ofk5qIVKpBevTqZJymHL8uYk5/DzN0zmbJtCrP3zCaffJrHNOeh8x7izMpnMmzJMI7kH6GCVKD6nuoB+92GxOfoA+FSTp9Q1YD+AN2Ad93nicAkoDawqtAxjYHFxZ0rPj5eQ9kPP/zgdQgBEdTlzM9X/fZb1U6dVEH1zDNV33hD9eBBZ//MmarPPec8nkJZy5ifn68zN8zUe766R2NfiFWeRhu+0lCHfDdEl+5YesyxMzfM1Od+fE5nbjh1TL4W1J+jD4VDOYG56oPvcy9qIJcB14vItUA0Th/IG0CsiFRQpxbSCAh8e4EJH6rw9dcwfDjMmuWM53jnHRgw4NgO8oQE58dP1u9bz38W/oeRC0eyYvcKKleozE0X3ET/Nv25oskVREZEnvCehMYJttCTCQoBTyCq+gTwBICIJAKPqertIvJfoAfOnVj9gS8DHZsJA6rw1VdO4khLg7PPhg8+gP79oVJg1sHIysliwtIJJKUn8cO6HwC4/OzLGXLZEHq06GF3U5mQEUw3jA8GxorIP4H5wMcex2PKk/x8+OILeOYZWLAAzjkHPv4Y+vYNyBod+ZpPyroUktKTmLB0AgeOHODcmufyj8R/0PfCvjSp2cTvMRjja54mEFVNAVLc52uAi72Mx5RDeXkwYYKTOBYvdtYiT0qC3r2hgv//+f+661dGpo9k1MJRbMzcSPVK1endujf92/SnY+OOtmysCWnBVAMxxnfy8iA5Gf75T1i2DC64AEaPdmbKjTyxX8GX9h7ay9jFYxm5cCS/bPqFCIngqnOv4l9d/0X3Zt2pXDE41kI3pqwsgZjyJTcXPv0Unn0WVqxw5q1KToabb/Zr4sjNz2XSikkkpScx8deJ5OTl0KpuK17q+hK3t77dZsM15ZIlEFM+HDkCo0Y5iWPNGmjTxmm6uuEGv64IuGDbApIWJJE0L4m9R/ZSu0pt7utwH/3a9KNd/XbWRGXKNUsgJrQdPuz0aTz3HKxfD/Hx8OWXcN11flvUaXvWdkYvGk1SehILty+kYkRFLj3jUh678jGuOe8aKkb6v1PemGBgCcSEpuxs5y6qF16ATZvgkkvg3XedqdX9kDiyc7OZ+OtEktKTmLJqCnmax8UNL+ada9+hZ8ueLJq9iMRmiT6/rjHBzBKICS2HDjmTHL74Imzd6kypPmIEXHmlzxOHqvLLpl9ISk8ieUky+7L30TCmIX/t+Ff6tenHBXUu8On1jAk1lkBMaDhwAN5/H156CbZvh8svd+6qSkz0eeJYv289oxaOYmT6SFbuWUmVilUKRod3jutc5OhwY8KRJRAT3Pbvd5qmXn4Zdu1yahrjxsHvf+/TyxQ1OjwxLpEnOj1ho8ONOQlLICY4ZWQ4Czm9+qqzGuDVV8NTT0HHjj67RL7m88PaH5zR4csmcPDIQc6teS7DE4fTt01f4mLjfHYtY8ojSyAmuOzdC2++Ca+/Dvv2QbduTuK42HeTFBw/OrxGpRr0ad2Hfm362ehwY06DJRATHHbvdpLGm29CZqYzfuNvf3Nuy/WBPYf2kLw4maT0JGZtnlUwOvylri9xfbPrbXS4MaVgCcR4a+dOp5nq7bchKwt69HASR5s2ZT71kbwjTFk95ZjR4a3rtublri/Tu3VvGx1uTBlZAjHe2LbN6Rh/7z3n1tyePeHJJ52pR8ro6OjwTxd/yo4DO6hTpQ73dbiP/m3607Z+W2uiMsZHLIGYwFF1phd57TWYO9eZt6p3bydxNG9ehtMqny37jI/nf8yK3StYvXc1UZFRXNf0Ovq36c/V511to8ON8QNLIMZ/Dh+G+fNpNG6cs9pfSopzKy44ExuOGQO33npap1RV1u5bS9qWNOZtnUfa1jRmbZpFZk4mABESwaMJjzL0d0M5o/IZPi6QMaYwSyDGd7Zvh9RUmDnT+Zk7Fw4f5jyAuDho2NDpLHfWvYfVq095OlVl9d7VxySLeVvnsTd7LwAVIirQum5rmtZqStrWNBRFEGpVrmXJw5gAsARiSicvz1mg6WiySE39LSFERTl3T/35z5CQwExVOvbo4RzTpQvk5DjHJCYWnC5f81m1Z9UJySLjcAYAFSMqcmG9C+nRogfxDeKJPzOe1nVbU6lCJVI3ptJlZBdy8nKIiowiMS7xxHiNMT5nCcSUzL59MGvWbwnjl1+cu6YA6tVzBvjde6/z2L49REcXvDUnJcV5kpAA06aR/8MPbGjXhJ+rrmHelPGkbU1j/rb5ZB52mqEqRVbiwnoX0qtVr4Jk0apuK6Iio4oMLaFxAtP6TSNlXQqJcYkkNE7w52/CGOOyBGJOpAorV/6WLGbOhKVLne0REXDhhdCvn5MsOnZ0mqdOcmdTXn4e6w6sY1T6qIKaxXyZT9ZsJ/lEV4imTb023N769oJk0bJOy9Pu9E5onGCJw5gAswRi4OBBmDPn2P6L3budfbGxcOmlzm22HTs6I8Jjip4XKjc/l+W7lh/TDLVg2wIOHDkAc6Fyhcq0rd+W/m36FySLC2pfYHdIGROiLIGEo40bj61dLFjg3FIL0KwZXH/9b7WL5s2LXNEvNz+XpTuXnpAsDuUeAqBKxSq0q9+OAe0GUCWjCn2u6EPz2s2pEGH/5IwpL+x/c3mXk+MkiKMd3TNnOgswAVSp4tQoHn/cSRaXXgq1ap1wiiN5R1iyc4mTKLakkbY1jfTt6WTnZgNQLaoa7eq34574e2jfoD3xZ8bTrFazgmnPU1JSaFW37AMEjTHBJeAJREQaAyOBeoACH6rqGyJyBpAMxAHrgFtVdW+g4wt5O3ce2xQ1Z46zeh/AWWdBp06/1S4uvBAqHtt8lJOXw+Idi49JFgu3L+Rw3mEAYqJiaN+gPfd3uL8gWTSt1ZQI8d+648aY4ORFDSQXeFRV54lIDJAmIt8BdwDTVPUFERkCDAEGexBf6MjLczq3CzdHrVrl7KtY0bkb6r77nGSRkOCMwyjkcO5hFm2Ze0yyWLRjETl5OQDUqFSD9g3a8+DFDxYki/POOM+ShTEG8CCBqOpWYKv7fL+ILAMaAt2BRPewJCAFSyDHysw88VbaTOfWV+rWdRLF3Xc7j/HxUPm3GWazc7NZtHkOaVvTCpLF4h2LOZJ/BIDY6FjiG8TzyCWPFCSLc2qeY8nCGHNSokdHBXtxcZE44EegFbBBVWPd7QLsPfr6uPcMBAYC1KtXL37s2LEBi9fXsrKyqFatWtE7Vam8ZQvVFy+mxpIlVF+yhKpr1yKqqAgHmjQho1UrMlu2JKNlS7LPPLPgVtrDeYdZfWA1K/avYEXWClbsX8G6g+vI0zwAqleoTtOYpjSt1pTzY86nabWmNIhu4LdJBk9ZznLCylh+hEM5O3funKaqHcp6Hs8SiIhUA6YDz6rqZyKyr3DCEJG9qlrzVOfo0KGDzp0719+h+k1KSgqJR0djHzrkTP1RuLN7505nX/XqThPU0b6Liy92tgEHjxwkfVu6U7NwaxdLdy4tSBa1q9R2bpltEF9Qszi7xtkBnZH2mHKWU1bG8iMcyikiPkkgntyFJSIVgQnAaFX9zN28XUQaqOpWEWkA7PAitoDZvJk6KSnw5ZdOspg377dbaZs2hWuv/S1htGgBERFk5WQ5yWLZJwXJYtmuZeRrPgB1q9YlvkE83Zt1L0gWjas3tunLjTF+4cVdWAJ8DCxT1VcL7ZoI9AdecB+/DHRsfnPkCKSnH1u72LCBluBM+XHxxfDYY7/dSlunDvsP72fBtgWkbZ1K2pcvkrYljeW7lqM4Ncb61eoT3yCemy+4uSBZNIxpaMnCGBMwXtRALgP6AotEZIG7bShO4hgnIncB64HTm+c7mOza5XRwH+3snj3baaICaNzYSRSDBpFWqRLxAwaQqdnM3zrfqVX8NJa0LWms2L2iIFmcGXMm8Q3i6dmyZ0GyODPmTA8LaIwx3tyFNQM42Z/JXQIZi0/k58OyZcfeSrtihbOvQgVo1w4GDiy4lXZfnZiCZPH1gols/OBVVu5ZWXC6RtUbEd8gnttb316QLOpXq+9R4Ywx5uRsJPrp2r/fqVEUnsY8w5lynNq1nUQxYAB07MjeFucwL2O528E9gbTPh7J6729rYNSrVI+OTTrSv03/gmRRt2pdjwpmjDGnxxLIqajC2rXH1i4WLXJqHSLO+t3uJIN7213A3Cr7SNs2j7Stc0mb9wFrv19bcKq42DjiG8RzV7u7aN+gPe0btGfJnCXl/m4PY0z5ZQmksOxsSEs7diqQ7dudfTExTgf3U0+R0b4lcxtHMivLrV1sGcb6z9cXnOacmufQ4cwOBXNDtW/QnlpVTpxjyhhjQll4JpDUVGd97latnMkGjyaLtDTnjingUFwjVrU9Ey7rwb52Lfix6i7m7phP2pYRbJy/EeY7pzrvjPO4tNGlPHDRAwXJomblUw5fMcaYciH8EkhqqrOUak7Ob9uio+Gii+Avf4GOHZlzVgU6TbqJnPxNkD8f0pzDmtZqSqezOhUMymvXoB2x0ScMljfGmLAQfgkkJeW3AXsREXDPPfD6684a3a6pPz1Pbr5zjCD8qf2fePkPL1O9UnUPAjbGmOAUfjPlJSZCpUoQGek89u17TPIASIxLpFKFSkRKJNEVormz7Z2WPIwx5jjhVwNJSIBp05yaSGKi8/r4QxonMK3fNFLWpZAYl2hrbRtjTBHCL4GAkzSKSBzHHNI4wRKHMcacQvg1YRljjPEJSyDGGGNKxRKIMcaYUrEEYowxplQsgRhjjCkVSyDGGGNKxbM10X1BRHbiLD4VqmoDu7wOIgDCoZxWxvIjHMrZTFVjynqSkB4Hoqp1vI6hLERkri8Wtg924VBOK2P5EQ7lFJG5vjiPNWEZY4wpFUsgxhhjSsUSiLc+9DqAAAmHcloZy49wKKdPyhjSnejGGGO8YzUQY4wxpWIJxBhjTKlYAvETEblaRH4VkVUiMuQkx9wqIktFZImIfFpoe38RWen+9A9c1KenjGXME5EF7s/EwEV9+oorp4i8VqgsK0RkX6F95eKzLKaM5emzPEtEfhCR+SKyUESuLbTvCfd9v4rIVYGNvORKW0YRiRORQ4U+y/eLvZiq2o+Pf4BIYDVwDhAFpAMtjjvmfGA+UNN9Xdd9PANY4z7WdJ/X9LpMviyj+zzL6zL4qpzHHf8gMKK8fZYnK2N5+yxxOpfvc5+3ANYVep4OVAKauOeJ9LpMPi5jHLD4dK5nNRD/uBhYpaprVDUHGAt0P+6Yu4F3VHUvgKrucLdfBXynqnvcfd8BVwco7tNRljKGkpKUs7DbgDHu8/L0WRZWuIyhpCTlVODo+tU1gC3u8+7AWFU9rKprgVXu+YJNWcp42iyB+EdDYGOh15vcbYU1BZqKyM8i8ouIXH0a7w0GZSkjQLSIzHW33+DvYMugxJ+HiJyN89fp96f7Xo+VpYxQvj7Lp4E+IrIJmIxT2yrpe4NBWcoI0MRt2pouIr8r7mIhPZVJiKuA08STCDQCfhSR1p5G5HtFllFV9wFnq+pmETkH+F5EFqnqag9j9YVewHhVzfM6ED8qqozl6bO8DfhEVV8RkQRglIi08jooHztZGbcCZ6nqbhGJB74QkZaqmnmyE1kNxD82A40LvW7kbitsEzBRVY+4VeIVOF+2JXlvMChLGVHVze7jGiAFaOfvgEvpdD6PXhzbtFOePsujji9jefss7wLGAahqKhCNM7liefosiyyj2zy3292ehtOX0vSUV/O606c8/uD85b0Gp6p/tCOr5XHHXA0kuc9r41Q7a+F0uK7F6XSt6T4/w+sy+biMNYFKhbav5BSdtsFeTve45sA63MG57rZy81meoozl6rMEvgbucJ9fgNM/IEBLju1EX0NwdqKXpYx1jpYJpxN+c3H/Xj0vcHn9Aa7F+Yt7NfCku204cL37XIBXgaXAIqBXofcOwOmkWwXc6XVZfF1GoKP7Ot19vMvrspSlnO7rp4EXinhvufgsT1bG8vZZ4tyV9LNbngXAHwq990n3fb8C13hdFl+XEbgZWOJumwdcV9y1bCoTY4wxpWJ9IMYYY0rFEogxxphSsQRijDGmVCyBGGOMKRVLIMYYY0rFEogxgIjcICIqIs29jsWYUGEJxBjHbcAM99EvRCTSX+c2xguWQEzYE5FqQCecKR56Fdo+WEQWiUi6iLzgbjtPRKa62+aJyLkikigikwq9720RucN9vk5EXhSRecAtInK3iMxx3z9BRKq4x9UTkc/d7eki0lFEhovII4XO+6yIPByQX4oxJWCTKRrjTHf9jaquEJGjE8nVdbdfoqoHReQM99jROKOxPxeRaJw/whoXfdoCu1W1PYCI1FLVj9zn/8RJWm8BbwLTVfVGt6ZSDWeKic+A10UkAie5BeMU4iZMWQIxxmm2esN9PtZ9LcSpZSkAAAFpSURBVMC/VfUggKruEZEYoKGqfu5uywYQkeLOn1zoeSs3ccTiJIkp7vYrgH7uefOADCDDTWjtgHrAfHUnuzMmGFgCMWHNrVlcAbQWEcVZ0U2B/57GaXI5tjk4+rj9Bwo9/wS4QVXT3WauxGLO/X/AHUB9YMRpxGSM31kfiAl3PYBRqnq2qsapamOcWXMzgDsL9VGcoar7gU1HF00SkUru/vVAC/d1LNDlFNeLAbaKSEXg9kLbpwH3ueeNFJEa7vbPcWY1vojfaivGBAVLICbc3YbzJV3YBKABMBGYKyILgMfcfX2Bh0RkITATqK+qG3HWV1jsPs4/xfWeAmbhzIa6vND2h4HOIrIISMOZMRV1liX9ARin5XuhKhOCbDZeY4KY23k+D7hFVVd6HY8xhVkNxJggJSItcNYRmWbJwwQjq4EYY4wpFauBGGOMKRVLIMYYY0rFEogxxphSsQRijDGmVCyBGGOMKZX/BxSGXUVfWJBtAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#plot\n",
+ "plt.title('CIFAR-10 time by accuracy')\n",
+ "plt.xlabel('Accuracy')\n",
+ "plt.ylabel('Time (min)')\n",
+ "plt.grid(True)\n",
+ "plt.plot(train_accuracy, time, 'g.-', label='Train')\n",
+ "plt.plot(test_accuracy, time, 'r.-', label='Test')\n",
+ "plt.legend()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.16"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/community-artifacts/Deep-learning/.ipynb_checkpoints/MADlib-Keras-model-selection-CNN-cifar10-v1-checkpoint.ipynb b/community-artifacts/Deep-learning/.ipynb_checkpoints/MADlib-Keras-model-selection-CNN-cifar10-v1-checkpoint.ipynb
new file mode 100755
index 0000000..70b9129
--- /dev/null
+++ b/community-artifacts/Deep-learning/.ipynb_checkpoints/MADlib-Keras-model-selection-CNN-cifar10-v1-checkpoint.ipynb
@@ -0,0 +1,8037 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Model Selection for CNN Using Keras and MADlib on CIFAR-10\n",
+ "\n",
+ "E2E classification example using MADlib calling a Keras CNN for different hyperparameters and model architectures on the CIFAR-10 dataset.\n",
+ "\n",
+ "The CIFAR-10 dataset consists of 60,000 32x32 colour images in 10 classes, with 6,000 images per class. There are 50,000 training images and 10,000 test images.\n",
+ "https://www.cs.toronto.edu/~kriz/cifar.html\n",
+ "\n",
+ "## Table of contents \n",
+ "\n",
+ "<a href=\"#setup\">0. Setup</a>\n",
+ "\n",
+ "<a href=\"#load_dataset\">1. Load dataset into table</a>\n",
+ "\n",
+ "<a href=\"#distr\">2. Setup distribution rules and call preprocessor</a>\n",
+ "\n",
+ "<a href=\"#arch\">3. Define and load model architectures</a>\n",
+ "\n",
+ "<a href=\"#mst\">4. Define and load model selection tuples</a>\n",
+ "\n",
+ "<a href=\"#train\">5. Train</a>\n",
+ "\n",
+ "<a href=\"#plot\">6. Plot results</a>\n",
+ "\n",
+ "<a href=\"#predict\">7. Inference</a>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"setup\"></a>\n",
+ "# 0. Setup"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [],
+ "source": [
+ "%load_ext sql"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "u'Connected: gpadmin@cifar_places'"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Greenplum Database 5.x on GCP - via tunnel\n",
+ "%sql postgresql://gpadmin@localhost:8000/cifar_places\n",
+ " \n",
+ "# PostgreSQL local\n",
+ "#%sql postgresql://fmcquillan@localhost:5432/madlib"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>version</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>MADlib version: 1.17-dev, git revision: rc/1.16-rc1-95-gc62dfe7, cmake configuration time: Tue Mar 17 16:53:55 UTC 2020, build type: RelWithDebInfo, build system: Linux-2.6.32-754.6.3.el6.x86_64, C compiler: gcc 4.4.7, C++ compiler: g++ 4.4.7</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'MADlib version: 1.17-dev, git revision: rc/1.16-rc1-95-gc62dfe7, cmake configuration time: Tue Mar 17 16:53:55 UTC 2020, build type: RelWithDebInfo, build system: Linux-2.6.32-754.6.3.el6.x86_64, C compiler: gcc 4.4.7, C++ compiler: g++ 4.4.7',)]"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%sql select madlib.version();\n",
+ "#%sql select version();"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Import libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Using TensorFlow backend.\n"
+ ]
+ }
+ ],
+ "source": [
+ "from __future__ import print_function\n",
+ "import keras\n",
+ "from keras.datasets import cifar10\n",
+ "from keras.preprocessing.image import ImageDataGenerator\n",
+ "from keras.models import Sequential\n",
+ "from keras.layers import Dense, Dropout, Activation, Flatten, BatchNormalization\n",
+ "from keras.layers import Conv2D, MaxPooling2D\n",
+ "import os"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Others needed in this workbook"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import sys\n",
+ "import os\n",
+ "from matplotlib import pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"load_dataset\"></a>\n",
+ "# 1. Load dataset into table\n",
+ "\n",
+ "PXF can be used to load image data into Greenplum database.\n",
+ "\n",
+ "But for this demo, we will get the dataset from Keras and use the script called madlib_image_loader.py located at https://github.com/apache/madlib-site/tree/asf-site/community-artifacts/Deep-learning .\n",
+ "\n",
+ "If the script is not in the same folder as the notebook, you can use the following lines to import it."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import sys\n",
+ "sys.path.insert(1, '/Users/fmcquillan/workspace/madlib-site/community-artifacts/Deep-learning')\n",
+ "from madlib_image_loader import ImageLoader, DbCredentials\n",
+ "\n",
+ "# Specify database credentials, for connecting to db\n",
+ "db_creds = DbCredentials(db_name='cifar_places',\n",
+ " user='gpadmin',\n",
+ " host='localhost',\n",
+ " port='8000',\n",
+ " password='')\n",
+ "\n",
+ "#db_creds = DbCredentials(db_name='cifar_places',\n",
+ "# user='fmcquillan',\n",
+ "# host='localhost',\n",
+ "# port='5432',\n",
+ "# password='')\n",
+ "\n",
+ "# Initialize ImageLoader (increase num_workers to run faster)\n",
+ "iloader = ImageLoader(num_workers=5, db_creds=db_creds)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Load dataset into tables"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Load dataset into np array\n",
+ "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
+ "\n",
+ "%sql DROP TABLE IF EXISTS cifar10_train, cifar10_val;\n",
+ "\n",
+ "# Save images to temporary directories and load into database\n",
+ "iloader.load_dataset_from_np(x_train, y_train, 'cifar10_train', append=False)\n",
+ "iloader.load_dataset_from_np(x_test, y_test, 'cifar10_val', append=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>50000</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(50000L,)]"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%sql SELECT COUNT(*) FROM cifar10_train;"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>10000</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(10000L,)]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%sql SELECT COUNT(*) FROM cifar10_val;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"distr\"></a>\n",
+ "# 2. Setup distribution rules and call preprocessor\n",
+ "\n",
+ "In this example we will train on 4 VMs with 4 segments/VM and 4 GPUs/VM (i.e., 16 workers).\n",
+ "\n",
+ "First get the GPU configuration in the cluster using the MADlib helper function `gpu_configuration`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "Done.\n",
+ "1 rows affected.\n",
+ "20 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>hostname</th>\n",
+ " <th>gpu_descr</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix0</td>\n",
+ " <td>device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix0</td>\n",
+ " <td>device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
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+ " </tr>\n",
+ " <tr>\n",
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+ " <td>gpsix1</td>\n",
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+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix1</td>\n",
+ " <td>device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix2</td>\n",
+ " <td>device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix2</td>\n",
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+ " </tr>\n",
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+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix2</td>\n",
+ " <td>device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix3</td>\n",
+ " <td>device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix3</td>\n",
+ " <td>device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix3</td>\n",
+ " <td>device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix3</td>\n",
+ " <td>device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix4</td>\n",
+ " <td>device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix4</td>\n",
+ " <td>device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix4</td>\n",
+ " <td>device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix4</td>\n",
+ " <td>device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
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+ " (u'gpsix0', u'device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0'),\n",
+ " (u'gpsix0', u'device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0'),\n",
+ " (u'gpsix1', u'device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0'),\n",
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+ " (u'gpsix1', u'device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0'),\n",
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+ " (u'gpsix2', u'device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0'),\n",
+ " (u'gpsix2', u'device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0'),\n",
+ " (u'gpsix2', u'device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0'),\n",
+ " (u'gpsix2', u'device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0'),\n",
+ " (u'gpsix3', u'device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0'),\n",
+ " (u'gpsix3', u'device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0'),\n",
+ " (u'gpsix3', u'device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0'),\n",
+ " (u'gpsix3', u'device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0'),\n",
+ " (u'gpsix4', u'device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0'),\n",
+ " (u'gpsix4', u'device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0'),\n",
+ " (u'gpsix4', u'device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0'),\n",
+ " (u'gpsix4', u'device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0')]"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS host_gpu_mapping_tf;\n",
+ "SELECT * FROM madlib.gpu_configuration('host_gpu_mapping_tf');\n",
+ "SELECT * FROM host_gpu_mapping_tf ORDER BY hostname, gpu_descr;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Review the Greenplum segments in the `gp_segment_configuration` table:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "21 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
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+ " <th>mode</th>\n",
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+ " <th>port</th>\n",
+ " <th>hostname</th>\n",
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+ " <td>gpsix1</td>\n",
+ " <td>/data/primary0/gpseg4</td>\n",
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+ " <td>gpsix1</td>\n",
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+ " <tr>\n",
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+ " <td>12</td>\n",
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+ " <td>/data/primary0/gpseg12</td>\n",
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+ " <tr>\n",
+ " <td>15</td>\n",
+ " <td>13</td>\n",
+ " <td>p</td>\n",
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+ " <td>40001</td>\n",
+ " <td>gpsix3</td>\n",
+ " <td>gpsix3</td>\n",
+ " <td>/data/primary1/gpseg13</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>16</td>\n",
+ " <td>14</td>\n",
+ " <td>p</td>\n",
+ " <td>p</td>\n",
+ " <td>n</td>\n",
+ " <td>u</td>\n",
+ " <td>40002</td>\n",
+ " <td>gpsix3</td>\n",
+ " <td>gpsix3</td>\n",
+ " <td>/data/primary2/gpseg14</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>17</td>\n",
+ " <td>15</td>\n",
+ " <td>p</td>\n",
+ " <td>p</td>\n",
+ " <td>n</td>\n",
+ " <td>u</td>\n",
+ " <td>40003</td>\n",
+ " <td>gpsix3</td>\n",
+ " <td>gpsix3</td>\n",
+ " <td>/data/primary3/gpseg15</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>18</td>\n",
+ " <td>16</td>\n",
+ " <td>p</td>\n",
+ " <td>p</td>\n",
+ " <td>n</td>\n",
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+ " <td>40000</td>\n",
+ " <td>gpsix4</td>\n",
+ " <td>gpsix4</td>\n",
+ " <td>/data/primary0/gpseg16</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>19</td>\n",
+ " <td>17</td>\n",
+ " <td>p</td>\n",
+ " <td>p</td>\n",
+ " <td>n</td>\n",
+ " <td>u</td>\n",
+ " <td>40001</td>\n",
+ " <td>gpsix4</td>\n",
+ " <td>gpsix4</td>\n",
+ " <td>/data/primary1/gpseg17</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>20</td>\n",
+ " <td>18</td>\n",
+ " <td>p</td>\n",
+ " <td>p</td>\n",
+ " <td>n</td>\n",
+ " <td>u</td>\n",
+ " <td>40002</td>\n",
+ " <td>gpsix4</td>\n",
+ " <td>gpsix4</td>\n",
+ " <td>/data/primary2/gpseg18</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>21</td>\n",
+ " <td>19</td>\n",
+ " <td>p</td>\n",
+ " <td>p</td>\n",
+ " <td>n</td>\n",
+ " <td>u</td>\n",
+ " <td>40003</td>\n",
+ " <td>gpsix4</td>\n",
+ " <td>gpsix4</td>\n",
+ " <td>/data/primary3/gpseg19</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
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+ " (15, 13, u'p', u'p', u'n', u'u', 40001, u'gpsix3', u'gpsix3', u'/data/primary1/gpseg13'),\n",
+ " (16, 14, u'p', u'p', u'n', u'u', 40002, u'gpsix3', u'gpsix3', u'/data/primary2/gpseg14'),\n",
+ " (17, 15, u'p', u'p', u'n', u'u', 40003, u'gpsix3', u'gpsix3', u'/data/primary3/gpseg15'),\n",
+ " (18, 16, u'p', u'p', u'n', u'u', 40000, u'gpsix4', u'gpsix4', u'/data/primary0/gpseg16'),\n",
+ " (19, 17, u'p', u'p', u'n', u'u', 40001, u'gpsix4', u'gpsix4', u'/data/primary1/gpseg17'),\n",
+ " (20, 18, u'p', u'p', u'n', u'u', 40002, u'gpsix4', u'gpsix4', u'/data/primary2/gpseg18'),\n",
+ " (21, 19, u'p', u'p', u'n', u'u', 40003, u'gpsix4', u'gpsix4', u'/data/primary3/gpseg19')]"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT * FROM gp_segment_configuration ORDER BY dbid;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now JOIN the above 2 tables to build up various distribution rules, depending on your needs.\n",
+ "\n",
+ "We build distribution rules table for 4 VMs:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "Done.\n",
+ "16 rows affected.\n",
+ "16 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
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+ " <th>hostname</th>\n",
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+ " <td>2</td>\n",
+ " <td>gpsix0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
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+ " <td>gpsix0</td>\n",
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+ " </tr>\n",
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+ " <td>gpsix3</td>\n",
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+ ],
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+ " (15, u'gpsix3'),\n",
+ " (16, u'gpsix3'),\n",
+ " (17, u'gpsix3')]"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS segments_to_use_4VMs;\n",
+ "CREATE TABLE segments_to_use_4VMs AS\n",
+ " SELECT DISTINCT dbid, hostname FROM gp_segment_configuration JOIN host_gpu_mapping_tf USING (hostname)\n",
+ " WHERE role='p' AND content>=0 AND hostname!='gpsix4';\n",
+ "SELECT * FROM segments_to_use_4VMs ORDER BY hostname, dbid;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Run the preprocessor to generate the packed output table on the segments we want to use for training:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "Done.\n",
+ "1 rows affected.\n",
+ "16 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
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+ " <th>buffer_id</th>\n",
+ " </tr>\n",
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+ " <td>0</td>\n",
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+ " (55, [3125, 32, 32, 3], [3125, 10], 3),\n",
+ " (56, [3125, 32, 32, 3], [3125, 10], 2)]"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS cifar10_train_packed, cifar10_train_packed_summary;\n",
+ "\n",
+ "SELECT madlib.training_preprocessor_dl('cifar10_train', -- Source table\n",
+ " 'cifar10_train_packed', -- Output table\n",
+ " 'y', -- Dependent variable\n",
+ " 'x', -- Independent variable\n",
+ " NULL, -- Buffer size\n",
+ " 256.0, -- Normalizing constant\n",
+ " NULL, -- Number of classes\n",
+ " 'segments_to_use_4VMs' -- Distribution rules\n",
+ " );\n",
+ "\n",
+ "SELECT __dist_key__, independent_var_shape, dependent_var_shape, buffer_id FROM cifar10_train_packed ORDER BY __dist_key__;"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>source_table</th>\n",
+ " <th>output_table</th>\n",
+ " <th>dependent_varname</th>\n",
+ " <th>independent_varname</th>\n",
+ " <th>dependent_vartype</th>\n",
+ " <th>class_values</th>\n",
+ " <th>buffer_size</th>\n",
+ " <th>normalizing_const</th>\n",
+ " <th>num_classes</th>\n",
+ " <th>distribution_rules</th>\n",
+ " <th>__internal_gpu_config__</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>cifar10_train</td>\n",
+ " <td>cifar10_train_packed</td>\n",
+ " <td>y</td>\n",
+ " <td>x</td>\n",
+ " <td>smallint</td>\n",
+ " <td>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]</td>\n",
+ " <td>3125</td>\n",
+ " <td>256.0</td>\n",
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+ " <td>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'cifar10_train', u'cifar10_train_packed', u'y', u'x', u'smallint', [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], 3125, 256.0, 10, [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15])]"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT * FROM cifar10_train_packed_summary;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Same for validation dataset:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "Done.\n",
+ "1 rows affected.\n",
+ "16 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
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+ " (56, [625, 32, 32, 3], [625, 10], 2)]"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS cifar10_val_packed, cifar10_val_packed_summary;\n",
+ "\n",
+ "SELECT madlib.validation_preprocessor_dl('cifar10_val', -- Source table\n",
+ " 'cifar10_val_packed', -- Output table\n",
+ " 'y', -- Dependent variable\n",
+ " 'x', -- Independent variable\n",
+ " 'cifar10_train_packed', -- From training preprocessor step\n",
+ " NULL, -- Buffer size\n",
+ " 'segments_to_use_4VMs' -- Distribution rules\n",
+ " ); \n",
+ "\n",
+ "SELECT __dist_key__, independent_var_shape, dependent_var_shape, buffer_id FROM cifar10_val_packed ORDER BY __dist_key__;"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>source_table</th>\n",
+ " <th>output_table</th>\n",
+ " <th>dependent_varname</th>\n",
+ " <th>independent_varname</th>\n",
+ " <th>dependent_vartype</th>\n",
+ " <th>class_values</th>\n",
+ " <th>buffer_size</th>\n",
+ " <th>normalizing_const</th>\n",
+ " <th>num_classes</th>\n",
+ " <th>distribution_rules</th>\n",
+ " <th>__internal_gpu_config__</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>cifar10_val</td>\n",
+ " <td>cifar10_val_packed</td>\n",
+ " <td>y</td>\n",
+ " <td>x</td>\n",
+ " <td>smallint</td>\n",
+ " <td>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]</td>\n",
+ " <td>625</td>\n",
+ " <td>256.0</td>\n",
+ " <td>10</td>\n",
+ " <td>[2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]</td>\n",
+ " <td>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'cifar10_val', u'cifar10_val_packed', u'y', u'x', u'smallint', [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], 625, 256.0, 10, [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15])]"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT * FROM cifar10_val_packed_summary;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"arch\"></a>\n",
+ "# 3. Define and load model architectures\n",
+ "\n",
+ "Model architecture from https://keras.io/examples/cifar10_cnn/"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "num_classes = 10"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "WARNING:tensorflow:From /Users/fmcquillan/Library/Python/2.7/lib/python/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
+ "\n",
+ "WARNING:tensorflow:From /Users/fmcquillan/Library/Python/2.7/lib/python/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
+ "\n",
+ "WARNING:tensorflow:From /Users/fmcquillan/Library/Python/2.7/lib/python/site-packages/keras/backend/tensorflow_backend.py:3976: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
+ "\n",
+ "WARNING:tensorflow:From /Users/fmcquillan/Library/Python/2.7/lib/python/site-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
+ "\n",
+ "WARNING:tensorflow:From /Users/fmcquillan/Library/Python/2.7/lib/python/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
+ "Instructions for updating:\n",
+ "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
+ "_________________________________________________________________\n",
+ "Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ "conv2d_1 (Conv2D) (None, 32, 32, 32) 896 \n",
+ "_________________________________________________________________\n",
+ "activation_1 (Activation) (None, 32, 32, 32) 0 \n",
+ "_________________________________________________________________\n",
+ "conv2d_2 (Conv2D) (None, 30, 30, 32) 9248 \n",
+ "_________________________________________________________________\n",
+ "activation_2 (Activation) (None, 30, 30, 32) 0 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_1 (MaxPooling2 (None, 15, 15, 32) 0 \n",
+ "_________________________________________________________________\n",
+ "dropout_1 (Dropout) (None, 15, 15, 32) 0 \n",
+ "_________________________________________________________________\n",
+ "conv2d_3 (Conv2D) (None, 15, 15, 64) 18496 \n",
+ "_________________________________________________________________\n",
+ "activation_3 (Activation) (None, 15, 15, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "conv2d_4 (Conv2D) (None, 13, 13, 64) 36928 \n",
+ "_________________________________________________________________\n",
+ "activation_4 (Activation) (None, 13, 13, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_2 (MaxPooling2 (None, 6, 6, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "dropout_2 (Dropout) (None, 6, 6, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "flatten_1 (Flatten) (None, 2304) 0 \n",
+ "_________________________________________________________________\n",
+ "dense_1 (Dense) (None, 512) 1180160 \n",
+ "_________________________________________________________________\n",
+ "activation_5 (Activation) (None, 512) 0 \n",
+ "_________________________________________________________________\n",
+ "dropout_3 (Dropout) (None, 512) 0 \n",
+ "_________________________________________________________________\n",
+ "dense_2 (Dense) (None, 10) 5130 \n",
+ "_________________________________________________________________\n",
+ "activation_6 (Activation) (None, 10) 0 \n",
+ "=================================================================\n",
+ "Total params: 1,250,858\n",
+ "Trainable params: 1,250,858\n",
+ "Non-trainable params: 0\n",
+ "_________________________________________________________________\n"
+ ]
+ }
+ ],
+ "source": [
+ "model1 = Sequential()\n",
+ "\n",
+ "model1.add(Conv2D(32, (3, 3), padding='same',\n",
+ " input_shape=x_train.shape[1:]))\n",
+ "model1.add(Activation('relu'))\n",
+ "model1.add(Conv2D(32, (3, 3)))\n",
+ "model1.add(Activation('relu'))\n",
+ "model1.add(MaxPooling2D(pool_size=(2, 2)))\n",
+ "model1.add(Dropout(0.25))\n",
+ "\n",
+ "model1.add(Conv2D(64, (3, 3), padding='same'))\n",
+ "model1.add(Activation('relu'))\n",
+ "model1.add(Conv2D(64, (3, 3)))\n",
+ "model1.add(Activation('relu'))\n",
+ "model1.add(MaxPooling2D(pool_size=(2, 2)))\n",
+ "model1.add(Dropout(0.25))\n",
+ "\n",
+ "model1.add(Flatten())\n",
+ "model1.add(Dense(512))\n",
+ "model1.add(Activation('relu'))\n",
+ "model1.add(Dropout(0.5))\n",
+ "model1.add(Dense(num_classes))\n",
+ "model1.add(Activation('softmax'))\n",
+ "\n",
+ "model1.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'{\"class_name\": \"Sequential\", \"keras_version\": \"2.2.4\", \"config\": {\"layers\": [{\"class_name\": \"Conv2D\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"conv2d_1\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"linear\", \"trainable\": true, \"data_format\": \"channels_last\", \"filters\": 32, \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"batch_input_shape\": [null, 32, 32, 3], \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"Activation\", \"config\": {\"activation\": \"relu\", \"trainable\": true, \"name\": \"activation_1\"}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"conv2d_2\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"linear\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"valid\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 32, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"Activation\", \"config\": {\"activation\": \"relu\", \"trainable\": true, \"name\": \"activation_2\"}}, {\"class_name\": \"MaxPooling2D\", \"config\": {\"name\": \"max_pooling2d_1\", \"trainable\": true, \"data_format\": \"channels_last\", \"pool_size\": [2, 2], \"padding\": \"valid\", \"strides\": [2, 2]}}, {\"class_name\": \"Dropout\", \"config\": {\"rate\": 0.25, \"noise_shape\": null, \"trainable\": true, \"seed\": null, \"name\": \"dropout_1\"}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"conv2d_3\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"linear\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 64, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"Activation\", \"config\": {\"activation\": \"relu\", \"trainable\": true, \"name\": \"activation_3\"}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"conv2d_4\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"linear\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"valid\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 64, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"Activation\", \"config\": {\"activation\": \"relu\", \"trainable\": true, \"name\": \"activation_4\"}}, {\"class_name\": \"MaxPooling2D\", \"config\": {\"name\": \"max_pooling2d_2\", \"trainable\": true, \"data_format\": \"channels_last\", \"pool_size\": [2, 2], \"padding\": \"valid\", \"strides\": [2, 2]}}, {\"class_name\": \"Dropout\", \"config\": {\"rate\": 0.25, \"noise_shape\": null, \"trainable\": true, \"seed\": null, \"name\": \"dropout_2\"}}, {\"class_name\": \"Flatten\", \"config\": {\"trainable\": true, \"name\": \"flatten_1\", \"data_format\": \"channels_last\"}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_1\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"linear\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 512, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Activation\", \"config\": {\"activation\": \"relu\", \"trainable\": true, \"name\": \"activation_5\"}}, {\"class_name\": \"Dropout\", \"config\": {\"rate\": 0.5, \"noise_shape\": null, \"trainable\": true, \"seed\": null, \"name\": \"dropout_3\"}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_2\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"linear\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Activation\", \"config\": {\"activation\": \"softmax\", \"trainable\": true, \"name\": \"activation_6\"}}], \"name\": \"sequential_2\"}, \"backend\": \"tensorflow\"}'"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model1.to_json()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Model architecture from https://machinelearningmastery.com/how-to-develop-a-cnn-from-scratch-for-cifar-10-photo-classification/"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "WARNING:tensorflow:From /Users/fmcquillan/Library/Python/2.7/lib/python/site-packages/keras/backend/tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
+ "\n",
+ "WARNING:tensorflow:From /Users/fmcquillan/Library/Python/2.7/lib/python/site-packages/keras/backend/tensorflow_backend.py:1834: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead.\n",
+ "\n",
+ "_________________________________________________________________\n",
+ "Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ "conv2d_5 (Conv2D) (None, 32, 32, 32) 896 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_1 (Batch (None, 32, 32, 32) 128 \n",
+ "_________________________________________________________________\n",
+ "conv2d_6 (Conv2D) (None, 32, 32, 32) 9248 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_2 (Batch (None, 32, 32, 32) 128 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_3 (MaxPooling2 (None, 16, 16, 32) 0 \n",
+ "_________________________________________________________________\n",
+ "dropout_4 (Dropout) (None, 16, 16, 32) 0 \n",
+ "_________________________________________________________________\n",
+ "conv2d_7 (Conv2D) (None, 16, 16, 64) 18496 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_3 (Batch (None, 16, 16, 64) 256 \n",
+ "_________________________________________________________________\n",
+ "conv2d_8 (Conv2D) (None, 16, 16, 64) 36928 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_4 (Batch (None, 16, 16, 64) 256 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_4 (MaxPooling2 (None, 8, 8, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "dropout_5 (Dropout) (None, 8, 8, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "conv2d_9 (Conv2D) (None, 8, 8, 128) 73856 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_5 (Batch (None, 8, 8, 128) 512 \n",
+ "_________________________________________________________________\n",
+ "conv2d_10 (Conv2D) (None, 8, 8, 128) 147584 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_6 (Batch (None, 8, 8, 128) 512 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_5 (MaxPooling2 (None, 4, 4, 128) 0 \n",
+ "_________________________________________________________________\n",
+ "dropout_6 (Dropout) (None, 4, 4, 128) 0 \n",
+ "_________________________________________________________________\n",
+ "flatten_2 (Flatten) (None, 2048) 0 \n",
+ "_________________________________________________________________\n",
+ "dense_3 (Dense) (None, 128) 262272 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_7 (Batch (None, 128) 512 \n",
+ "_________________________________________________________________\n",
+ "dropout_7 (Dropout) (None, 128) 0 \n",
+ "_________________________________________________________________\n",
+ "dense_4 (Dense) (None, 10) 1290 \n",
+ "=================================================================\n",
+ "Total params: 552,874\n",
+ "Trainable params: 551,722\n",
+ "Non-trainable params: 1,152\n",
+ "_________________________________________________________________\n"
+ ]
+ }
+ ],
+ "source": [
+ "model2 = Sequential()\n",
+ "\n",
+ "model2.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same', input_shape=(32, 32, 3)))\n",
+ "model2.add(BatchNormalization())\n",
+ "model2.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
+ "model2.add(BatchNormalization())\n",
+ "model2.add(MaxPooling2D((2, 2)))\n",
+ "model2.add(Dropout(0.2))\n",
+ "model2.add(Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
+ "model2.add(BatchNormalization())\n",
+ "model2.add(Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
+ "model2.add(BatchNormalization())\n",
+ "model2.add(MaxPooling2D((2, 2)))\n",
+ "model2.add(Dropout(0.3))\n",
+ "model2.add(Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
+ "model2.add(BatchNormalization())\n",
+ "model2.add(Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
+ "model2.add(BatchNormalization())\n",
+ "model2.add(MaxPooling2D((2, 2)))\n",
+ "model2.add(Dropout(0.4))\n",
+ "model2.add(Flatten())\n",
+ "model2.add(Dense(128, activation='relu', kernel_initializer='he_uniform'))\n",
+ "model2.add(BatchNormalization())\n",
+ "model2.add(Dropout(0.5))\n",
+ "model2.add(Dense(10, activation='softmax'))\n",
+ "\n",
+ "model2.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'{\"class_name\": \"Sequential\", \"keras_version\": \"2.2.4\", \"config\": {\"layers\": [{\"class_name\": \"Conv2D\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"conv2d_5\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"data_format\": \"channels_last\", \"filters\": 32, \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"batch_input_shape\": [null, 32, 32, 3], \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"BatchNormalization\", \"config\": {\"beta_constraint\": null, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"name\": \"batch_normalization_1\", \"epsilon\": 0.001, \"trainable\": true, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"scale\": true, \"axis\": -1, \"gamma_constraint\": null, \"gamma_regularizer\": null, \"beta_regularizer\": null, \"momentum\": 0.99, \"center\": true}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"conv2d_6\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 32, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"BatchNormalization\", \"config\": {\"beta_constraint\": null, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"name\": \"batch_normalization_2\", \"epsilon\": 0.001, \"trainable\": true, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"scale\": true, \"axis\": -1, \"gamma_constraint\": null, \"gamma_regularizer\": null, \"beta_regularizer\": null, \"momentum\": 0.99, \"center\": true}}, {\"class_name\": \"MaxPooling2D\", \"config\": {\"name\": \"max_pooling2d_3\", \"trainable\": true, \"data_format\": \"channels_last\", \"pool_size\": [2, 2], \"padding\": \"valid\", \"strides\": [2, 2]}}, {\"class_name\": \"Dropout\", \"config\": {\"rate\": 0.2, \"noise_shape\": null, \"trainable\": true, \"seed\": null, \"name\": \"dropout_4\"}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"conv2d_7\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 64, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"BatchNormalization\", \"config\": {\"beta_constraint\": null, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"name\": \"batch_normalization_3\", \"epsilon\": 0.001, \"trainable\": true, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"scale\": true, \"axis\": -1, \"gamma_constraint\": null, \"gamma_regularizer\": null, \"beta_regularizer\": null, \"momentum\": 0.99, \"center\": true}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"conv2d_8\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 64, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"BatchNormalization\", \"config\": {\"beta_constraint\": null, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"name\": \"batch_normalization_4\", \"epsilon\": 0.001, \"trainable\": true, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"scale\": true, \"axis\": -1, \"gamma_constraint\": null, \"gamma_regularizer\": null, \"beta_regularizer\": null, \"momentum\": 0.99, \"center\": true}}, {\"class_name\": \"MaxPooling2D\", \"config\": {\"name\": \"max_pooling2d_4\", \"trainable\": true, \"data_format\": \"channels_last\", \"pool_size\": [2, 2], \"padding\": \"valid\", \"strides\": [2, 2]}}, {\"class_name\": \"Dropout\", \"config\": {\"rate\": 0.3, \"noise_shape\": null, \"trainable\": true, \"seed\": null, \"name\": \"dropout_5\"}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"conv2d_9\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 128, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"BatchNormalization\", \"config\": {\"beta_constraint\": null, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"name\": \"batch_normalization_5\", \"epsilon\": 0.001, \"trainable\": true, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"scale\": true, \"axis\": -1, \"gamma_constraint\": null, \"gamma_regularizer\": null, \"beta_regularizer\": null, \"momentum\": 0.99, \"center\": true}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"conv2d_10\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 128, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"BatchNormalization\", \"config\": {\"beta_constraint\": null, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"name\": \"batch_normalization_6\", \"epsilon\": 0.001, \"trainable\": true, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"scale\": true, \"axis\": -1, \"gamma_constraint\": null, \"gamma_regularizer\": null, \"beta_regularizer\": null, \"momentum\": 0.99, \"center\": true}}, {\"class_name\": \"MaxPooling2D\", \"config\": {\"name\": \"max_pooling2d_5\", \"trainable\": true, \"data_format\": \"channels_last\", \"pool_size\": [2, 2], \"padding\": \"valid\", \"strides\": [2, 2]}}, {\"class_name\": \"Dropout\", \"config\": {\"rate\": 0.4, \"noise_shape\": null, \"trainable\": true, \"seed\": null, \"name\": \"dropout_6\"}}, {\"class_name\": \"Flatten\", \"config\": {\"trainable\": true, \"name\": \"flatten_2\", \"data_format\": \"channels_last\"}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"dense_3\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 128, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"BatchNormalization\", \"config\": {\"beta_constraint\": null, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"name\": \"batch_normalization_7\", \"epsilon\": 0.001, \"trainable\": true, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"scale\": true, \"axis\": -1, \"gamma_constraint\": null, \"gamma_regularizer\": null, \"beta_regularizer\": null, \"momentum\": 0.99, \"center\": true}}, {\"class_name\": \"Dropout\", \"config\": {\"rate\": 0.5, \"noise_shape\": null, \"trainable\": true, \"seed\": null, \"name\": \"dropout_7\"}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_4\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"softmax\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}], \"name\": \"sequential_3\"}, \"backend\": \"tensorflow\"}'"
+ ]
+ },
+ "execution_count": 28,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model2.to_json()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Another model architecture from https://machinelearningmastery.com/how-to-develop-a-cnn-from-scratch-for-cifar-10-photo-classification/"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "_________________________________________________________________\n",
+ "Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ "conv2d_11 (Conv2D) (None, 32, 32, 32) 896 \n",
+ "_________________________________________________________________\n",
+ "conv2d_12 (Conv2D) (None, 32, 32, 32) 9248 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_6 (MaxPooling2 (None, 16, 16, 32) 0 \n",
+ "_________________________________________________________________\n",
+ "dropout_8 (Dropout) (None, 16, 16, 32) 0 \n",
+ "_________________________________________________________________\n",
+ "conv2d_13 (Conv2D) (None, 16, 16, 64) 18496 \n",
+ "_________________________________________________________________\n",
+ "conv2d_14 (Conv2D) (None, 16, 16, 64) 36928 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_7 (MaxPooling2 (None, 8, 8, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "dropout_9 (Dropout) (None, 8, 8, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "conv2d_15 (Conv2D) (None, 8, 8, 128) 73856 \n",
+ "_________________________________________________________________\n",
+ "conv2d_16 (Conv2D) (None, 8, 8, 128) 147584 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_8 (MaxPooling2 (None, 4, 4, 128) 0 \n",
+ "_________________________________________________________________\n",
+ "dropout_10 (Dropout) (None, 4, 4, 128) 0 \n",
+ "_________________________________________________________________\n",
+ "flatten_3 (Flatten) (None, 2048) 0 \n",
+ "_________________________________________________________________\n",
+ "dense_5 (Dense) (None, 128) 262272 \n",
+ "_________________________________________________________________\n",
+ "dropout_11 (Dropout) (None, 128) 0 \n",
+ "_________________________________________________________________\n",
+ "dense_6 (Dense) (None, 10) 1290 \n",
+ "=================================================================\n",
+ "Total params: 550,570\n",
+ "Trainable params: 550,570\n",
+ "Non-trainable params: 0\n",
+ "_________________________________________________________________\n"
+ ]
+ }
+ ],
+ "source": [
+ "model3 = Sequential()\n",
+ "\n",
+ "model3.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same', input_shape=(32, 32, 3)))\n",
+ "model3.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
+ "model3.add(MaxPooling2D((2, 2)))\n",
+ "model3.add(Dropout(0.2))\n",
+ "model3.add(Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
+ "model3.add(Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
+ "model3.add(MaxPooling2D((2, 2)))\n",
+ "model3.add(Dropout(0.3))\n",
+ "model3.add(Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
+ "model3.add(Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
+ "model3.add(MaxPooling2D((2, 2)))\n",
+ "model3.add(Dropout(0.4))\n",
+ "model3.add(Flatten())\n",
+ "model3.add(Dense(128, activation='relu', kernel_initializer='he_uniform'))\n",
+ "model3.add(Dropout(0.5))\n",
+ "model3.add(Dense(10, activation='softmax'))\n",
+ "\n",
+ "model3.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'{\"class_name\": \"Sequential\", \"keras_version\": \"2.1.6\", \"config\": [{\"class_name\": \"Conv2D\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"conv2d_17\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"data_format\": \"channels_last\", \"filters\": 32, \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"batch_input_shape\": [null, 32, 32, 3], \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"conv2d_18\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 32, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"MaxPooling2D\", \"config\": {\"name\": \"max_pooling2d_9\", \"trainable\": true, \"data_format\": \"channels_last\", \"pool_size\": [2, 2], \"padding\": \"valid\", \"strides\": [2, 2]}}, {\"class_name\": \"Dropout\", \"config\": {\"rate\": 0.2, \"noise_shape\": null, \"trainable\": true, \"seed\": null, \"name\": \"dropout_12\"}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"conv2d_19\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 64, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"conv2d_20\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 64, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"MaxPooling2D\", \"config\": {\"name\": \"max_pooling2d_10\", \"trainable\": true, \"data_format\": \"channels_last\", \"pool_size\": [2, 2], \"padding\": \"valid\", \"strides\": [2, 2]}}, {\"class_name\": \"Dropout\", \"config\": {\"rate\": 0.3, \"noise_shape\": null, \"trainable\": true, \"seed\": null, \"name\": \"dropout_13\"}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"conv2d_21\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 128, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"conv2d_22\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 128, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"MaxPooling2D\", \"config\": {\"name\": \"max_pooling2d_11\", \"trainable\": true, \"data_format\": \"channels_last\", \"pool_size\": [2, 2], \"padding\": \"valid\", \"strides\": [2, 2]}}, {\"class_name\": \"Dropout\", \"config\": {\"rate\": 0.4, \"noise_shape\": null, \"trainable\": true, \"seed\": null, \"name\": \"dropout_14\"}}, {\"class_name\": \"Flatten\", \"config\": {\"trainable\": true, \"name\": \"flatten_4\", \"data_format\": \"channels_last\"}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"dense_7\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 128, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dropout\", \"config\": {\"rate\": 0.5, \"noise_shape\": null, \"trainable\": true, \"seed\": null, \"name\": \"dropout_15\"}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_8\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"softmax\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}], \"backend\": \"tensorflow\"}'"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model3.to_json()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Load into model architecture table using psycopg2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "Done.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 31,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "3 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>model_id</th>\n",
+ " <th>name</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>CNN from Keras docs for CIFAR-10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>CNN from Jason Brownlee blog post</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>CNN from Jason Brownlee blog post - no batch normalization</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(1, u'CNN from Keras docs for CIFAR-10'),\n",
+ " (2, u'CNN from Jason Brownlee blog post'),\n",
+ " (3, u'CNN from Jason Brownlee blog post - no batch normalization')]"
+ ]
+ },
+ "execution_count": 31,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import psycopg2 as p2\n",
+ "#conn = p2.connect('postgresql://gpadmin@35.239.240.26:5432/madlib')\n",
+ "#conn = p2.connect('postgresql://fmcquillan@localhost:5432/madlib')\n",
+ "conn = p2.connect('postgresql://gpadmin@localhost:8000/cifar_places')\n",
+ "cur = conn.cursor()\n",
+ "\n",
+ "%sql DROP TABLE IF EXISTS model_arch_library;\n",
+ "query = \"SELECT madlib.load_keras_model('model_arch_library', %s, NULL, %s)\"\n",
+ "cur.execute(query,[model1.to_json(), \"CNN from Keras docs for CIFAR-10\"])\n",
+ "conn.commit()\n",
+ "\n",
+ "query = \"SELECT madlib.load_keras_model('model_arch_library', %s, NULL, %s)\"\n",
+ "cur.execute(query,[model2.to_json(), \"CNN from Jason Brownlee blog post\"])\n",
+ "conn.commit()\n",
+ "\n",
+ "query = \"SELECT madlib.load_keras_model('model_arch_library', %s, NULL, %s)\"\n",
+ "cur.execute(query,[model3.to_json(), \"CNN from Jason Brownlee blog post - no batch normalization\"])\n",
+ "conn.commit()\n",
+ "\n",
+ "# check model loaded OK\n",
+ "%sql SELECT model_id, name FROM model_arch_library ORDER BY model_id;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"mst\"></a>\n",
+ "# 4. Define and load model selection tuples"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Select the model(s) from the model architecture table that you want to run, along with the compile and fit parameters. Permutations for grid search will be created for the set of model selection parameters will be loaded:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "Done.\n",
+ "1 rows affected.\n",
+ "16 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>mst_key</th>\n",
+ " <th>model_id</th>\n",
+ " <th>compile_params</th>\n",
+ " <th>fit_params</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='adam(lr=0.0001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=64,epochs=5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='adam(lr=0.0001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=128,epochs=5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='rmsprop(lr=0.0001, decay=1e-6)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=64,epochs=5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>4</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='rmsprop(lr=0.0001, decay=1e-6)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=128,epochs=5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>5</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='rmsprop(lr=0.001, decay=1e-6)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=64,epochs=5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>6</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='rmsprop(lr=0.001, decay=1e-6)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=128,epochs=5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>7</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='adam(lr=0.001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=64,epochs=5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>8</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='adam(lr=0.001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=128,epochs=5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>9</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='adam(lr=0.0001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=64,epochs=5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>10</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='adam(lr=0.0001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=128,epochs=5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>11</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='rmsprop(lr=0.0001, decay=1e-6)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=64,epochs=5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>12</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='rmsprop(lr=0.0001, decay=1e-6)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=128,epochs=5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>13</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='rmsprop(lr=0.001, decay=1e-6)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=64,epochs=5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>14</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='rmsprop(lr=0.001, decay=1e-6)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=128,epochs=5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>15</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='adam(lr=0.001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=64,epochs=5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>16</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='adam(lr=0.001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=128,epochs=5</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(1, 1, u\"loss='categorical_crossentropy',optimizer='adam(lr=0.0001)',metrics=['accuracy']\", u'batch_size=64,epochs=5'),\n",
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+ " (12, 2, u\"loss='categorical_crossentropy',optimizer='rmsprop(lr=0.0001, decay=1e-6)',metrics=['accuracy']\", u'batch_size=128,epochs=5'),\n",
+ " (13, 2, u\"loss='categorical_crossentropy',optimizer='rmsprop(lr=0.001, decay=1e-6)',metrics=['accuracy']\", u'batch_size=64,epochs=5'),\n",
+ " (14, 2, u\"loss='categorical_crossentropy',optimizer='rmsprop(lr=0.001, decay=1e-6)',metrics=['accuracy']\", u'batch_size=128,epochs=5'),\n",
+ " (15, 2, u\"loss='categorical_crossentropy',optimizer='adam(lr=0.001)',metrics=['accuracy']\", u'batch_size=64,epochs=5'),\n",
+ " (16, 2, u\"loss='categorical_crossentropy',optimizer='adam(lr=0.001)',metrics=['accuracy']\", u'batch_size=128,epochs=5')]"
+ ]
+ },
+ "execution_count": 32,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS mst_table, mst_table_summary;\n",
+ "\n",
+ "SELECT madlib.load_model_selection_table('model_arch_library', -- model architecture table\n",
+ " 'mst_table', -- model selection table output\n",
+ " ARRAY[1,2], -- model ids from model architecture table\n",
+ " ARRAY[ -- compile params \n",
+ " $$loss='categorical_crossentropy',optimizer='rmsprop(lr=0.0001, decay=1e-6)',metrics=['accuracy']$$,\n",
+ " $$loss='categorical_crossentropy',optimizer='rmsprop(lr=0.001, decay=1e-6)',metrics=['accuracy']$$,\n",
+ " $$loss='categorical_crossentropy',optimizer='adam(lr=0.0001)',metrics=['accuracy']$$,\n",
+ " $$loss='categorical_crossentropy',optimizer='adam(lr=0.001)',metrics=['accuracy']$$\n",
+ " ],\n",
+ " ARRAY[ -- fit params\n",
+ " $$batch_size=64,epochs=5$$, \n",
+ " $$batch_size=128,epochs=5$$\n",
+ " ]\n",
+ " );\n",
+ " \n",
+ "SELECT * FROM mst_table ORDER BY mst_key;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This is the name of the model architecture table that corresponds to the model selection table:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>model_arch_table</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>model_arch_library</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'model_arch_library',)]"
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT * FROM mst_table_summary;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"train\"></a>\n",
+ "# 5. Train\n",
+ "Train multiple models:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>madlib_keras_fit_multiple_model</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td></td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[('',)]"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS cifar10_multi_model, cifar10_multi_model_summary, cifar10_multi_model_info;\n",
+ "\n",
+ "SELECT madlib.madlib_keras_fit_multiple_model('cifar10_train_packed', -- source_table\n",
+ " 'cifar10_multi_model', -- model_output_table\n",
+ " 'mst_table', -- model_selection_table\n",
+ " 10, -- num_iterations\n",
+ " TRUE, -- use gpus\n",
+ " 'cifar10_val_packed', -- validation dataset\n",
+ " 1 -- metrics compute frequency\n",
+ " );"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "View the model summary:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>source_table</th>\n",
+ " <th>validation_table</th>\n",
+ " <th>model</th>\n",
+ " <th>model_info</th>\n",
+ " <th>dependent_varname</th>\n",
+ " <th>independent_varname</th>\n",
+ " <th>model_arch_table</th>\n",
+ " <th>num_iterations</th>\n",
+ " <th>metrics_compute_frequency</th>\n",
+ " <th>warm_start</th>\n",
+ " <th>name</th>\n",
+ " <th>description</th>\n",
+ " <th>start_training_time</th>\n",
+ " <th>end_training_time</th>\n",
+ " <th>madlib_version</th>\n",
+ " <th>num_classes</th>\n",
+ " <th>class_values</th>\n",
+ " <th>dependent_vartype</th>\n",
+ " <th>normalizing_const</th>\n",
+ " <th>metrics_iters</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>cifar10_train_packed</td>\n",
+ " <td>cifar10_val_packed</td>\n",
+ " <td>cifar10_multi_model</td>\n",
+ " <td>cifar10_multi_model_info</td>\n",
+ " <td>y</td>\n",
+ " <td>x</td>\n",
+ " <td>model_arch_library</td>\n",
+ " <td>10</td>\n",
+ " <td>1</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>2020-03-17 23:21:35.497938</td>\n",
+ " <td>2020-03-17 23:50:01.109448</td>\n",
+ " <td>1.17-dev</td>\n",
+ " <td>10</td>\n",
+ " <td>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]</td>\n",
+ " <td>smallint</td>\n",
+ " <td>256.0</td>\n",
+ " <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'cifar10_train_packed', u'cifar10_val_packed', u'cifar10_multi_model', u'cifar10_multi_model_info', u'y', u'x', u'model_arch_library', 10, 1, False, None, None, datetime.datetime(2020, 3, 17, 23, 21, 35, 497938), datetime.datetime(2020, 3, 17, 23, 50, 1, 109448), u'1.17-dev', 10, [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], u'smallint', 256.0, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10])]"
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT * FROM cifar10_multi_model_summary;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "View performance of each model:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "16 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>mst_key</th>\n",
+ " <th>model_id</th>\n",
+ " <th>compile_params</th>\n",
+ " <th>fit_params</th>\n",
+ " <th>model_type</th>\n",
+ " <th>model_size</th>\n",
+ " <th>metrics_elapsed_time</th>\n",
+ " <th>metrics_type</th>\n",
+ " <th>training_metrics_final</th>\n",
+ " <th>training_loss_final</th>\n",
+ " <th>training_metrics</th>\n",
+ " <th>training_loss</th>\n",
+ " <th>validation_metrics_final</th>\n",
+ " <th>validation_loss_final</th>\n",
+ " <th>validation_metrics</th>\n",
+ " <th>validation_loss</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>15</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='adam(lr=0.001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=64,epochs=5</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>2159.70019531</td>\n",
+ " <td>[160.21645283699, 325.693609952927, 493.035051822662, 661.953631877899, 831.346253871918, 1001.65144181252, 1171.72806191444, 1342.43474984169, 1515.81184601784, 1689.71926879883]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.958739995956</td>\n",
+ " <td>0.12002915144</td>\n",
+ " <td>[0.779980003833771, 0.845019996166229, 0.873939990997314, 0.893540024757385, 0.905359983444214, 0.92519998550415, 0.942740023136139, 0.936339974403381, 0.944379985332489, 0.958739995956421]</td>\n",
+ " <td>[0.63620263338089, 0.451914638280869, 0.365966022014618, 0.304257303476334, 0.272642701864243, 0.214935272932053, 0.167475894093513, 0.184087827801704, 0.162149116396904, 0.120029151439667]</td>\n",
+ " <td>0.838599979877</td>\n",
+ " <td>0.570000112057</td>\n",
+ " <td>[0.749100029468536, 0.795099973678589, 0.809499979019165, 0.814599990844727, 0.817300021648407, 0.825900018215179, 0.831399977207184, 0.829599976539612, 0.826099991798401, 0.838599979877472]</td>\n",
+ " <td>[0.729304790496826, 0.612815201282501, 0.598590016365051, 0.574969530105591, 0.585948467254639, 0.566278994083405, 0.566746890544891, 0.570696115493774, 0.600630104541779, 0.570000112056732]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>16</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='adam(lr=0.001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=128,epochs=5</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>2159.70019531</td>\n",
+ " <td>[168.006911993027, 334.143662929535, 501.23655295372, 670.235726833344, 840.096035003662, 1010.38422298431, 1180.51074194908, 1351.65223288536, 1524.53146886826, 1699.84652090073]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.959839999676</td>\n",
+ " <td>0.119847580791</td>\n",
+ " <td>[0.763499975204468, 0.829859972000122, 0.858560025691986, 0.889379978179932, 0.903039991855621, 0.922519981861115, 0.938279986381531, 0.941100001335144, 0.953220009803772, 0.959839999675751]</td>\n",
+ " <td>[0.676432132720947, 0.491187304258347, 0.408329516649246, 0.319939345121384, 0.279028236865997, 0.222155645489693, 0.179503843188286, 0.17168553173542, 0.136883869767189, 0.11984758079052]</td>\n",
+ " <td>0.833100020885</td>\n",
+ " <td>0.599731981754</td>\n",
+ " <td>[0.741900026798248, 0.784900009632111, 0.796500027179718, 0.814000010490417, 0.820100009441376, 0.828100025653839, 0.83160001039505, 0.831499993801117, 0.834399998188019, 0.833100020885468]</td>\n",
+ " <td>[0.74375057220459, 0.641318619251251, 0.627871870994568, 0.585915207862854, 0.588144600391388, 0.569212675094604, 0.577586710453033, 0.590799033641815, 0.581186473369598, 0.599731981754303]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>13</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='rmsprop(lr=0.001, decay=1e-6)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=64,epochs=5</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>2159.70019531</td>\n",
+ " <td>[165.07025885582, 331.04939699173, 498.211872816086, 667.060778856277, 836.708249807358, 1007.20118093491, 1176.76868081093, 1348.24542784691, 1520.97673892975, 1695.76891899109]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.94892001152</td>\n",
+ " <td>0.146594136953</td>\n",
+ " <td>[0.786400020122528, 0.839680016040802, 0.869140028953552, 0.878480017185211, 0.909940004348755, 0.913100004196167, 0.934099972248077, 0.94021999835968, 0.936819970607758, 0.948920011520386]</td>\n",
+ " <td>[0.625118017196655, 0.466760665178299, 0.38435173034668, 0.353523939847946, 0.260264813899994, 0.252679228782654, 0.190901413559914, 0.176555588841438, 0.181787580251694, 0.146594136953354]</td>\n",
+ " <td>0.82959997654</td>\n",
+ " <td>0.581429600716</td>\n",
+ " <td>[0.76120001077652, 0.787599980831146, 0.804199993610382, 0.805400013923645, 0.815400004386902, 0.824199974536896, 0.827499985694885, 0.826099991798401, 0.822399973869324, 0.829599976539612]</td>\n",
+ " <td>[0.711538255214691, 0.647833049297333, 0.601243674755096, 0.620489895343781, 0.593936264514923, 0.592362821102142, 0.572449862957001, 0.586679399013519, 0.630510628223419, 0.581429600715637]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>14</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='rmsprop(lr=0.001, decay=1e-6)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=128,epochs=5</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>2159.70019531</td>\n",
+ " <td>[161.95653796196, 327.381590843201, 494.75689291954, 663.697657823563, 833.140888929367, 1003.58734488487, 1173.66367697716, 1344.3388338089, 1517.75524687767, 1691.74780988693]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.942839980125</td>\n",
+ " <td>0.167295366526</td>\n",
+ " <td>[0.770560026168823, 0.822459995746613, 0.871060013771057, 0.886059999465942, 0.906120002269745, 0.91347998380661, 0.916339993476868, 0.929560005664825, 0.938979983329773, 0.942839980125427]</td>\n",
+ " <td>[0.656668424606323, 0.515599489212036, 0.367846250534058, 0.332457065582275, 0.276119023561478, 0.253687649965286, 0.2399021089077, 0.203119158744812, 0.174109742045403, 0.16729536652565]</td>\n",
+ " <td>0.827600002289</td>\n",
+ " <td>0.60536968708</td>\n",
+ " <td>[0.734799981117249, 0.784600019454956, 0.806299984455109, 0.811600029468536, 0.817499995231628, 0.823800027370453, 0.828299999237061, 0.829900026321411, 0.828999996185303, 0.827600002288818]</td>\n",
+ " <td>[0.777421414852142, 0.645794928073883, 0.587472915649414, 0.603748321533203, 0.590349853038788, 0.586721003055573, 0.58501935005188, 0.603322744369507, 0.596234917640686, 0.605369687080383]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>11</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='rmsprop(lr=0.0001, decay=1e-6)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=64,epochs=5</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>2159.70019531</td>\n",
+ " <td>[173.035629987717, 339.004810810089, 506.329674959183, 675.320897817612, 845.306622982025, 1015.91489100456, 1185.85607385635, 1357.71336293221, 1530.14687585831, 1705.61137890816]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.892719984055</td>\n",
+ " <td>0.309859842062</td>\n",
+ " <td>[0.583959996700287, 0.695259988307953, 0.754980027675629, 0.787720024585724, 0.814040005207062, 0.835359990596771, 0.856899976730347, 0.871460020542145, 0.884779989719391, 0.892719984054565]</td>\n",
+ " <td>[1.16789627075195, 0.861167967319489, 0.705251038074493, 0.607605278491974, 0.531997323036194, 0.470408618450165, 0.413226217031479, 0.370105147361755, 0.334705889225006, 0.309859842061996]</td>\n",
+ " <td>0.813000023365</td>\n",
+ " <td>0.566866695881</td>\n",
+ " <td>[0.579999983310699, 0.690999984741211, 0.738099992275238, 0.763599991798401, 0.773800015449524, 0.783800005912781, 0.798500001430511, 0.802699983119965, 0.80620002746582, 0.813000023365021]</td>\n",
+ " <td>[1.17161071300507, 0.881313383579254, 0.754627048969269, 0.680095791816711, 0.643820106983185, 0.620280385017395, 0.590712904930115, 0.576853334903717, 0.574894845485687, 0.56686669588089]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>9</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='adam(lr=0.0001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=64,epochs=5</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>2159.70019531</td>\n",
+ " <td>[156.6842648983, 322.059647798538, 489.514621019363, 658.153139829636, 827.505573987961, 997.966463804245, 1168.05154681206, 1338.58688092232, 1511.8177728653, 1685.66397881508]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.89484000206</td>\n",
+ " <td>0.305973917246</td>\n",
+ " <td>[0.607659995555878, 0.689279973506927, 0.748199999332428, 0.779420018196106, 0.812300026416779, 0.830940008163452, 0.854920029640198, 0.868260025978088, 0.885720014572144, 0.894840002059937]</td>\n",
+ " <td>[1.11698472499847, 0.874710261821747, 0.711394190788269, 0.626727402210236, 0.533221900463104, 0.478961884975433, 0.414784848690033, 0.376370459794998, 0.330575972795486, 0.305973917245865]</td>\n",
+ " <td>0.81099998951</td>\n",
+ " <td>0.550887346268</td>\n",
+ " <td>[0.602699995040894, 0.681299984455109, 0.728600025177002, 0.754400014877319, 0.774600028991699, 0.784300029277802, 0.795799970626831, 0.80129998922348, 0.807399988174438, 0.810999989509583]</td>\n",
+ " <td>[1.11438655853271, 0.900401651859283, 0.765824854373932, 0.704216003417969, 0.643769145011902, 0.616570711135864, 0.586219370365143, 0.571570515632629, 0.558478593826294, 0.5508873462677]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>10</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='adam(lr=0.0001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=128,epochs=5</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>2159.70019531</td>\n",
+ " <td>[158.506701946259, 323.779083013535, 491.302199840546, 660.148201942444, 829.340363025665, 999.844955921173, 1169.83032798767, 1340.5411260128, 1513.8498609066, 1687.69545793533]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.867579996586</td>\n",
+ " <td>0.380911707878</td>\n",
+ " <td>[0.576640009880066, 0.66431999206543, 0.707199990749359, 0.749520003795624, 0.778980016708374, 0.799520015716553, 0.820659995079041, 0.839940011501312, 0.854659974575043, 0.867579996585846]</td>\n",
+ " <td>[1.20035827159882, 0.945839285850525, 0.823047578334808, 0.703792750835419, 0.624295234680176, 0.566677749156952, 0.511338114738464, 0.459649682044983, 0.418204575777054, 0.380911707878113]</td>\n",
+ " <td>0.799700021744</td>\n",
+ " <td>0.571797966957</td>\n",
+ " <td>[0.572700023651123, 0.655099987983704, 0.69980001449585, 0.731299996376038, 0.756399989128113, 0.771399974822998, 0.778999984264374, 0.791599988937378, 0.798200011253357, 0.799700021743774]</td>\n",
+ " <td>[1.20565474033356, 0.964107036590576, 0.849484860897064, 0.752416431903839, 0.691979646682739, 0.65268349647522, 0.628291726112366, 0.599337160587311, 0.586054861545563, 0.571797966957092]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>12</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='rmsprop(lr=0.0001, decay=1e-6)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=128,epochs=5</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>2159.70019531</td>\n",
+ " <td>[170.873965024948, 337.211632966995, 504.536657810211, 673.510901927948, 843.392476797104, 1014.04761791229, 1183.94673490524, 1355.76256680489, 1528.15198993683, 1703.5478978157]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.856419980526</td>\n",
+ " <td>0.410014539957</td>\n",
+ " <td>[0.547559976577759, 0.647800028324127, 0.698180019855499, 0.746360003948212, 0.769439995288849, 0.794420003890991, 0.814459979534149, 0.829039990901947, 0.850260019302368, 0.85641998052597]</td>\n",
+ " <td>[1.27583348751068, 1.00206804275513, 0.853795170783997, 0.720450162887573, 0.650525093078613, 0.582838296890259, 0.527670443058014, 0.484861791133881, 0.428409487009048, 0.410014539957047]</td>\n",
+ " <td>0.798799991608</td>\n",
+ " <td>0.597697675228</td>\n",
+ " <td>[0.546700000762939, 0.638999998569489, 0.690900027751923, 0.731299996376038, 0.745500028133392, 0.763100028038025, 0.776300013065338, 0.78549998998642, 0.795000016689301, 0.798799991607666]</td>\n",
+ " <td>[1.27788388729095, 1.02294909954071, 0.881045579910278, 0.773496091365814, 0.725675582885742, 0.681352615356445, 0.649362862110138, 0.62593412399292, 0.598857045173645, 0.597697675228119]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>8</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='adam(lr=0.001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=128,epochs=5</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>4886.20019531</td>\n",
+ " <td>[165.667771816254, 331.63804101944, 498.80203294754, 667.662895917892, 837.58491396904, 1007.91998696327, 1177.68679785728, 1348.86184287071, 1521.96094989777, 1696.78608179092]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.909500002861</td>\n",
+ " <td>0.272008836269</td>\n",
+ " <td>[0.752040028572083, 0.828859984874725, 0.851639986038208, 0.888540029525757, 0.888599991798401, 0.894879996776581, 0.903320014476776, 0.910380005836487, 0.879000008106232, 0.909500002861023]</td>\n",
+ " <td>[0.712807893753052, 0.499261021614075, 0.437727391719818, 0.330645024776459, 0.325151771306992, 0.308288186788559, 0.296011507511139, 0.272213906049728, 0.394761264324188, 0.272008836269379]</td>\n",
+ " <td>0.779699981213</td>\n",
+ " <td>0.834259152412</td>\n",
+ " <td>[0.708500027656555, 0.751299977302551, 0.765500009059906, 0.772300004959106, 0.777100026607513, 0.783599972724915, 0.777999997138977, 0.784099996089935, 0.769800007343292, 0.779699981212616]</td>\n",
+ " <td>[0.841326177120209, 0.729629755020142, 0.776288628578186, 0.718003392219543, 0.76948493719101, 0.767067849636078, 0.827211439609528, 0.747310042381287, 0.895535230636597, 0.834259152412415]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='adam(lr=0.0001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=64,epochs=5</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>4886.20019531</td>\n",
+ " <td>[168.611300945282, 334.899528980255, 502.037551879883, 671.096584796906, 840.693498849869, 1011.29117488861, 1181.11248087883, 1352.68110394478, 1525.13323402405, 1700.88493180275]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.920239984989</td>\n",
+ " <td>0.272924244404</td>\n",
+ " <td>[0.584800004959106, 0.682219982147217, 0.732940018177032, 0.774779975414276, 0.806119978427887, 0.837840020656586, 0.862119972705841, 0.883340001106262, 0.90311998128891, 0.920239984989166]</td>\n",
+ " <td>[1.17424917221069, 0.919099688529968, 0.776308834552765, 0.661072790622711, 0.573073744773865, 0.487901866436005, 0.420156627893448, 0.368478238582611, 0.319370418787003, 0.272924244403839]</td>\n",
+ " <td>0.775600016117</td>\n",
+ " <td>0.679777920246</td>\n",
+ " <td>[0.576799988746643, 0.657599985599518, 0.694700002670288, 0.721199989318848, 0.741400003433228, 0.750500023365021, 0.758800029754639, 0.764599978923798, 0.771899998188019, 0.775600016117096]</td>\n",
+ " <td>[1.19727396965027, 0.98458856344223, 0.873661160469055, 0.809533834457397, 0.764622151851654, 0.735906422138214, 0.700538396835327, 0.705035388469696, 0.696578562259674, 0.679777920246124]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>7</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='adam(lr=0.001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=64,epochs=5</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>4886.20019531</td>\n",
+ " <td>[154.371929883957, 319.090498924255, 486.952117919922, 655.78808093071, 824.799381971359, 995.210795879364, 1165.24253582954, 1336.07785487175, 1508.86772489548, 1682.56205582619]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.861419975758</td>\n",
+ " <td>0.432331353426</td>\n",
+ " <td>[0.766240000724792, 0.827859997749329, 0.855539977550507, 0.868900001049042, 0.871540009975433, 0.88238000869751, 0.873179972171783, 0.878740012645721, 0.873459994792938, 0.861419975757599]</td>\n",
+ " <td>[0.682850182056427, 0.503075182437897, 0.426931649446487, 0.385531783103943, 0.383735597133636, 0.371502071619034, 0.389450550079346, 0.369848489761353, 0.391900181770325, 0.43233135342598]</td>\n",
+ " <td>0.767599999905</td>\n",
+ " <td>0.752326309681</td>\n",
+ " <td>[0.719299972057343, 0.751500010490417, 0.765399992465973, 0.769999980926514, 0.772499978542328, 0.776799976825714, 0.770099997520447, 0.77649998664856, 0.76800000667572, 0.767599999904633]</td>\n",
+ " <td>[0.819014072418213, 0.739358127117157, 0.74931389093399, 0.773028433322906, 0.763317465782166, 0.729169189929962, 0.805014729499817, 0.808122754096985, 0.812756359577179, 0.752326309680939]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='rmsprop(lr=0.0001, decay=1e-6)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=64,epochs=5</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>4886.20019531</td>\n",
+ " <td>[154.969959974289, 319.906549930573, 487.758535861969, 656.377619981766, 825.622062921524, 996.102727890015, 1166.17477893829, 1336.67210102081, 1509.83961796761, 1683.64339399338]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.821099996567</td>\n",
+ " <td>0.525239348412</td>\n",
+ " <td>[0.561699986457825, 0.668940007686615, 0.717679977416992, 0.75297999382019, 0.767099976539612, 0.795260012149811, 0.807739973068237, 0.811280012130737, 0.826099991798401, 0.821099996566772]</td>\n",
+ " <td>[1.220210313797, 0.947799980640411, 0.812605798244476, 0.718978762626648, 0.679905712604523, 0.613099038600922, 0.566433131694794, 0.552485108375549, 0.512454450130463, 0.52523934841156]</td>\n",
+ " <td>0.758599996567</td>\n",
+ " <td>0.737899065018</td>\n",
+ " <td>[0.559899985790253, 0.647000014781952, 0.683300018310547, 0.709100008010864, 0.723800003528595, 0.742299973964691, 0.749700009822845, 0.757099986076355, 0.765600025653839, 0.758599996566772]</td>\n",
+ " <td>[1.22284317016602, 0.988233506679535, 0.88149094581604, 0.825053095817566, 0.811959385871887, 0.752561450004578, 0.741220593452454, 0.739940404891968, 0.711078464984894, 0.7378990650177]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='adam(lr=0.0001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=128,epochs=5</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>4886.20019531</td>\n",
+ " <td>[169.201556921005, 335.50149679184, 502.804733991623, 671.735637903214, 841.536010980606, 1012.17449593544, 1182.0387070179, 1353.62765884399, 1526.14807486534, 1701.52705287933]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.866079986095</td>\n",
+ " <td>0.411734908819</td>\n",
+ " <td>[0.535120010375977, 0.633019983768463, 0.683080017566681, 0.727400004863739, 0.757719993591309, 0.788060009479523, 0.812640011310577, 0.835120022296906, 0.846180021762848, 0.866079986095428]</td>\n",
+ " <td>[1.29918956756592, 1.05417799949646, 0.917978286743164, 0.795661866664886, 0.70832484960556, 0.632884621620178, 0.562899112701416, 0.502775311470032, 0.463661164045334, 0.411734908819199]</td>\n",
+ " <td>0.758199989796</td>\n",
+ " <td>0.725014865398</td>\n",
+ " <td>[0.527800023555756, 0.620299994945526, 0.656300008296967, 0.690400004386902, 0.707400023937225, 0.726199984550476, 0.734799981117249, 0.746500015258789, 0.750999987125397, 0.758199989795685]</td>\n",
+ " <td>[1.31133198738098, 1.09069919586182, 0.985389471054077, 0.897808969020844, 0.84535801410675, 0.804515540599823, 0.770778298377991, 0.748202204704285, 0.736022055149078, 0.725014865398407]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>4</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='rmsprop(lr=0.0001, decay=1e-6)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=128,epochs=5</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>4886.20019531</td>\n",
+ " <td>[163.359121799469, 328.837852954865, 496.364542961121, 665.302300930023, 834.891145944595, 1005.38074088097, 1174.84563589096, 1345.91664791107, 1518.93280696869, 1693.73289394379]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.82415997982</td>\n",
+ " <td>0.518441617489</td>\n",
+ " <td>[0.521520018577576, 0.612500011920929, 0.638840019702911, 0.686819970607758, 0.740119993686676, 0.765739977359772, 0.78847998380661, 0.814140021800995, 0.825020015239716, 0.824159979820251]</td>\n",
+ " <td>[1.33973300457001, 1.10515522956848, 1.02763831615448, 0.890439391136169, 0.749050080776215, 0.679889440536499, 0.620155572891235, 0.560859560966492, 0.520996809005737, 0.518441617488861]</td>\n",
+ " <td>0.757799983025</td>\n",
+ " <td>0.732741773129</td>\n",
+ " <td>[0.515500009059906, 0.602699995040894, 0.630599975585938, 0.66619998216629, 0.70959997177124, 0.721300005912781, 0.73470002412796, 0.751500010490417, 0.756099998950958, 0.757799983024597]</td>\n",
+ " <td>[1.34494018554688, 1.12949633598328, 1.05980122089386, 0.951755106449127, 0.840038895606995, 0.81215900182724, 0.776918768882751, 0.734996318817139, 0.712943911552429, 0.73274177312851]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>6</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='rmsprop(lr=0.001, decay=1e-6)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=128,epochs=5</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>4886.20019531</td>\n",
+ " <td>[162.552975893021, 328.213756799698, 495.55163693428, 664.478772878647, 834.023772001266, 1004.50640487671, 1174.26192784309, 1344.94176697731, 1518.35087895393, 1692.74292802811]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.734799981117</td>\n",
+ " <td>0.835899949074</td>\n",
+ " <td>[0.71916002035141, 0.750440001487732, 0.720459997653961, 0.801540017127991, 0.725839972496033, 0.777220010757446, 0.780219972133636, 0.706719994544983, 0.750180006027222, 0.734799981117249]</td>\n",
+ " <td>[0.821036815643311, 0.778892278671265, 0.90295821428299, 0.621506929397583, 0.841251850128174, 0.677752196788788, 0.701953232288361, 0.95904815196991, 0.82386702299118, 0.835899949073792]</td>\n",
+ " <td>0.709599971771</td>\n",
+ " <td>0.930533230305</td>\n",
+ " <td>[0.680499970912933, 0.7185999751091, 0.670499980449677, 0.75220000743866, 0.685699999332428, 0.733200013637543, 0.733200013637543, 0.669700026512146, 0.712100028991699, 0.70959997177124]</td>\n",
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+ " </tr>\n",
+ " <tr>\n",
+ " <td>5</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='rmsprop(lr=0.001, decay=1e-6)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=64,epochs=5</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>4886.20019531</td>\n",
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+ " <td>[u'accuracy']</td>\n",
+ " <td>0.585380017757</td>\n",
+ " <td>1.2495957613</td>\n",
+ " <td>[0.71341997385025, 0.755879998207092, 0.7189000248909, 0.719219982624054, 0.683700025081635, 0.646440029144287, 0.565659999847412, 0.623939990997314, 0.66210001707077, 0.585380017757416]</td>\n",
+ " <td>[0.875595450401306, 0.737006664276123, 0.879582762718201, 0.826622664928436, 0.986110627651215, 1.02004647254944, 1.33022010326385, 1.12789785861969, 0.977221727371216, 1.24959576129913]</td>\n",
+ " <td>0.591700017452</td>\n",
+ " <td>1.32029783726</td>\n",
+ " <td>[0.690599977970123, 0.721400022506714, 0.707000017166138, 0.700900018215179, 0.673799991607666, 0.638000011444092, 0.565599977970123, 0.620199978351593, 0.65719997882843, 0.59170001745224]</td>\n",
+ " <td>[0.990004479885101, 0.883562862873077, 0.931297481060028, 0.941763758659363, 1.02153491973877, 1.06588494777679, 1.32568216323853, 1.17570757865906, 1.0156409740448, 1.32029783725739]</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
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+ " (3, 1, u\"loss='categorical_crossentropy',optimizer='rmsprop(lr=0.0001, decay=1e-6)',metrics=['accuracy']\", u'batch_size=64,epochs=5', u'madlib_keras', 4886.20019531, [154.969959974289, 319.906549930573, 487.758535861969, 656.377619981766, 825.622062921524, 996.102727890015, 1166.17477893829, 1336.67210102081, 1509.83961796761, 1683.64339399338], [u'accuracy'], 0.821099996567, 0.525239348412, [0.561699986457825, 0.668940007686615, 0.717679977416992, 0.75297999382019, 0.767099976539612, 0.795260012149811, 0.807739973068237, 0.811280012130737, 0.826099991798401, 0.821099996566772], [1.220210313797, 0.947799980640411, 0.812605798244476, 0.718978762626648, 0.679905712604523, 0.613099038600922, 0.566433131694794, 0.552485108375549, 0.512454450130463, 0.52523934841156], 0.758599996567, 0.737899065018, [0.559899985790253, 0.647000014781952, 0.683300018310547, 0.709100008010864, 0.723800003528595, 0.742299973964691, 0.749700009822845, 0.757099986076355, 0.765600025653839, 0.758599996566772], [1.22284317016602, 0.988233506679535, 0.88149094581604, 0.825053095817566, 0.811959385871887, 0.752561450004578, 0.741220593452454, 0.739940404891968, 0.711078464984894, 0.7378990650177]),\n",
+ " (2, 1, u\"loss='categorical_crossentropy',optimizer='adam(lr=0.0001)',metrics=['accuracy']\", u'batch_size=128,epochs=5', u'madlib_keras', 4886.20019531, [169.201556921005, 335.50149679184, 502.804733991623, 671.735637903214, 841.536010980606, 1012.17449593544, 1182.0387070179, 1353.62765884399, 1526.14807486534, 1701.52705287933], [u'accuracy'], 0.866079986095, 0.411734908819, [0.535120010375977, 0.633019983768463, 0.683080017566681, 0.727400004863739, 0.757719993591309, 0.788060009479523, 0.812640011310577, 0.835120022296906, 0.846180021762848, 0.866079986095428], [1.29918956756592, 1.05417799949646, 0.917978286743164, 0.795661866664886, 0.70832484960556, 0.632884621620178, 0.562899112701416, 0.502775311470032, 0.463661164045334, 0.411734908819199], 0.758199989796, 0.725014865398, [0.527800023555756, 0.620299994945526, 0.656300008296967, 0.690400004386902, 0.707400023937225, 0.726199984550476, 0.734799981117249, 0.746500015258789, 0.750999987125397, 0.758199989795685], [1.31133198738098, 1.09069919586182, 0.985389471054077, 0.897808969020844, 0.84535801410675, 0.804515540599823, 0.770778298377991, 0.748202204704285, 0.736022055149078, 0.725014865398407]),\n",
+ " (4, 1, u\"loss='categorical_crossentropy',optimizer='rmsprop(lr=0.0001, decay=1e-6)',metrics=['accuracy']\", u'batch_size=128,epochs=5', u'madlib_keras', 4886.20019531, [163.359121799469, 328.837852954865, 496.364542961121, 665.302300930023, 834.891145944595, 1005.38074088097, 1174.84563589096, 1345.91664791107, 1518.93280696869, 1693.73289394379], [u'accuracy'], 0.82415997982, 0.518441617489, [0.521520018577576, 0.612500011920929, 0.638840019702911, 0.686819970607758, 0.740119993686676, 0.765739977359772, 0.78847998380661, 0.814140021800995, 0.825020015239716, 0.824159979820251], [1.33973300457001, 1.10515522956848, 1.02763831615448, 0.890439391136169, 0.749050080776215, 0.679889440536499, 0.620155572891235, 0.560859560966492, 0.520996809005737, 0.518441617488861], 0.757799983025, 0.732741773129, [0.515500009059906, 0.602699995040894, 0.630599975585938, 0.66619998216629, 0.70959997177124, 0.721300005912781, 0.73470002412796, 0.751500010490417, 0.756099998950958, 0.757799983024597], [1.34494018554688, 1.12949633598328, 1.05980122089386, 0.951755106449127, 0.840038895606995, 0.81215900182724, 0.776918768882751, 0.734996318817139, 0.712943911552429, 0.73274177312851]),\n",
+ " (6, 1, u\"loss='categorical_crossentropy',optimizer='rmsprop(lr=0.001, decay=1e-6)',metrics=['accuracy']\", u'batch_size=128,epochs=5', u'madlib_keras', 4886.20019531, [162.552975893021, 328.213756799698, 495.55163693428, 664.478772878647, 834.023772001266, 1004.50640487671, 1174.26192784309, 1344.94176697731, 1518.35087895393, 1692.74292802811], [u'accuracy'], 0.734799981117, 0.835899949074, [0.71916002035141, 0.750440001487732, 0.720459997653961, 0.801540017127991, 0.725839972496033, 0.777220010757446, 0.780219972133636, 0.706719994544983, 0.750180006027222, 0.734799981117249], [0.821036815643311, 0.778892278671265, 0.90295821428299, 0.621506929397583, 0.841251850128174, 0.677752196788788, 0.701953232288361, 0.95904815196991, 0.82386702299118, 0.835899949073792], 0.709599971771, 0.930533230305, [0.680499970912933, 0.7185999751091, 0.670499980449677, 0.75220000743866, 0.685699999332428, 0.733200013637543, 0.733200013637543, 0.669700026512146, 0.712100028991699, 0.70959997177124], [0.936905562877655, 0.941630959510803, 1.1544371843338, 0.890039265155792, 1.02143895626068, 0.915954768657684, 0.975173354148865, 1.12838399410248, 0.942200124263763, 0.930533230304718]),\n",
+ " (5, 1, u\"loss='categorical_crossentropy',optimizer='rmsprop(lr=0.001, decay=1e-6)',metrics=['accuracy']\", u'batch_size=64,epochs=5', u'madlib_keras', 4886.20019531, [166.255312919617, 332.407908916473, 499.474656820297, 668.450613975525, 838.271963834763, 1008.50526785851, 1178.62056994438, 1349.74808692932, 1522.54409790039, 1697.81855082512], [u'accuracy'], 0.585380017757, 1.2495957613, [0.71341997385025, 0.755879998207092, 0.7189000248909, 0.719219982624054, 0.683700025081635, 0.646440029144287, 0.565659999847412, 0.623939990997314, 0.66210001707077, 0.585380017757416], [0.875595450401306, 0.737006664276123, 0.879582762718201, 0.826622664928436, 0.986110627651215, 1.02004647254944, 1.33022010326385, 1.12789785861969, 0.977221727371216, 1.24959576129913], 0.591700017452, 1.32029783726, [0.690599977970123, 0.721400022506714, 0.707000017166138, 0.700900018215179, 0.673799991607666, 0.638000011444092, 0.565599977970123, 0.620199978351593, 0.65719997882843, 0.59170001745224], [0.990004479885101, 0.883562862873077, 0.931297481060028, 0.941763758659363, 1.02153491973877, 1.06588494777679, 1.32568216323853, 1.17570757865906, 1.0156409740448, 1.32029783725739])]"
+ ]
+ },
+ "execution_count": 37,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT * FROM cifar10_multi_model_info ORDER BY validation_metrics_final DESC;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"plot\"></a>\n",
+ "# 6. Plot results"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib notebook\n",
+ "import matplotlib.pyplot as plt\n",
+ "from matplotlib.ticker import MaxNLocator\n",
+ "from collections import defaultdict\n",
+ "import pandas as pd\n",
+ "plt.rcParams.update({'font.size': 12})\n",
+ "pd.set_option('display.max_colwidth', -1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Training data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "16 rows affected.\n",
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
+ " if (typeof(WebSocket) !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support.' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
+ "\n",
+ " this.ws = websocket;\n",
+ "\n",
+ " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ "\n",
+ " if (!this.supports_binary) {\n",
+ " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
+ "\n",
+ " this.context = undefined;\n",
+ " this.message = undefined;\n",
+ " this.canvas = undefined;\n",
+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
+ " if (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+ " }\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " }\n",
+ "\n",
+ " this.imageObj.onload = function() {\n",
+ " if (fig.image_mode == 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onunload = function() {\n",
+ " fig.ws.close();\n",
+ " }\n",
+ "\n",
+ " this.ws.onmessage = this._make_on_message_function(this);\n",
+ "\n",
+ " this.ondownload = ondownload;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option)\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width, fig.canvas.height);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var width = this.canvas.width/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " // select the cell after this one\n",
+ " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
+ " IPython.notebook.select(index + 1);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i<ncells; i++) {\n",
+ " var cell = cells[i];\n",
+ " if (cell.cell_type === 'code'){\n",
+ " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " var data = cell.output_area.outputs[j];\n",
+ " if (data.data) {\n",
+ " // IPython >= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
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+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.legend.Legend at 0x154945910>"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5,21,'Iteration')"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5,1,'Training Accuracy')"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5,21,'Iteration')"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5,1,'Training Loss (Cross Entropy)')"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x156a53950>]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "16 rows affected.\n",
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x156a331d0>]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "16 rows affected.\n",
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x156a5f250>]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "16 rows affected.\n",
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x156a45e90>]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "16 rows affected.\n",
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x156a5f310>]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "16 rows affected.\n",
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x156a82290>]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "16 rows affected.\n",
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x156a827d0>]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "16 rows affected.\n",
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x156a82d10>]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "16 rows affected.\n",
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x156a82790>]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "16 rows affected.\n",
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x156a45dd0>]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "16 rows affected.\n",
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x156a97610>]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "16 rows affected.\n",
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x156a97b50>]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "16 rows affected.\n",
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x156aa40d0>]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "16 rows affected.\n",
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x156a97b10>]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "16 rows affected.\n",
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x156aa4110>]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "16 rows affected.\n",
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x156aa4990>]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "16 rows affected.\n",
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
+ " if (typeof(WebSocket) !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support.' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
+ "\n",
+ " this.ws = websocket;\n",
+ "\n",
+ " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ "\n",
+ " if (!this.supports_binary) {\n",
+ " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
+ "\n",
+ " this.context = undefined;\n",
+ " this.message = undefined;\n",
+ " this.canvas = undefined;\n",
+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
+ " if (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+ " }\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " }\n",
+ "\n",
+ " this.imageObj.onload = function() {\n",
+ " if (fig.image_mode == 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onunload = function() {\n",
+ " fig.ws.close();\n",
+ " }\n",
+ "\n",
+ " this.ws.onmessage = this._make_on_message_function(this);\n",
+ "\n",
+ " this.ondownload = ondownload;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option)\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width, fig.canvas.height);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var width = this.canvas.width/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " // select the cell after this one\n",
+ " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
+ " IPython.notebook.select(index + 1);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i<ncells; i++) {\n",
+ " var cell = cells[i];\n",
+ " if (cell.cell_type === 'code'){\n",
+ " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " var data = cell.output_area.outputs[j];\n",
+ " if (data.data) {\n",
+ " // IPython >= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
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+ ]
+ },
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+ "output_type": "display_data"
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+ "[<matplotlib.lines.Line2D at 0x156b7a350>]"
+ ]
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+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
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+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
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+ "data": {
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+ "[<matplotlib.lines.Line2D at 0x156b3f090>]"
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+ "execution_count": 8,
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+ "data": {
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+ "[<matplotlib.lines.Line2D at 0x156b7a690>]"
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+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
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+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
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+ "data": {
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+ "data": {
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+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
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+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
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+ "data": {
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+ "[<matplotlib.lines.Line2D at 0x156b868d0>]"
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+ "data": {
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+ "[<matplotlib.lines.Line2D at 0x156b86990>]"
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+ "metadata": {},
+ "output_type": "execute_result"
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+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
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+ "[<matplotlib.lines.Line2D at 0x156b90190>]"
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+ "execution_count": 8,
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+ "output_type": "execute_result"
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+ "data": {
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+ "[<matplotlib.lines.Line2D at 0x156b90250>]"
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+ "output_type": "execute_result"
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+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x156b7af90>]"
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+ "execution_count": 8,
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+ "data": {
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+ "[<matplotlib.lines.Line2D at 0x156b3f310>]"
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+ "metadata": {},
+ "output_type": "execute_result"
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+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
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+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x156b90a90>]"
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+ "[<matplotlib.lines.Line2D at 0x156b90f50>]"
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+ "metadata": {},
+ "output_type": "execute_result"
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+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
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+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x156b9d610>]"
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+ "output_type": "execute_result"
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+ "[<matplotlib.lines.Line2D at 0x156b9d890>]"
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+ "metadata": {},
+ "output_type": "execute_result"
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+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
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+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x156bac090>]"
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+ "data": {
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+ "[<matplotlib.lines.Line2D at 0x156bac150>]"
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+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
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+ "[<matplotlib.lines.Line2D at 0x156b9d5d0>]"
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+ "output_type": "execute_result"
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+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x156b90ed0>]"
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+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
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+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x156baca50>]"
+ ]
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+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x156bacb50>]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.legend.Legend at 0x156abfc90>"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_results = %sql SELECT * FROM cifar10_multi_model_info ORDER BY training_loss_final ASC LIMIT 100;\n",
+ "df_results = df_results.DataFrame()\n",
+ "\n",
+ "df_summary = %sql SELECT * FROM cifar10_multi_model_summary;\n",
+ "df_summary = df_summary.DataFrame()\n",
+ "\n",
+ "#set up plots\n",
+ "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(10,5))\n",
+ "fig.legend(ncol=4)\n",
+ "fig.tight_layout()\n",
+ "\n",
+ "ax_metric = axs[0]\n",
+ "ax_loss = axs[1]\n",
+ "\n",
+ "ax_metric.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
+ "ax_metric.set_xlabel('Iteration')\n",
+ "#ax_metric.set_ylabel('Accuracy')\n",
+ "ax_metric.set_title('Training Accuracy')\n",
+ "\n",
+ "ax_loss.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
+ "ax_loss.set_xlabel('Iteration')\n",
+ "#ax_loss.set_ylabel('Cross Entropy Loss')\n",
+ "ax_loss.set_title('Training Loss (Cross Entropy)')\n",
+ "\n",
+ "iters = df_summary['metrics_iters'][0]\n",
+ "\n",
+ "for mst_key in df_results['mst_key']:\n",
+ " df_output_info = %sql SELECT training_metrics,training_loss FROM cifar10_multi_model_info WHERE mst_key = $mst_key\n",
+ " df_output_info = df_output_info.DataFrame()\n",
+ " training_metrics = df_output_info['training_metrics'][0]\n",
+ " training_loss = df_output_info['training_loss'][0]\n",
+ " \n",
+ " ax_metric.plot(iters, training_metrics, label=mst_key, marker='o')\n",
+ " df_results = %sql SELECT * FROM cifar10_multi_model_info ORDER BY training_loss_final ASC LIMIT 100;\n",
+ "df_results = df_results.DataFrame()\n",
+ "\n",
+ "df_summary = %sql SELECT * FROM cifar10_multi_model_summary;\n",
+ "df_summary = df_summary.DataFrame()\n",
+ "\n",
+ "#set up plots\n",
+ "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(10,5))\n",
+ "fig.legend(ncol=4)\n",
+ "fig.tight_layout()\n",
+ "\n",
+ "ax_metric = axs[0]\n",
+ "ax_loss = axs[1]\n",
+ "\n",
+ "ax_metric.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
+ "ax_metric.set_xlabel('Iteration')\n",
+ "#ax_metric.set_ylabel('Accuracy')\n",
+ "ax_metric.set_title('Training Accuracy')\n",
+ "\n",
+ "ax_loss.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
+ "ax_loss.set_xlabel('Iteration')\n",
+ "#ax_loss.set_ylabel('Cross Entropy Loss')\n",
+ "ax_loss.set_title('Training Loss (Cross Entropy)')\n",
+ "\n",
+ "iters = df_summary['metrics_iters'][0]\n",
+ "\n",
+ "for mst_key in df_results['mst_key']:\n",
+ " df_output_info = %sql SELECT training_metrics,training_loss FROM cifar10_multi_model_info WHERE mst_key = $mst_key\n",
+ " df_output_info = df_output_info.DataFrame()\n",
+ " training_metrics = df_output_info['training_metrics'][0]\n",
+ " training_loss = df_output_info['training_loss'][0]\n",
+ " \n",
+ " #ax_metric.plot(iters, training_metrics, label=mst_key, marker='o')\n",
+ " ax_metric.plot(iters, training_metrics)\n",
+ " \n",
+ " #ax_loss.plot(iters, training_loss, label=mst_key, marker='o')\n",
+ " ax_loss.plot(iters, training_loss)\n",
+ "\n",
+ "plt.legend()\n",
+ "# fig.savefig('./lc_keras_fit.png', dpi = 300)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Validation data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "16 rows affected.\n",
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
+ " if (typeof(WebSocket) !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support.' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
+ "\n",
+ " this.ws = websocket;\n",
+ "\n",
+ " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ "\n",
+ " if (!this.supports_binary) {\n",
+ " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
+ "\n",
+ " this.context = undefined;\n",
+ " this.message = undefined;\n",
+ " this.canvas = undefined;\n",
+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
+ " if (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+ " }\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " }\n",
+ "\n",
+ " this.imageObj.onload = function() {\n",
+ " if (fig.image_mode == 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onunload = function() {\n",
+ " fig.ws.close();\n",
+ " }\n",
+ "\n",
+ " this.ws.onmessage = this._make_on_message_function(this);\n",
+ "\n",
+ " this.ondownload = ondownload;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option)\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width, fig.canvas.height);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var width = this.canvas.width/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " // select the cell after this one\n",
+ " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
+ " IPython.notebook.select(index + 1);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i<ncells; i++) {\n",
+ " var cell = cells[i];\n",
+ " if (cell.cell_type === 'code'){\n",
+ " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " var data = cell.output_area.outputs[j];\n",
+ " if (data.data) {\n",
+ " // IPython >= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
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\" width=\"720\">"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.legend.Legend at 0x156c14f10>"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5,21,'Iteration')"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5,1,'Validation Accuracy')"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5,21,'Iteration')"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5,1,'Validation Loss (Cross Entropy)')"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
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+ "1 rows affected.\n"
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+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
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+ "[<matplotlib.lines.Line2D at 0x156d24050>]"
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+ "output_type": "execute_result"
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+ "output_type": "stream",
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+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
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+ },
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+ "[<matplotlib.lines.Line2D at 0x156d247d0>]"
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+ },
+ {
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+ "[<matplotlib.lines.Line2D at 0x156d248d0>]"
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+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
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+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
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+ "output_type": "execute_result"
+ },
+ {
+ "data": {
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+ "[<matplotlib.lines.Line2D at 0x156d33250>]"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x156d33a50>]"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x156d33bd0>]"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.legend.Legend at 0x156c72690>"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_results = %sql SELECT * FROM cifar10_multi_model_info ORDER BY validation_loss_final ASC LIMIT 100;\n",
+ "df_results = df_results.DataFrame()\n",
+ "\n",
+ "df_summary = %sql SELECT * FROM cifar10_multi_model_summary;\n",
+ "df_summary = df_summary.DataFrame()\n",
+ "\n",
+ "#set up plots\n",
+ "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(10,5))\n",
+ "fig.legend(ncol=4)\n",
+ "fig.tight_layout()\n",
+ "\n",
+ "ax_metric = axs[0]\n",
+ "ax_loss = axs[1]\n",
+ "\n",
+ "ax_metric.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
+ "ax_metric.set_xlabel('Iteration')\n",
+ "#ax_metric.set_ylabel('Accuracy')\n",
+ "ax_metric.set_title('Validation Accuracy')\n",
+ "\n",
+ "ax_loss.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
+ "ax_loss.set_xlabel('Iteration')\n",
+ "#ax_loss.set_ylabel('Cross Entropy Loss')\n",
+ "ax_loss.set_title('Validation Loss (Cross Entropy)')\n",
+ "\n",
+ "iters = df_summary['metrics_iters'][0]\n",
+ "\n",
+ "for mst_key in df_results['mst_key']:\n",
+ " df_output_info = %sql SELECT validation_metrics,validation_loss FROM cifar10_multi_model_info WHERE mst_key = $mst_key\n",
+ " df_output_info = df_output_info.DataFrame()\n",
+ " validation_metrics = df_output_info['validation_metrics'][0]\n",
+ " validation_loss = df_output_info['validation_loss'][0]\n",
+ " \n",
+ " #ax_metric.plot(iters, validation_metrics, label=mst_key, marker='o')\n",
+ " ax_metric.plot(iters, validation_metrics)\n",
+ " \n",
+ " #ax_loss.plot(iters, validation_loss, label=mst_key, marker='o')\n",
+ " ax_loss.plot(iters, validation_loss)\n",
+ "\n",
+ "plt.legend()\n",
+ "# fig.savefig('./lc_keras_fit.png', dpi = 300)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Plot training and validation curves together"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n",
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
+ " if (typeof(WebSocket) !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support.' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
+ "\n",
+ " this.ws = websocket;\n",
+ "\n",
+ " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ "\n",
+ " if (!this.supports_binary) {\n",
+ " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
+ "\n",
+ " this.context = undefined;\n",
+ " this.message = undefined;\n",
+ " this.canvas = undefined;\n",
+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
+ " if (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+ " }\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " }\n",
+ "\n",
+ " this.imageObj.onload = function() {\n",
+ " if (fig.image_mode == 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onunload = function() {\n",
+ " fig.ws.close();\n",
+ " }\n",
+ "\n",
+ " this.ws.onmessage = this._make_on_message_function(this);\n",
+ "\n",
+ " this.ondownload = ondownload;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option)\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width, fig.canvas.height);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var width = this.canvas.width/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " // select the cell after this one\n",
+ " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
+ " IPython.notebook.select(index + 1);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i<ncells; i++) {\n",
+ " var cell = cells[i];\n",
+ " if (cell.cell_type === 'code'){\n",
+ " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " var data = cell.output_area.outputs[j];\n",
+ " if (data.data) {\n",
+ " // IPython >= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
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\" width=\"720\">"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.legend.Legend at 0x1570e6a50>"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5,21,'Iteration')"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5,1,'Classification Accuracy')"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5,21,'Iteration')"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5,1,'Cross Entropy Loss')"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n",
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x157191790>]"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x157191c10>]"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x1571912d0>]"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x157184890>]"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.legend.Legend at 0x15719c810>"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# mst tuple(s) to plot\n",
+ "mst_key_to_plot = 10\n",
+ "df_results = %sql SELECT * FROM cifar10_multi_model_info WHERE mst_key = $mst_key_to_plot;\n",
+ "df_results = df_results.DataFrame()\n",
+ "\n",
+ "df_summary = %sql SELECT * FROM cifar10_multi_model_summary;\n",
+ "df_summary = df_summary.DataFrame()\n",
+ "\n",
+ "#set up plots\n",
+ "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(10,5))\n",
+ "fig.legend(ncol=4)\n",
+ "fig.tight_layout()\n",
+ "\n",
+ "ax_metric = axs[0]\n",
+ "ax_loss = axs[1]\n",
+ "\n",
+ "ax_metric.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
+ "ax_metric.set_xlabel('Iteration')\n",
+ "#ax_metric.set_ylabel('Accuracy')\n",
+ "ax_metric.set_title('Classification Accuracy')\n",
+ "\n",
+ "ax_loss.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
+ "ax_loss.set_xlabel('Iteration')\n",
+ "#ax_loss.set_ylabel('Loss')\n",
+ "ax_loss.set_title('Cross Entropy Loss')\n",
+ "\n",
+ "iters = df_summary['metrics_iters'][0]\n",
+ "\n",
+ "for mst_key in df_results['mst_key']:\n",
+ " \n",
+ " #train\n",
+ " df_output_info = %sql SELECT training_metrics,training_loss FROM cifar10_multi_model_info WHERE mst_key = $mst_key\n",
+ " df_output_info = df_output_info.DataFrame()\n",
+ " training_metrics = df_output_info['training_metrics'][0]\n",
+ " training_loss = df_output_info['training_loss'][0]\n",
+ " \n",
+ " #test\n",
+ " df_output_info = %sql SELECT validation_metrics,validation_loss FROM cifar10_multi_model_info WHERE mst_key = $mst_key\n",
+ " df_output_info = df_output_info.DataFrame()\n",
+ " validation_metrics = df_output_info['validation_metrics'][0]\n",
+ " validation_loss = df_output_info['validation_loss'][0]\n",
+ " \n",
+ " label_train = str(mst_key) + '-train'\n",
+ " #ax_metric.plot(iters, training_metrics, label=label_train, marker='x')\n",
+ " #ax_loss.plot(iters, training_loss, label=label_train, marker='x')\n",
+ " ax_metric.plot(iters, training_metrics, label=label_train)\n",
+ " ax_loss.plot(iters, training_loss, label=label_train)\n",
+ " \n",
+ " \n",
+ " label_test = str(mst_key) + '-test'\n",
+ " #ax_metric.plot(iters, validation_metrics, label=label_test, marker='o')\n",
+ " #ax_loss.plot(iters, validation_loss, label=label_test, marker='o')\n",
+ " ax_metric.plot(iters, validation_metrics, label=label_test)\n",
+ " ax_loss.plot(iters, validation_loss, label=label_test)\n",
+ "\n",
+ "\n",
+ "plt.legend()\n",
+ "# fig.savefig('./lc_keras_fit.png', dpi = 300)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"predict\"></a>\n",
+ "# 7. Inference\n",
+ "\n",
+ "## 7a. Run predict on the whole validation dataset\n",
+ "\n",
+ "Pick a reasonable model from the previous run."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>mst_key</th>\n",
+ " <th>model_id</th>\n",
+ " <th>compile_params</th>\n",
+ " <th>fit_params</th>\n",
+ " <th>model_type</th>\n",
+ " <th>model_size</th>\n",
+ " <th>metrics_elapsed_time</th>\n",
+ " <th>metrics_type</th>\n",
+ " <th>training_metrics_final</th>\n",
+ " <th>training_loss_final</th>\n",
+ " <th>training_metrics</th>\n",
+ " <th>training_loss</th>\n",
+ " <th>validation_metrics_final</th>\n",
+ " <th>validation_loss_final</th>\n",
+ " <th>validation_metrics</th>\n",
+ " <th>validation_loss</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>10</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='adam(lr=0.0001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=128,epochs=5</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>2159.70019531</td>\n",
+ " <td>[158.506701946259, 323.779083013535, 491.302199840546, 660.148201942444, 829.340363025665, 999.844955921173, 1169.83032798767, 1340.5411260128, 1513.8498609066, 1687.69545793533]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.867579996586</td>\n",
+ " <td>0.380911707878</td>\n",
+ " <td>[0.576640009880066, 0.66431999206543, 0.707199990749359, 0.749520003795624, 0.778980016708374, 0.799520015716553, 0.820659995079041, 0.839940011501312, 0.854659974575043, 0.867579996585846]</td>\n",
+ " <td>[1.20035827159882, 0.945839285850525, 0.823047578334808, 0.703792750835419, 0.624295234680176, 0.566677749156952, 0.511338114738464, 0.459649682044983, 0.418204575777054, 0.380911707878113]</td>\n",
+ " <td>0.799700021744</td>\n",
+ " <td>0.571797966957</td>\n",
+ " <td>[0.572700023651123, 0.655099987983704, 0.69980001449585, 0.731299996376038, 0.756399989128113, 0.771399974822998, 0.778999984264374, 0.791599988937378, 0.798200011253357, 0.799700021743774]</td>\n",
+ " <td>[1.20565474033356, 0.964107036590576, 0.849484860897064, 0.752416431903839, 0.691979646682739, 0.65268349647522, 0.628291726112366, 0.599337160587311, 0.586054861545563, 0.571797966957092]</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(10, 2, u\"loss='categorical_crossentropy',optimizer='adam(lr=0.0001)',metrics=['accuracy']\", u'batch_size=128,epochs=5', u'madlib_keras', 2159.70019531, [158.506701946259, 323.779083013535, 491.302199840546, 660.148201942444, 829.340363025665, 999.844955921173, 1169.83032798767, 1340.5411260128, 1513.8498609066, 1687.69545793533], [u'accuracy'], 0.867579996586, 0.380911707878, [0.576640009880066, 0.66431999206543, 0.707199990749359, 0.749520003795624, 0.778980016708374, 0.799520015716553, 0.820659995079041, 0.839940011501312, 0.854659974575043, 0.867579996585846], [1.20035827159882, 0.945839285850525, 0.823047578334808, 0.703792750835419, 0.624295234680176, 0.566677749156952, 0.511338114738464, 0.459649682044983, 0.418204575777054, 0.380911707878113], 0.799700021744, 0.571797966957, [0.572700023651123, 0.655099987983704, 0.69980001449585, 0.731299996376038, 0.756399989128113, 0.771399974822998, 0.778999984264374, 0.791599988937378, 0.798200011253357, 0.799700021743774], [1.20565474033356, 0.964107036590576, 0.849484860897064, 0.752416431903839, 0.691979646682739, 0.65268349647522, 0.628291726112366, 0.599337160587311, 0.586054861545563, 0.571797966957092])]"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT * FROM cifar10_multi_model_info WHERE mst_key=10;"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "Done.\n",
+ "1 rows affected.\n",
+ "5 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>id</th>\n",
+ " <th>estimated_y</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>8</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>8</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>4</td>\n",
+ " <td>8</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>5</td>\n",
+ " <td>6</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(1, 3), (2, 8), (3, 8), (4, 8), (5, 6)]"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS cifar10_val_predict;\n",
+ "\n",
+ "SELECT madlib.madlib_keras_predict('cifar10_multi_model', -- model\n",
+ " 'cifar10_val', -- test_table\n",
+ " 'id', -- id column\n",
+ " 'x', -- independent var\n",
+ " 'cifar10_val_predict', -- output table\n",
+ " 'response', -- prediction type\n",
+ " TRUE, -- use gpus\n",
+ " 10 -- mst_key to use\n",
+ " );\n",
+ "\n",
+ "SELECT * FROM cifar10_val_predict ORDER BY id LIMIT 5;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Count missclassifications"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2003</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(2003L,)]"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT COUNT(*) FROM cifar10_val_predict JOIN cifar10_val USING (id) \n",
+ "WHERE cifar10_val_predict.estimated_y != cifar10_val.y;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Accuracy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>test_accuracy_percent</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>79.97</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(Decimal('79.97'),)]"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT round(count(*)*100.0/10000.0,2) as test_accuracy_percent from\n",
+ " (select cifar10_val.y as actual, cifar10_val_predict.estimated_y as predicted\n",
+ " from cifar10_val_predict inner join cifar10_val\n",
+ " on cifar10_val.id=cifar10_val_predict.id) q\n",
+ "WHERE q.actual=q.predicted;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 7b. Select a random image from the validation dataset and run predict\n",
+ "\n",
+ "Label map"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "label_names = {\n",
+ " 0 :\"airplane\",\n",
+ " 1 :\"automobile\",\n",
+ " 2 :\"bird\",\n",
+ " 3 :\"cat\",\n",
+ " 4 :\"deer\",\n",
+ " 5 :\"dog\",\n",
+ " 6 :\"frog\",\n",
+ " 7 :\"horse\",\n",
+ " 8 :\"ship\",\n",
+ " 9 :\"truck\"\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Pick a random image"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "Done.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS cifar10_val_random;\n",
+ "CREATE TABLE cifar10_val_random AS\n",
+ " SELECT * FROM cifar10_val ORDER BY random() LIMIT 1;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Predict"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "Done.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>id</th>\n",
+ " <th>prob_0</th>\n",
+ " <th>prob_1</th>\n",
+ " <th>prob_2</th>\n",
+ " <th>prob_3</th>\n",
+ " <th>prob_4</th>\n",
+ " <th>prob_5</th>\n",
+ " <th>prob_6</th>\n",
+ " <th>prob_7</th>\n",
+ " <th>prob_8</th>\n",
+ " <th>prob_9</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>5221</td>\n",
+ " <td>1.0724234e-06</td>\n",
+ " <td>1.4683914e-07</td>\n",
+ " <td>1.5451868e-06</td>\n",
+ " <td>2.83348e-07</td>\n",
+ " <td>6.203704e-05</td>\n",
+ " <td>4.6814375e-06</td>\n",
+ " <td>6.654035e-09</td>\n",
+ " <td>0.99993</td>\n",
+ " <td>1.17486385e-08</td>\n",
+ " <td>5.6579474e-08</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(5221, 1.0724234e-06, 1.4683914e-07, 1.5451868e-06, 2.83348e-07, 6.203704e-05, 4.6814375e-06, 6.654035e-09, 0.99993, 1.17486385e-08, 5.6579474e-08)]"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS cifar10_val_random_predict;\n",
+ "\n",
+ "SELECT madlib.madlib_keras_predict('cifar10_multi_model', -- model\n",
+ " 'cifar10_val_random', -- test_table\n",
+ " 'id', -- id column\n",
+ " 'x', -- independent var\n",
+ " 'cifar10_val_random_predict', -- output table\n",
+ " 'prob', -- prediction type\n",
+ " TRUE, -- use gpus\n",
+ " 10 -- mst_key to use\n",
+ " );\n",
+ "\n",
+ "SELECT * FROM cifar10_val_random_predict;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Format output and display"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "Done.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>feature_vector</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[1.0724234e-06, 1.4683914e-07, 1.5451868e-06, 2.83348e-07, 6.203704e-05, 4.6814375e-06, 6.654035e-09, 0.99993, 1.17486385e-08, 5.6579474e-08]</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[([1.0724234e-06, 1.4683914e-07, 1.5451868e-06, 2.83348e-07, 6.203704e-05, 4.6814375e-06, 6.654035e-09, 0.99993, 1.17486385e-08, 5.6579474e-08],)]"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS cifar10_val_random_predict_array, cifar10_val_random_predict_array_summary;\n",
+ "SELECT madlib.cols2vec(\n",
+ " 'cifar10_val_random_predict',\n",
+ " 'cifar10_val_random_predict_array',\n",
+ " '*',\n",
+ " 'id'\n",
+ ");\n",
+ "select * from cifar10_val_random_predict_array;"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.image.AxesImage at 0x15724d350>"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n",
+ " \n",
+ "horse 0.99993\n",
+ "deer 6.203704e-05\n",
+ "dog 4.6814375e-06\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "x = %sql SELECT x FROM cifar10_val_random;\n",
+ "x = x.DataFrame().to_numpy()\n",
+ "import numpy as np\n",
+ "from matplotlib.pyplot import imshow\n",
+ "%matplotlib inline\n",
+ "x_np = np.array(x[0][0], dtype=np.uint8)\n",
+ "imshow(x_np)\n",
+ "\n",
+ "x = %sql SELECT * FROM cifar10_val_random_predict_array;\n",
+ "x = x.DataFrame().to_numpy()\n",
+ "x = np.array(x[0][0])\n",
+ "top_3_prob_label_indices = x.argsort()[-3:][::-1]\n",
+ "print (\" \");\n",
+ "for index in top_3_prob_label_indices:\n",
+ " print (label_names[index], x[index])"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.16"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/community-artifacts/Deep-learning/.ipynb_checkpoints/MADlib-Keras-model-selection-MLP-v1-checkpoint.ipynb b/community-artifacts/Deep-learning/.ipynb_checkpoints/MADlib-Keras-model-selection-MLP-v1-checkpoint.ipynb
new file mode 100644
index 0000000..cfe8c97
--- /dev/null
+++ b/community-artifacts/Deep-learning/.ipynb_checkpoints/MADlib-Keras-model-selection-MLP-v1-checkpoint.ipynb
@@ -0,0 +1,5709 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Model Selection for Multilayer Perceptron Using Keras and MADlib\n",
+ "\n",
+ "E2E classification example using MADlib calling a Keras MLP for different hyperparameters and model architectures.\n",
+ "\n",
+ "Deep learning works best on very large datasets, but that is not convenient for a quick introduction to the syntax. So in this workbook we use the well known iris data set from https://archive.ics.uci.edu/ml/datasets/iris to help get you started. It is similar to the example in user docs http://madlib.apache.org/docs/latest/index.html\n",
+ "\n",
+ "For more realistic examples please refer to the deep learning notebooks at https://github.com/apache/madlib-site/tree/asf-site/community-artifacts\n",
+ "\n",
+ "## Table of contents\n",
+ "\n",
+ "<a href=\"#class\">Classification</a>\n",
+ "\n",
+ "* <a href=\"#create_input_data\">1. Create input data</a>\n",
+ "\n",
+ "* <a href=\"#pp\">2. Call preprocessor for deep learning</a>\n",
+ "\n",
+ "* <a href=\"#load\">3. Define and load model architecture</a>\n",
+ "\n",
+ "* <a href=\"#def_mst\">4. Define and load model selection tuples</a>\n",
+ "\n",
+ "* <a href=\"#train\">5. Train</a>\n",
+ "\n",
+ "* <a href=\"#eval\">6. Evaluate</a>\n",
+ "\n",
+ "* <a href=\"#pred\">7. Predict</a>\n",
+ "\n",
+ "<a href=\"#class2\">Classification with Other Parameters</a>\n",
+ "\n",
+ "* <a href=\"#val_dataset\">1. Validation dataset</a>\n",
+ "\n",
+ "* <a href=\"#pred_prob\">2. Predict probabilities</a>\n",
+ "\n",
+ "* <a href=\"#warm_start\">3. Warm start</a>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/fmcquillan/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated since IPython 4.0. You should import from traitlets.config instead.\n",
+ " \"You should import from traitlets.config instead.\", ShimWarning)\n",
+ "/Users/fmcquillan/anaconda/lib/python2.7/site-packages/IPython/utils/traitlets.py:5: UserWarning: IPython.utils.traitlets has moved to a top-level traitlets package.\n",
+ " warn(\"IPython.utils.traitlets has moved to a top-level traitlets package.\")\n"
+ ]
+ }
+ ],
+ "source": [
+ "%load_ext sql"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "u'Connected: gpadmin@madlib'"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Greenplum Database 5.x on GCP (PM demo machine) - direct external IP access\n",
+ "#%sql postgresql://gpadmin@34.67.65.96:5432/madlib\n",
+ "\n",
+ "# Greenplum Database 5.x on GCP - via tunnel\n",
+ "%sql postgresql://gpadmin@localhost:8000/madlib\n",
+ " \n",
+ "# PostgreSQL local\n",
+ "#%sql postgresql://fmcquillan@localhost:5432/madlib"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>version</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>MADlib version: 1.17-dev, git revision: rel/v1.16-54-gec5614f, cmake configuration time: Wed Dec 18 17:08:05 UTC 2019, build type: release, build system: Linux-3.10.0-1062.4.3.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'MADlib version: 1.17-dev, git revision: rel/v1.16-54-gec5614f, cmake configuration time: Wed Dec 18 17:08:05 UTC 2019, build type: release, build system: Linux-3.10.0-1062.4.3.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5',)]"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%sql select madlib.version();\n",
+ "#%sql select version();"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"class\"></a>\n",
+ "# Classification\n",
+ "\n",
+ "<a id=\"create_input_data\"></a>\n",
+ "# 1. Create input data\n",
+ "\n",
+ "Load iris data set."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "Done.\n",
+ "150 rows affected.\n",
+ "150 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>id</th>\n",
+ " <th>attributes</th>\n",
+ " <th>class_text</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>[Decimal('5.1'), Decimal('3.5'), Decimal('1.4'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>[Decimal('4.9'), Decimal('3.0'), Decimal('1.4'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>[Decimal('4.7'), Decimal('3.2'), Decimal('1.3'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>4</td>\n",
+ " <td>[Decimal('4.6'), Decimal('3.1'), Decimal('1.5'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>5</td>\n",
+ " <td>[Decimal('5.0'), Decimal('3.6'), Decimal('1.4'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>6</td>\n",
+ " <td>[Decimal('5.4'), Decimal('3.9'), Decimal('1.7'), Decimal('0.4')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>7</td>\n",
+ " <td>[Decimal('4.6'), Decimal('3.4'), Decimal('1.4'), Decimal('0.3')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>8</td>\n",
+ " <td>[Decimal('5.0'), Decimal('3.4'), Decimal('1.5'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>9</td>\n",
+ " <td>[Decimal('4.4'), Decimal('2.9'), Decimal('1.4'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>10</td>\n",
+ " <td>[Decimal('4.9'), Decimal('3.1'), Decimal('1.5'), Decimal('0.1')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>11</td>\n",
+ " <td>[Decimal('5.4'), Decimal('3.7'), Decimal('1.5'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>12</td>\n",
+ " <td>[Decimal('4.8'), Decimal('3.4'), Decimal('1.6'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>13</td>\n",
+ " <td>[Decimal('4.8'), Decimal('3.0'), Decimal('1.4'), Decimal('0.1')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>14</td>\n",
+ " <td>[Decimal('4.3'), Decimal('3.0'), Decimal('1.1'), Decimal('0.1')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>15</td>\n",
+ " <td>[Decimal('5.8'), Decimal('4.0'), Decimal('1.2'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>16</td>\n",
+ " <td>[Decimal('5.7'), Decimal('4.4'), Decimal('1.5'), Decimal('0.4')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>17</td>\n",
+ " <td>[Decimal('5.4'), Decimal('3.9'), Decimal('1.3'), Decimal('0.4')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>18</td>\n",
+ " <td>[Decimal('5.1'), Decimal('3.5'), Decimal('1.4'), Decimal('0.3')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>19</td>\n",
+ " <td>[Decimal('5.7'), Decimal('3.8'), Decimal('1.7'), Decimal('0.3')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>20</td>\n",
+ " <td>[Decimal('5.1'), Decimal('3.8'), Decimal('1.5'), Decimal('0.3')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>21</td>\n",
+ " <td>[Decimal('5.4'), Decimal('3.4'), Decimal('1.7'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>22</td>\n",
+ " <td>[Decimal('5.1'), Decimal('3.7'), Decimal('1.5'), Decimal('0.4')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>23</td>\n",
+ " <td>[Decimal('4.6'), Decimal('3.6'), Decimal('1.0'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>24</td>\n",
+ " <td>[Decimal('5.1'), Decimal('3.3'), Decimal('1.7'), Decimal('0.5')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>25</td>\n",
+ " <td>[Decimal('4.8'), Decimal('3.4'), Decimal('1.9'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>26</td>\n",
+ " <td>[Decimal('5.0'), Decimal('3.0'), Decimal('1.6'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>27</td>\n",
+ " <td>[Decimal('5.0'), Decimal('3.4'), Decimal('1.6'), Decimal('0.4')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>28</td>\n",
+ " <td>[Decimal('5.2'), Decimal('3.5'), Decimal('1.5'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>29</td>\n",
+ " <td>[Decimal('5.2'), Decimal('3.4'), Decimal('1.4'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>30</td>\n",
+ " <td>[Decimal('4.7'), Decimal('3.2'), Decimal('1.6'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>31</td>\n",
+ " <td>[Decimal('4.8'), Decimal('3.1'), Decimal('1.6'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>32</td>\n",
+ " <td>[Decimal('5.4'), Decimal('3.4'), Decimal('1.5'), Decimal('0.4')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>33</td>\n",
+ " <td>[Decimal('5.2'), Decimal('4.1'), Decimal('1.5'), Decimal('0.1')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>34</td>\n",
+ " <td>[Decimal('5.5'), Decimal('4.2'), Decimal('1.4'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>35</td>\n",
+ " <td>[Decimal('4.9'), Decimal('3.1'), Decimal('1.5'), Decimal('0.1')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>36</td>\n",
+ " <td>[Decimal('5.0'), Decimal('3.2'), Decimal('1.2'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>37</td>\n",
+ " <td>[Decimal('5.5'), Decimal('3.5'), Decimal('1.3'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>38</td>\n",
+ " <td>[Decimal('4.9'), Decimal('3.1'), Decimal('1.5'), Decimal('0.1')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>39</td>\n",
+ " <td>[Decimal('4.4'), Decimal('3.0'), Decimal('1.3'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>40</td>\n",
+ " <td>[Decimal('5.1'), Decimal('3.4'), Decimal('1.5'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>41</td>\n",
+ " <td>[Decimal('5.0'), Decimal('3.5'), Decimal('1.3'), Decimal('0.3')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>42</td>\n",
+ " <td>[Decimal('4.5'), Decimal('2.3'), Decimal('1.3'), Decimal('0.3')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>43</td>\n",
+ " <td>[Decimal('4.4'), Decimal('3.2'), Decimal('1.3'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>44</td>\n",
+ " <td>[Decimal('5.0'), Decimal('3.5'), Decimal('1.6'), Decimal('0.6')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>45</td>\n",
+ " <td>[Decimal('5.1'), Decimal('3.8'), Decimal('1.9'), Decimal('0.4')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>46</td>\n",
+ " <td>[Decimal('4.8'), Decimal('3.0'), Decimal('1.4'), Decimal('0.3')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>47</td>\n",
+ " <td>[Decimal('5.1'), Decimal('3.8'), Decimal('1.6'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>48</td>\n",
+ " <td>[Decimal('4.6'), Decimal('3.2'), Decimal('1.4'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>49</td>\n",
+ " <td>[Decimal('5.3'), Decimal('3.7'), Decimal('1.5'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>50</td>\n",
+ " <td>[Decimal('5.0'), Decimal('3.3'), Decimal('1.4'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>51</td>\n",
+ " <td>[Decimal('7.0'), Decimal('3.2'), Decimal('4.7'), Decimal('1.4')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>52</td>\n",
+ " <td>[Decimal('6.4'), Decimal('3.2'), Decimal('4.5'), Decimal('1.5')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>53</td>\n",
+ " <td>[Decimal('6.9'), Decimal('3.1'), Decimal('4.9'), Decimal('1.5')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>54</td>\n",
+ " <td>[Decimal('5.5'), Decimal('2.3'), Decimal('4.0'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>55</td>\n",
+ " <td>[Decimal('6.5'), Decimal('2.8'), Decimal('4.6'), Decimal('1.5')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>56</td>\n",
+ " <td>[Decimal('5.7'), Decimal('2.8'), Decimal('4.5'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>57</td>\n",
+ " <td>[Decimal('6.3'), Decimal('3.3'), Decimal('4.7'), Decimal('1.6')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>58</td>\n",
+ " <td>[Decimal('4.9'), Decimal('2.4'), Decimal('3.3'), Decimal('1.0')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>59</td>\n",
+ " <td>[Decimal('6.6'), Decimal('2.9'), Decimal('4.6'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>60</td>\n",
+ " <td>[Decimal('5.2'), Decimal('2.7'), Decimal('3.9'), Decimal('1.4')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>61</td>\n",
+ " <td>[Decimal('5.0'), Decimal('2.0'), Decimal('3.5'), Decimal('1.0')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>62</td>\n",
+ " <td>[Decimal('5.9'), Decimal('3.0'), Decimal('4.2'), Decimal('1.5')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>63</td>\n",
+ " <td>[Decimal('6.0'), Decimal('2.2'), Decimal('4.0'), Decimal('1.0')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>64</td>\n",
+ " <td>[Decimal('6.1'), Decimal('2.9'), Decimal('4.7'), Decimal('1.4')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>65</td>\n",
+ " <td>[Decimal('5.6'), Decimal('2.9'), Decimal('3.6'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>66</td>\n",
+ " <td>[Decimal('6.7'), Decimal('3.1'), Decimal('4.4'), Decimal('1.4')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>67</td>\n",
+ " <td>[Decimal('5.6'), Decimal('3.0'), Decimal('4.5'), Decimal('1.5')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>68</td>\n",
+ " <td>[Decimal('5.8'), Decimal('2.7'), Decimal('4.1'), Decimal('1.0')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>69</td>\n",
+ " <td>[Decimal('6.2'), Decimal('2.2'), Decimal('4.5'), Decimal('1.5')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>70</td>\n",
+ " <td>[Decimal('5.6'), Decimal('2.5'), Decimal('3.9'), Decimal('1.1')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>71</td>\n",
+ " <td>[Decimal('5.9'), Decimal('3.2'), Decimal('4.8'), Decimal('1.8')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>72</td>\n",
+ " <td>[Decimal('6.1'), Decimal('2.8'), Decimal('4.0'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>73</td>\n",
+ " <td>[Decimal('6.3'), Decimal('2.5'), Decimal('4.9'), Decimal('1.5')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>74</td>\n",
+ " <td>[Decimal('6.1'), Decimal('2.8'), Decimal('4.7'), Decimal('1.2')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>75</td>\n",
+ " <td>[Decimal('6.4'), Decimal('2.9'), Decimal('4.3'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>76</td>\n",
+ " <td>[Decimal('6.6'), Decimal('3.0'), Decimal('4.4'), Decimal('1.4')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>77</td>\n",
+ " <td>[Decimal('6.8'), Decimal('2.8'), Decimal('4.8'), Decimal('1.4')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>78</td>\n",
+ " <td>[Decimal('6.7'), Decimal('3.0'), Decimal('5.0'), Decimal('1.7')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>79</td>\n",
+ " <td>[Decimal('6.0'), Decimal('2.9'), Decimal('4.5'), Decimal('1.5')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>80</td>\n",
+ " <td>[Decimal('5.7'), Decimal('2.6'), Decimal('3.5'), Decimal('1.0')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>81</td>\n",
+ " <td>[Decimal('5.5'), Decimal('2.4'), Decimal('3.8'), Decimal('1.1')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>82</td>\n",
+ " <td>[Decimal('5.5'), Decimal('2.4'), Decimal('3.7'), Decimal('1.0')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>83</td>\n",
+ " <td>[Decimal('5.8'), Decimal('2.7'), Decimal('3.9'), Decimal('1.2')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>84</td>\n",
+ " <td>[Decimal('6.0'), Decimal('2.7'), Decimal('5.1'), Decimal('1.6')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>85</td>\n",
+ " <td>[Decimal('5.4'), Decimal('3.0'), Decimal('4.5'), Decimal('1.5')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>86</td>\n",
+ " <td>[Decimal('6.0'), Decimal('3.4'), Decimal('4.5'), Decimal('1.6')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>87</td>\n",
+ " <td>[Decimal('6.7'), Decimal('3.1'), Decimal('4.7'), Decimal('1.5')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>88</td>\n",
+ " <td>[Decimal('6.3'), Decimal('2.3'), Decimal('4.4'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>89</td>\n",
+ " <td>[Decimal('5.6'), Decimal('3.0'), Decimal('4.1'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>90</td>\n",
+ " <td>[Decimal('5.5'), Decimal('2.5'), Decimal('4.0'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>91</td>\n",
+ " <td>[Decimal('5.5'), Decimal('2.6'), Decimal('4.4'), Decimal('1.2')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>92</td>\n",
+ " <td>[Decimal('6.1'), Decimal('3.0'), Decimal('4.6'), Decimal('1.4')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>93</td>\n",
+ " <td>[Decimal('5.8'), Decimal('2.6'), Decimal('4.0'), Decimal('1.2')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>94</td>\n",
+ " <td>[Decimal('5.0'), Decimal('2.3'), Decimal('3.3'), Decimal('1.0')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>95</td>\n",
+ " <td>[Decimal('5.6'), Decimal('2.7'), Decimal('4.2'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>96</td>\n",
+ " <td>[Decimal('5.7'), Decimal('3.0'), Decimal('4.2'), Decimal('1.2')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>97</td>\n",
+ " <td>[Decimal('5.7'), Decimal('2.9'), Decimal('4.2'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>98</td>\n",
+ " <td>[Decimal('6.2'), Decimal('2.9'), Decimal('4.3'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>99</td>\n",
+ " <td>[Decimal('5.1'), Decimal('2.5'), Decimal('3.0'), Decimal('1.1')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>100</td>\n",
+ " <td>[Decimal('5.7'), Decimal('2.8'), Decimal('4.1'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>101</td>\n",
+ " <td>[Decimal('6.3'), Decimal('3.3'), Decimal('6.0'), Decimal('2.5')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>102</td>\n",
+ " <td>[Decimal('5.8'), Decimal('2.7'), Decimal('5.1'), Decimal('1.9')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>103</td>\n",
+ " <td>[Decimal('7.1'), Decimal('3.0'), Decimal('5.9'), Decimal('2.1')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>104</td>\n",
+ " <td>[Decimal('6.3'), Decimal('2.9'), Decimal('5.6'), Decimal('1.8')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>105</td>\n",
+ " <td>[Decimal('6.5'), Decimal('3.0'), Decimal('5.8'), Decimal('2.2')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>106</td>\n",
+ " <td>[Decimal('7.6'), Decimal('3.0'), Decimal('6.6'), Decimal('2.1')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>107</td>\n",
+ " <td>[Decimal('4.9'), Decimal('2.5'), Decimal('4.5'), Decimal('1.7')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>108</td>\n",
+ " <td>[Decimal('7.3'), Decimal('2.9'), Decimal('6.3'), Decimal('1.8')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>109</td>\n",
+ " <td>[Decimal('6.7'), Decimal('2.5'), Decimal('5.8'), Decimal('1.8')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>110</td>\n",
+ " <td>[Decimal('7.2'), Decimal('3.6'), Decimal('6.1'), Decimal('2.5')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>111</td>\n",
+ " <td>[Decimal('6.5'), Decimal('3.2'), Decimal('5.1'), Decimal('2.0')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>112</td>\n",
+ " <td>[Decimal('6.4'), Decimal('2.7'), Decimal('5.3'), Decimal('1.9')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>113</td>\n",
+ " <td>[Decimal('6.8'), Decimal('3.0'), Decimal('5.5'), Decimal('2.1')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>114</td>\n",
+ " <td>[Decimal('5.7'), Decimal('2.5'), Decimal('5.0'), Decimal('2.0')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>115</td>\n",
+ " <td>[Decimal('5.8'), Decimal('2.8'), Decimal('5.1'), Decimal('2.4')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>116</td>\n",
+ " <td>[Decimal('6.4'), Decimal('3.2'), Decimal('5.3'), Decimal('2.3')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>117</td>\n",
+ " <td>[Decimal('6.5'), Decimal('3.0'), Decimal('5.5'), Decimal('1.8')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>118</td>\n",
+ " <td>[Decimal('7.7'), Decimal('3.8'), Decimal('6.7'), Decimal('2.2')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>119</td>\n",
+ " <td>[Decimal('7.7'), Decimal('2.6'), Decimal('6.9'), Decimal('2.3')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>120</td>\n",
+ " <td>[Decimal('6.0'), Decimal('2.2'), Decimal('5.0'), Decimal('1.5')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>121</td>\n",
+ " <td>[Decimal('6.9'), Decimal('3.2'), Decimal('5.7'), Decimal('2.3')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>122</td>\n",
+ " <td>[Decimal('5.6'), Decimal('2.8'), Decimal('4.9'), Decimal('2.0')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>123</td>\n",
+ " <td>[Decimal('7.7'), Decimal('2.8'), Decimal('6.7'), Decimal('2.0')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>124</td>\n",
+ " <td>[Decimal('6.3'), Decimal('2.7'), Decimal('4.9'), Decimal('1.8')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>125</td>\n",
+ " <td>[Decimal('6.7'), Decimal('3.3'), Decimal('5.7'), Decimal('2.1')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>126</td>\n",
+ " <td>[Decimal('7.2'), Decimal('3.2'), Decimal('6.0'), Decimal('1.8')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>127</td>\n",
+ " <td>[Decimal('6.2'), Decimal('2.8'), Decimal('4.8'), Decimal('1.8')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>128</td>\n",
+ " <td>[Decimal('6.1'), Decimal('3.0'), Decimal('4.9'), Decimal('1.8')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>129</td>\n",
+ " <td>[Decimal('6.4'), Decimal('2.8'), Decimal('5.6'), Decimal('2.1')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>130</td>\n",
+ " <td>[Decimal('7.2'), Decimal('3.0'), Decimal('5.8'), Decimal('1.6')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>131</td>\n",
+ " <td>[Decimal('7.4'), Decimal('2.8'), Decimal('6.1'), Decimal('1.9')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>132</td>\n",
+ " <td>[Decimal('7.9'), Decimal('3.8'), Decimal('6.4'), Decimal('2.0')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>133</td>\n",
+ " <td>[Decimal('6.4'), Decimal('2.8'), Decimal('5.6'), Decimal('2.2')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>134</td>\n",
+ " <td>[Decimal('6.3'), Decimal('2.8'), Decimal('5.1'), Decimal('1.5')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>135</td>\n",
+ " <td>[Decimal('6.1'), Decimal('2.6'), Decimal('5.6'), Decimal('1.4')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>136</td>\n",
+ " <td>[Decimal('7.7'), Decimal('3.0'), Decimal('6.1'), Decimal('2.3')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>137</td>\n",
+ " <td>[Decimal('6.3'), Decimal('3.4'), Decimal('5.6'), Decimal('2.4')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>138</td>\n",
+ " <td>[Decimal('6.4'), Decimal('3.1'), Decimal('5.5'), Decimal('1.8')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>139</td>\n",
+ " <td>[Decimal('6.0'), Decimal('3.0'), Decimal('4.8'), Decimal('1.8')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>140</td>\n",
+ " <td>[Decimal('6.9'), Decimal('3.1'), Decimal('5.4'), Decimal('2.1')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>141</td>\n",
+ " <td>[Decimal('6.7'), Decimal('3.1'), Decimal('5.6'), Decimal('2.4')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>142</td>\n",
+ " <td>[Decimal('6.9'), Decimal('3.1'), Decimal('5.1'), Decimal('2.3')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>143</td>\n",
+ " <td>[Decimal('5.8'), Decimal('2.7'), Decimal('5.1'), Decimal('1.9')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>144</td>\n",
+ " <td>[Decimal('6.8'), Decimal('3.2'), Decimal('5.9'), Decimal('2.3')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>145</td>\n",
+ " <td>[Decimal('6.7'), Decimal('3.3'), Decimal('5.7'), Decimal('2.5')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>146</td>\n",
+ " <td>[Decimal('6.7'), Decimal('3.0'), Decimal('5.2'), Decimal('2.3')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>147</td>\n",
+ " <td>[Decimal('6.3'), Decimal('2.5'), Decimal('5.0'), Decimal('1.9')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>148</td>\n",
+ " <td>[Decimal('6.5'), Decimal('3.0'), Decimal('5.2'), Decimal('2.0')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>149</td>\n",
+ " <td>[Decimal('6.2'), Decimal('3.4'), Decimal('5.4'), Decimal('2.3')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>150</td>\n",
+ " <td>[Decimal('5.9'), Decimal('3.0'), Decimal('5.1'), Decimal('1.8')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(1, [Decimal('5.1'), Decimal('3.5'), Decimal('1.4'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (2, [Decimal('4.9'), Decimal('3.0'), Decimal('1.4'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (3, [Decimal('4.7'), Decimal('3.2'), Decimal('1.3'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (4, [Decimal('4.6'), Decimal('3.1'), Decimal('1.5'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (5, [Decimal('5.0'), Decimal('3.6'), Decimal('1.4'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (6, [Decimal('5.4'), Decimal('3.9'), Decimal('1.7'), Decimal('0.4')], u'Iris-setosa'),\n",
+ " (7, [Decimal('4.6'), Decimal('3.4'), Decimal('1.4'), Decimal('0.3')], u'Iris-setosa'),\n",
+ " (8, [Decimal('5.0'), Decimal('3.4'), Decimal('1.5'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (9, [Decimal('4.4'), Decimal('2.9'), Decimal('1.4'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (10, [Decimal('4.9'), Decimal('3.1'), Decimal('1.5'), Decimal('0.1')], u'Iris-setosa'),\n",
+ " (11, [Decimal('5.4'), Decimal('3.7'), Decimal('1.5'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (12, [Decimal('4.8'), Decimal('3.4'), Decimal('1.6'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (13, [Decimal('4.8'), Decimal('3.0'), Decimal('1.4'), Decimal('0.1')], u'Iris-setosa'),\n",
+ " (14, [Decimal('4.3'), Decimal('3.0'), Decimal('1.1'), Decimal('0.1')], u'Iris-setosa'),\n",
+ " (15, [Decimal('5.8'), Decimal('4.0'), Decimal('1.2'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (16, [Decimal('5.7'), Decimal('4.4'), Decimal('1.5'), Decimal('0.4')], u'Iris-setosa'),\n",
+ " (17, [Decimal('5.4'), Decimal('3.9'), Decimal('1.3'), Decimal('0.4')], u'Iris-setosa'),\n",
+ " (18, [Decimal('5.1'), Decimal('3.5'), Decimal('1.4'), Decimal('0.3')], u'Iris-setosa'),\n",
+ " (19, [Decimal('5.7'), Decimal('3.8'), Decimal('1.7'), Decimal('0.3')], u'Iris-setosa'),\n",
+ " (20, [Decimal('5.1'), Decimal('3.8'), Decimal('1.5'), Decimal('0.3')], u'Iris-setosa'),\n",
+ " (21, [Decimal('5.4'), Decimal('3.4'), Decimal('1.7'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (22, [Decimal('5.1'), Decimal('3.7'), Decimal('1.5'), Decimal('0.4')], u'Iris-setosa'),\n",
+ " (23, [Decimal('4.6'), Decimal('3.6'), Decimal('1.0'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (24, [Decimal('5.1'), Decimal('3.3'), Decimal('1.7'), Decimal('0.5')], u'Iris-setosa'),\n",
+ " (25, [Decimal('4.8'), Decimal('3.4'), Decimal('1.9'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (26, [Decimal('5.0'), Decimal('3.0'), Decimal('1.6'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (27, [Decimal('5.0'), Decimal('3.4'), Decimal('1.6'), Decimal('0.4')], u'Iris-setosa'),\n",
+ " (28, [Decimal('5.2'), Decimal('3.5'), Decimal('1.5'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (29, [Decimal('5.2'), Decimal('3.4'), Decimal('1.4'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (30, [Decimal('4.7'), Decimal('3.2'), Decimal('1.6'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (31, [Decimal('4.8'), Decimal('3.1'), Decimal('1.6'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (32, [Decimal('5.4'), Decimal('3.4'), Decimal('1.5'), Decimal('0.4')], u'Iris-setosa'),\n",
+ " (33, [Decimal('5.2'), Decimal('4.1'), Decimal('1.5'), Decimal('0.1')], u'Iris-setosa'),\n",
+ " (34, [Decimal('5.5'), Decimal('4.2'), Decimal('1.4'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (35, [Decimal('4.9'), Decimal('3.1'), Decimal('1.5'), Decimal('0.1')], u'Iris-setosa'),\n",
+ " (36, [Decimal('5.0'), Decimal('3.2'), Decimal('1.2'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (37, [Decimal('5.5'), Decimal('3.5'), Decimal('1.3'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (38, [Decimal('4.9'), Decimal('3.1'), Decimal('1.5'), Decimal('0.1')], u'Iris-setosa'),\n",
+ " (39, [Decimal('4.4'), Decimal('3.0'), Decimal('1.3'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (40, [Decimal('5.1'), Decimal('3.4'), Decimal('1.5'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (41, [Decimal('5.0'), Decimal('3.5'), Decimal('1.3'), Decimal('0.3')], u'Iris-setosa'),\n",
+ " (42, [Decimal('4.5'), Decimal('2.3'), Decimal('1.3'), Decimal('0.3')], u'Iris-setosa'),\n",
+ " (43, [Decimal('4.4'), Decimal('3.2'), Decimal('1.3'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (44, [Decimal('5.0'), Decimal('3.5'), Decimal('1.6'), Decimal('0.6')], u'Iris-setosa'),\n",
+ " (45, [Decimal('5.1'), Decimal('3.8'), Decimal('1.9'), Decimal('0.4')], u'Iris-setosa'),\n",
+ " (46, [Decimal('4.8'), Decimal('3.0'), Decimal('1.4'), Decimal('0.3')], u'Iris-setosa'),\n",
+ " (47, [Decimal('5.1'), Decimal('3.8'), Decimal('1.6'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (48, [Decimal('4.6'), Decimal('3.2'), Decimal('1.4'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (49, [Decimal('5.3'), Decimal('3.7'), Decimal('1.5'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (50, [Decimal('5.0'), Decimal('3.3'), Decimal('1.4'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (51, [Decimal('7.0'), Decimal('3.2'), Decimal('4.7'), Decimal('1.4')], u'Iris-versicolor'),\n",
+ " (52, [Decimal('6.4'), Decimal('3.2'), Decimal('4.5'), Decimal('1.5')], u'Iris-versicolor'),\n",
+ " (53, [Decimal('6.9'), Decimal('3.1'), Decimal('4.9'), Decimal('1.5')], u'Iris-versicolor'),\n",
+ " (54, [Decimal('5.5'), Decimal('2.3'), Decimal('4.0'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (55, [Decimal('6.5'), Decimal('2.8'), Decimal('4.6'), Decimal('1.5')], u'Iris-versicolor'),\n",
+ " (56, [Decimal('5.7'), Decimal('2.8'), Decimal('4.5'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (57, [Decimal('6.3'), Decimal('3.3'), Decimal('4.7'), Decimal('1.6')], u'Iris-versicolor'),\n",
+ " (58, [Decimal('4.9'), Decimal('2.4'), Decimal('3.3'), Decimal('1.0')], u'Iris-versicolor'),\n",
+ " (59, [Decimal('6.6'), Decimal('2.9'), Decimal('4.6'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (60, [Decimal('5.2'), Decimal('2.7'), Decimal('3.9'), Decimal('1.4')], u'Iris-versicolor'),\n",
+ " (61, [Decimal('5.0'), Decimal('2.0'), Decimal('3.5'), Decimal('1.0')], u'Iris-versicolor'),\n",
+ " (62, [Decimal('5.9'), Decimal('3.0'), Decimal('4.2'), Decimal('1.5')], u'Iris-versicolor'),\n",
+ " (63, [Decimal('6.0'), Decimal('2.2'), Decimal('4.0'), Decimal('1.0')], u'Iris-versicolor'),\n",
+ " (64, [Decimal('6.1'), Decimal('2.9'), Decimal('4.7'), Decimal('1.4')], u'Iris-versicolor'),\n",
+ " (65, [Decimal('5.6'), Decimal('2.9'), Decimal('3.6'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (66, [Decimal('6.7'), Decimal('3.1'), Decimal('4.4'), Decimal('1.4')], u'Iris-versicolor'),\n",
+ " (67, [Decimal('5.6'), Decimal('3.0'), Decimal('4.5'), Decimal('1.5')], u'Iris-versicolor'),\n",
+ " (68, [Decimal('5.8'), Decimal('2.7'), Decimal('4.1'), Decimal('1.0')], u'Iris-versicolor'),\n",
+ " (69, [Decimal('6.2'), Decimal('2.2'), Decimal('4.5'), Decimal('1.5')], u'Iris-versicolor'),\n",
+ " (70, [Decimal('5.6'), Decimal('2.5'), Decimal('3.9'), Decimal('1.1')], u'Iris-versicolor'),\n",
+ " (71, [Decimal('5.9'), Decimal('3.2'), Decimal('4.8'), Decimal('1.8')], u'Iris-versicolor'),\n",
+ " (72, [Decimal('6.1'), Decimal('2.8'), Decimal('4.0'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (73, [Decimal('6.3'), Decimal('2.5'), Decimal('4.9'), Decimal('1.5')], u'Iris-versicolor'),\n",
+ " (74, [Decimal('6.1'), Decimal('2.8'), Decimal('4.7'), Decimal('1.2')], u'Iris-versicolor'),\n",
+ " (75, [Decimal('6.4'), Decimal('2.9'), Decimal('4.3'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (76, [Decimal('6.6'), Decimal('3.0'), Decimal('4.4'), Decimal('1.4')], u'Iris-versicolor'),\n",
+ " (77, [Decimal('6.8'), Decimal('2.8'), Decimal('4.8'), Decimal('1.4')], u'Iris-versicolor'),\n",
+ " (78, [Decimal('6.7'), Decimal('3.0'), Decimal('5.0'), Decimal('1.7')], u'Iris-versicolor'),\n",
+ " (79, [Decimal('6.0'), Decimal('2.9'), Decimal('4.5'), Decimal('1.5')], u'Iris-versicolor'),\n",
+ " (80, [Decimal('5.7'), Decimal('2.6'), Decimal('3.5'), Decimal('1.0')], u'Iris-versicolor'),\n",
+ " (81, [Decimal('5.5'), Decimal('2.4'), Decimal('3.8'), Decimal('1.1')], u'Iris-versicolor'),\n",
+ " (82, [Decimal('5.5'), Decimal('2.4'), Decimal('3.7'), Decimal('1.0')], u'Iris-versicolor'),\n",
+ " (83, [Decimal('5.8'), Decimal('2.7'), Decimal('3.9'), Decimal('1.2')], u'Iris-versicolor'),\n",
+ " (84, [Decimal('6.0'), Decimal('2.7'), Decimal('5.1'), Decimal('1.6')], u'Iris-versicolor'),\n",
+ " (85, [Decimal('5.4'), Decimal('3.0'), Decimal('4.5'), Decimal('1.5')], u'Iris-versicolor'),\n",
+ " (86, [Decimal('6.0'), Decimal('3.4'), Decimal('4.5'), Decimal('1.6')], u'Iris-versicolor'),\n",
+ " (87, [Decimal('6.7'), Decimal('3.1'), Decimal('4.7'), Decimal('1.5')], u'Iris-versicolor'),\n",
+ " (88, [Decimal('6.3'), Decimal('2.3'), Decimal('4.4'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (89, [Decimal('5.6'), Decimal('3.0'), Decimal('4.1'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (90, [Decimal('5.5'), Decimal('2.5'), Decimal('4.0'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (91, [Decimal('5.5'), Decimal('2.6'), Decimal('4.4'), Decimal('1.2')], u'Iris-versicolor'),\n",
+ " (92, [Decimal('6.1'), Decimal('3.0'), Decimal('4.6'), Decimal('1.4')], u'Iris-versicolor'),\n",
+ " (93, [Decimal('5.8'), Decimal('2.6'), Decimal('4.0'), Decimal('1.2')], u'Iris-versicolor'),\n",
+ " (94, [Decimal('5.0'), Decimal('2.3'), Decimal('3.3'), Decimal('1.0')], u'Iris-versicolor'),\n",
+ " (95, [Decimal('5.6'), Decimal('2.7'), Decimal('4.2'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (96, [Decimal('5.7'), Decimal('3.0'), Decimal('4.2'), Decimal('1.2')], u'Iris-versicolor'),\n",
+ " (97, [Decimal('5.7'), Decimal('2.9'), Decimal('4.2'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (98, [Decimal('6.2'), Decimal('2.9'), Decimal('4.3'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (99, [Decimal('5.1'), Decimal('2.5'), Decimal('3.0'), Decimal('1.1')], u'Iris-versicolor'),\n",
+ " (100, [Decimal('5.7'), Decimal('2.8'), Decimal('4.1'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (101, [Decimal('6.3'), Decimal('3.3'), Decimal('6.0'), Decimal('2.5')], u'Iris-virginica'),\n",
+ " (102, [Decimal('5.8'), Decimal('2.7'), Decimal('5.1'), Decimal('1.9')], u'Iris-virginica'),\n",
+ " (103, [Decimal('7.1'), Decimal('3.0'), Decimal('5.9'), Decimal('2.1')], u'Iris-virginica'),\n",
+ " (104, [Decimal('6.3'), Decimal('2.9'), Decimal('5.6'), Decimal('1.8')], u'Iris-virginica'),\n",
+ " (105, [Decimal('6.5'), Decimal('3.0'), Decimal('5.8'), Decimal('2.2')], u'Iris-virginica'),\n",
+ " (106, [Decimal('7.6'), Decimal('3.0'), Decimal('6.6'), Decimal('2.1')], u'Iris-virginica'),\n",
+ " (107, [Decimal('4.9'), Decimal('2.5'), Decimal('4.5'), Decimal('1.7')], u'Iris-virginica'),\n",
+ " (108, [Decimal('7.3'), Decimal('2.9'), Decimal('6.3'), Decimal('1.8')], u'Iris-virginica'),\n",
+ " (109, [Decimal('6.7'), Decimal('2.5'), Decimal('5.8'), Decimal('1.8')], u'Iris-virginica'),\n",
+ " (110, [Decimal('7.2'), Decimal('3.6'), Decimal('6.1'), Decimal('2.5')], u'Iris-virginica'),\n",
+ " (111, [Decimal('6.5'), Decimal('3.2'), Decimal('5.1'), Decimal('2.0')], u'Iris-virginica'),\n",
+ " (112, [Decimal('6.4'), Decimal('2.7'), Decimal('5.3'), Decimal('1.9')], u'Iris-virginica'),\n",
+ " (113, [Decimal('6.8'), Decimal('3.0'), Decimal('5.5'), Decimal('2.1')], u'Iris-virginica'),\n",
+ " (114, [Decimal('5.7'), Decimal('2.5'), Decimal('5.0'), Decimal('2.0')], u'Iris-virginica'),\n",
+ " (115, [Decimal('5.8'), Decimal('2.8'), Decimal('5.1'), Decimal('2.4')], u'Iris-virginica'),\n",
+ " (116, [Decimal('6.4'), Decimal('3.2'), Decimal('5.3'), Decimal('2.3')], u'Iris-virginica'),\n",
+ " (117, [Decimal('6.5'), Decimal('3.0'), Decimal('5.5'), Decimal('1.8')], u'Iris-virginica'),\n",
+ " (118, [Decimal('7.7'), Decimal('3.8'), Decimal('6.7'), Decimal('2.2')], u'Iris-virginica'),\n",
+ " (119, [Decimal('7.7'), Decimal('2.6'), Decimal('6.9'), Decimal('2.3')], u'Iris-virginica'),\n",
+ " (120, [Decimal('6.0'), Decimal('2.2'), Decimal('5.0'), Decimal('1.5')], u'Iris-virginica'),\n",
+ " (121, [Decimal('6.9'), Decimal('3.2'), Decimal('5.7'), Decimal('2.3')], u'Iris-virginica'),\n",
+ " (122, [Decimal('5.6'), Decimal('2.8'), Decimal('4.9'), Decimal('2.0')], u'Iris-virginica'),\n",
+ " (123, [Decimal('7.7'), Decimal('2.8'), Decimal('6.7'), Decimal('2.0')], u'Iris-virginica'),\n",
+ " (124, [Decimal('6.3'), Decimal('2.7'), Decimal('4.9'), Decimal('1.8')], u'Iris-virginica'),\n",
+ " (125, [Decimal('6.7'), Decimal('3.3'), Decimal('5.7'), Decimal('2.1')], u'Iris-virginica'),\n",
+ " (126, [Decimal('7.2'), Decimal('3.2'), Decimal('6.0'), Decimal('1.8')], u'Iris-virginica'),\n",
+ " (127, [Decimal('6.2'), Decimal('2.8'), Decimal('4.8'), Decimal('1.8')], u'Iris-virginica'),\n",
+ " (128, [Decimal('6.1'), Decimal('3.0'), Decimal('4.9'), Decimal('1.8')], u'Iris-virginica'),\n",
+ " (129, [Decimal('6.4'), Decimal('2.8'), Decimal('5.6'), Decimal('2.1')], u'Iris-virginica'),\n",
+ " (130, [Decimal('7.2'), Decimal('3.0'), Decimal('5.8'), Decimal('1.6')], u'Iris-virginica'),\n",
+ " (131, [Decimal('7.4'), Decimal('2.8'), Decimal('6.1'), Decimal('1.9')], u'Iris-virginica'),\n",
+ " (132, [Decimal('7.9'), Decimal('3.8'), Decimal('6.4'), Decimal('2.0')], u'Iris-virginica'),\n",
+ " (133, [Decimal('6.4'), Decimal('2.8'), Decimal('5.6'), Decimal('2.2')], u'Iris-virginica'),\n",
+ " (134, [Decimal('6.3'), Decimal('2.8'), Decimal('5.1'), Decimal('1.5')], u'Iris-virginica'),\n",
+ " (135, [Decimal('6.1'), Decimal('2.6'), Decimal('5.6'), Decimal('1.4')], u'Iris-virginica'),\n",
+ " (136, [Decimal('7.7'), Decimal('3.0'), Decimal('6.1'), Decimal('2.3')], u'Iris-virginica'),\n",
+ " (137, [Decimal('6.3'), Decimal('3.4'), Decimal('5.6'), Decimal('2.4')], u'Iris-virginica'),\n",
+ " (138, [Decimal('6.4'), Decimal('3.1'), Decimal('5.5'), Decimal('1.8')], u'Iris-virginica'),\n",
+ " (139, [Decimal('6.0'), Decimal('3.0'), Decimal('4.8'), Decimal('1.8')], u'Iris-virginica'),\n",
+ " (140, [Decimal('6.9'), Decimal('3.1'), Decimal('5.4'), Decimal('2.1')], u'Iris-virginica'),\n",
+ " (141, [Decimal('6.7'), Decimal('3.1'), Decimal('5.6'), Decimal('2.4')], u'Iris-virginica'),\n",
+ " (142, [Decimal('6.9'), Decimal('3.1'), Decimal('5.1'), Decimal('2.3')], u'Iris-virginica'),\n",
+ " (143, [Decimal('5.8'), Decimal('2.7'), Decimal('5.1'), Decimal('1.9')], u'Iris-virginica'),\n",
+ " (144, [Decimal('6.8'), Decimal('3.2'), Decimal('5.9'), Decimal('2.3')], u'Iris-virginica'),\n",
+ " (145, [Decimal('6.7'), Decimal('3.3'), Decimal('5.7'), Decimal('2.5')], u'Iris-virginica'),\n",
+ " (146, [Decimal('6.7'), Decimal('3.0'), Decimal('5.2'), Decimal('2.3')], u'Iris-virginica'),\n",
+ " (147, [Decimal('6.3'), Decimal('2.5'), Decimal('5.0'), Decimal('1.9')], u'Iris-virginica'),\n",
+ " (148, [Decimal('6.5'), Decimal('3.0'), Decimal('5.2'), Decimal('2.0')], u'Iris-virginica'),\n",
+ " (149, [Decimal('6.2'), Decimal('3.4'), Decimal('5.4'), Decimal('2.3')], u'Iris-virginica'),\n",
+ " (150, [Decimal('5.9'), Decimal('3.0'), Decimal('5.1'), Decimal('1.8')], u'Iris-virginica')]"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql \n",
+ "DROP TABLE IF EXISTS iris_data;\n",
+ "\n",
+ "CREATE TABLE iris_data(\n",
+ " id serial,\n",
+ " attributes numeric[],\n",
+ " class_text varchar\n",
+ ");\n",
+ "\n",
+ "INSERT INTO iris_data(id, attributes, class_text) VALUES\n",
+ "(1,ARRAY[5.1,3.5,1.4,0.2],'Iris-setosa'),\n",
+ "(2,ARRAY[4.9,3.0,1.4,0.2],'Iris-setosa'),\n",
+ "(3,ARRAY[4.7,3.2,1.3,0.2],'Iris-setosa'),\n",
+ "(4,ARRAY[4.6,3.1,1.5,0.2],'Iris-setosa'),\n",
+ "(5,ARRAY[5.0,3.6,1.4,0.2],'Iris-setosa'),\n",
+ "(6,ARRAY[5.4,3.9,1.7,0.4],'Iris-setosa'),\n",
+ "(7,ARRAY[4.6,3.4,1.4,0.3],'Iris-setosa'),\n",
+ "(8,ARRAY[5.0,3.4,1.5,0.2],'Iris-setosa'),\n",
+ "(9,ARRAY[4.4,2.9,1.4,0.2],'Iris-setosa'),\n",
+ "(10,ARRAY[4.9,3.1,1.5,0.1],'Iris-setosa'),\n",
+ "(11,ARRAY[5.4,3.7,1.5,0.2],'Iris-setosa'),\n",
+ "(12,ARRAY[4.8,3.4,1.6,0.2],'Iris-setosa'),\n",
+ "(13,ARRAY[4.8,3.0,1.4,0.1],'Iris-setosa'),\n",
+ "(14,ARRAY[4.3,3.0,1.1,0.1],'Iris-setosa'),\n",
+ "(15,ARRAY[5.8,4.0,1.2,0.2],'Iris-setosa'),\n",
+ "(16,ARRAY[5.7,4.4,1.5,0.4],'Iris-setosa'),\n",
+ "(17,ARRAY[5.4,3.9,1.3,0.4],'Iris-setosa'),\n",
+ "(18,ARRAY[5.1,3.5,1.4,0.3],'Iris-setosa'),\n",
+ "(19,ARRAY[5.7,3.8,1.7,0.3],'Iris-setosa'),\n",
+ "(20,ARRAY[5.1,3.8,1.5,0.3],'Iris-setosa'),\n",
+ "(21,ARRAY[5.4,3.4,1.7,0.2],'Iris-setosa'),\n",
+ "(22,ARRAY[5.1,3.7,1.5,0.4],'Iris-setosa'),\n",
+ "(23,ARRAY[4.6,3.6,1.0,0.2],'Iris-setosa'),\n",
+ "(24,ARRAY[5.1,3.3,1.7,0.5],'Iris-setosa'),\n",
+ "(25,ARRAY[4.8,3.4,1.9,0.2],'Iris-setosa'),\n",
+ "(26,ARRAY[5.0,3.0,1.6,0.2],'Iris-setosa'),\n",
+ "(27,ARRAY[5.0,3.4,1.6,0.4],'Iris-setosa'),\n",
+ "(28,ARRAY[5.2,3.5,1.5,0.2],'Iris-setosa'),\n",
+ "(29,ARRAY[5.2,3.4,1.4,0.2],'Iris-setosa'),\n",
+ "(30,ARRAY[4.7,3.2,1.6,0.2],'Iris-setosa'),\n",
+ "(31,ARRAY[4.8,3.1,1.6,0.2],'Iris-setosa'),\n",
+ "(32,ARRAY[5.4,3.4,1.5,0.4],'Iris-setosa'),\n",
+ "(33,ARRAY[5.2,4.1,1.5,0.1],'Iris-setosa'),\n",
+ "(34,ARRAY[5.5,4.2,1.4,0.2],'Iris-setosa'),\n",
+ "(35,ARRAY[4.9,3.1,1.5,0.1],'Iris-setosa'),\n",
+ "(36,ARRAY[5.0,3.2,1.2,0.2],'Iris-setosa'),\n",
+ "(37,ARRAY[5.5,3.5,1.3,0.2],'Iris-setosa'),\n",
+ "(38,ARRAY[4.9,3.1,1.5,0.1],'Iris-setosa'),\n",
+ "(39,ARRAY[4.4,3.0,1.3,0.2],'Iris-setosa'),\n",
+ "(40,ARRAY[5.1,3.4,1.5,0.2],'Iris-setosa'),\n",
+ "(41,ARRAY[5.0,3.5,1.3,0.3],'Iris-setosa'),\n",
+ "(42,ARRAY[4.5,2.3,1.3,0.3],'Iris-setosa'),\n",
+ "(43,ARRAY[4.4,3.2,1.3,0.2],'Iris-setosa'),\n",
+ "(44,ARRAY[5.0,3.5,1.6,0.6],'Iris-setosa'),\n",
+ "(45,ARRAY[5.1,3.8,1.9,0.4],'Iris-setosa'),\n",
+ "(46,ARRAY[4.8,3.0,1.4,0.3],'Iris-setosa'),\n",
+ "(47,ARRAY[5.1,3.8,1.6,0.2],'Iris-setosa'),\n",
+ "(48,ARRAY[4.6,3.2,1.4,0.2],'Iris-setosa'),\n",
+ "(49,ARRAY[5.3,3.7,1.5,0.2],'Iris-setosa'),\n",
+ "(50,ARRAY[5.0,3.3,1.4,0.2],'Iris-setosa'),\n",
+ "(51,ARRAY[7.0,3.2,4.7,1.4],'Iris-versicolor'),\n",
+ "(52,ARRAY[6.4,3.2,4.5,1.5],'Iris-versicolor'),\n",
+ "(53,ARRAY[6.9,3.1,4.9,1.5],'Iris-versicolor'),\n",
+ "(54,ARRAY[5.5,2.3,4.0,1.3],'Iris-versicolor'),\n",
+ "(55,ARRAY[6.5,2.8,4.6,1.5],'Iris-versicolor'),\n",
+ "(56,ARRAY[5.7,2.8,4.5,1.3],'Iris-versicolor'),\n",
+ "(57,ARRAY[6.3,3.3,4.7,1.6],'Iris-versicolor'),\n",
+ "(58,ARRAY[4.9,2.4,3.3,1.0],'Iris-versicolor'),\n",
+ "(59,ARRAY[6.6,2.9,4.6,1.3],'Iris-versicolor'),\n",
+ "(60,ARRAY[5.2,2.7,3.9,1.4],'Iris-versicolor'),\n",
+ "(61,ARRAY[5.0,2.0,3.5,1.0],'Iris-versicolor'),\n",
+ "(62,ARRAY[5.9,3.0,4.2,1.5],'Iris-versicolor'),\n",
+ "(63,ARRAY[6.0,2.2,4.0,1.0],'Iris-versicolor'),\n",
+ "(64,ARRAY[6.1,2.9,4.7,1.4],'Iris-versicolor'),\n",
+ "(65,ARRAY[5.6,2.9,3.6,1.3],'Iris-versicolor'),\n",
+ "(66,ARRAY[6.7,3.1,4.4,1.4],'Iris-versicolor'),\n",
+ "(67,ARRAY[5.6,3.0,4.5,1.5],'Iris-versicolor'),\n",
+ "(68,ARRAY[5.8,2.7,4.1,1.0],'Iris-versicolor'),\n",
+ "(69,ARRAY[6.2,2.2,4.5,1.5],'Iris-versicolor'),\n",
+ "(70,ARRAY[5.6,2.5,3.9,1.1],'Iris-versicolor'),\n",
+ "(71,ARRAY[5.9,3.2,4.8,1.8],'Iris-versicolor'),\n",
+ "(72,ARRAY[6.1,2.8,4.0,1.3],'Iris-versicolor'),\n",
+ "(73,ARRAY[6.3,2.5,4.9,1.5],'Iris-versicolor'),\n",
+ "(74,ARRAY[6.1,2.8,4.7,1.2],'Iris-versicolor'),\n",
+ "(75,ARRAY[6.4,2.9,4.3,1.3],'Iris-versicolor'),\n",
+ "(76,ARRAY[6.6,3.0,4.4,1.4],'Iris-versicolor'),\n",
+ "(77,ARRAY[6.8,2.8,4.8,1.4],'Iris-versicolor'),\n",
+ "(78,ARRAY[6.7,3.0,5.0,1.7],'Iris-versicolor'),\n",
+ "(79,ARRAY[6.0,2.9,4.5,1.5],'Iris-versicolor'),\n",
+ "(80,ARRAY[5.7,2.6,3.5,1.0],'Iris-versicolor'),\n",
+ "(81,ARRAY[5.5,2.4,3.8,1.1],'Iris-versicolor'),\n",
+ "(82,ARRAY[5.5,2.4,3.7,1.0],'Iris-versicolor'),\n",
+ "(83,ARRAY[5.8,2.7,3.9,1.2],'Iris-versicolor'),\n",
+ "(84,ARRAY[6.0,2.7,5.1,1.6],'Iris-versicolor'),\n",
+ "(85,ARRAY[5.4,3.0,4.5,1.5],'Iris-versicolor'),\n",
+ "(86,ARRAY[6.0,3.4,4.5,1.6],'Iris-versicolor'),\n",
+ "(87,ARRAY[6.7,3.1,4.7,1.5],'Iris-versicolor'),\n",
+ "(88,ARRAY[6.3,2.3,4.4,1.3],'Iris-versicolor'),\n",
+ "(89,ARRAY[5.6,3.0,4.1,1.3],'Iris-versicolor'),\n",
+ "(90,ARRAY[5.5,2.5,4.0,1.3],'Iris-versicolor'),\n",
+ "(91,ARRAY[5.5,2.6,4.4,1.2],'Iris-versicolor'),\n",
+ "(92,ARRAY[6.1,3.0,4.6,1.4],'Iris-versicolor'),\n",
+ "(93,ARRAY[5.8,2.6,4.0,1.2],'Iris-versicolor'),\n",
+ "(94,ARRAY[5.0,2.3,3.3,1.0],'Iris-versicolor'),\n",
+ "(95,ARRAY[5.6,2.7,4.2,1.3],'Iris-versicolor'),\n",
+ "(96,ARRAY[5.7,3.0,4.2,1.2],'Iris-versicolor'),\n",
+ "(97,ARRAY[5.7,2.9,4.2,1.3],'Iris-versicolor'),\n",
+ "(98,ARRAY[6.2,2.9,4.3,1.3],'Iris-versicolor'),\n",
+ "(99,ARRAY[5.1,2.5,3.0,1.1],'Iris-versicolor'),\n",
+ "(100,ARRAY[5.7,2.8,4.1,1.3],'Iris-versicolor'),\n",
+ "(101,ARRAY[6.3,3.3,6.0,2.5],'Iris-virginica'),\n",
+ "(102,ARRAY[5.8,2.7,5.1,1.9],'Iris-virginica'),\n",
+ "(103,ARRAY[7.1,3.0,5.9,2.1],'Iris-virginica'),\n",
+ "(104,ARRAY[6.3,2.9,5.6,1.8],'Iris-virginica'),\n",
+ "(105,ARRAY[6.5,3.0,5.8,2.2],'Iris-virginica'),\n",
+ "(106,ARRAY[7.6,3.0,6.6,2.1],'Iris-virginica'),\n",
+ "(107,ARRAY[4.9,2.5,4.5,1.7],'Iris-virginica'),\n",
+ "(108,ARRAY[7.3,2.9,6.3,1.8],'Iris-virginica'),\n",
+ "(109,ARRAY[6.7,2.5,5.8,1.8],'Iris-virginica'),\n",
+ "(110,ARRAY[7.2,3.6,6.1,2.5],'Iris-virginica'),\n",
+ "(111,ARRAY[6.5,3.2,5.1,2.0],'Iris-virginica'),\n",
+ "(112,ARRAY[6.4,2.7,5.3,1.9],'Iris-virginica'),\n",
+ "(113,ARRAY[6.8,3.0,5.5,2.1],'Iris-virginica'),\n",
+ "(114,ARRAY[5.7,2.5,5.0,2.0],'Iris-virginica'),\n",
+ "(115,ARRAY[5.8,2.8,5.1,2.4],'Iris-virginica'),\n",
+ "(116,ARRAY[6.4,3.2,5.3,2.3],'Iris-virginica'),\n",
+ "(117,ARRAY[6.5,3.0,5.5,1.8],'Iris-virginica'),\n",
+ "(118,ARRAY[7.7,3.8,6.7,2.2],'Iris-virginica'),\n",
+ "(119,ARRAY[7.7,2.6,6.9,2.3],'Iris-virginica'),\n",
+ "(120,ARRAY[6.0,2.2,5.0,1.5],'Iris-virginica'),\n",
+ "(121,ARRAY[6.9,3.2,5.7,2.3],'Iris-virginica'),\n",
+ "(122,ARRAY[5.6,2.8,4.9,2.0],'Iris-virginica'),\n",
+ "(123,ARRAY[7.7,2.8,6.7,2.0],'Iris-virginica'),\n",
+ "(124,ARRAY[6.3,2.7,4.9,1.8],'Iris-virginica'),\n",
+ "(125,ARRAY[6.7,3.3,5.7,2.1],'Iris-virginica'),\n",
+ "(126,ARRAY[7.2,3.2,6.0,1.8],'Iris-virginica'),\n",
+ "(127,ARRAY[6.2,2.8,4.8,1.8],'Iris-virginica'),\n",
+ "(128,ARRAY[6.1,3.0,4.9,1.8],'Iris-virginica'),\n",
+ "(129,ARRAY[6.4,2.8,5.6,2.1],'Iris-virginica'),\n",
+ "(130,ARRAY[7.2,3.0,5.8,1.6],'Iris-virginica'),\n",
+ "(131,ARRAY[7.4,2.8,6.1,1.9],'Iris-virginica'),\n",
+ "(132,ARRAY[7.9,3.8,6.4,2.0],'Iris-virginica'),\n",
+ "(133,ARRAY[6.4,2.8,5.6,2.2],'Iris-virginica'),\n",
+ "(134,ARRAY[6.3,2.8,5.1,1.5],'Iris-virginica'),\n",
+ "(135,ARRAY[6.1,2.6,5.6,1.4],'Iris-virginica'),\n",
+ "(136,ARRAY[7.7,3.0,6.1,2.3],'Iris-virginica'),\n",
+ "(137,ARRAY[6.3,3.4,5.6,2.4],'Iris-virginica'),\n",
+ "(138,ARRAY[6.4,3.1,5.5,1.8],'Iris-virginica'),\n",
+ "(139,ARRAY[6.0,3.0,4.8,1.8],'Iris-virginica'),\n",
+ "(140,ARRAY[6.9,3.1,5.4,2.1],'Iris-virginica'),\n",
+ "(141,ARRAY[6.7,3.1,5.6,2.4],'Iris-virginica'),\n",
+ "(142,ARRAY[6.9,3.1,5.1,2.3],'Iris-virginica'),\n",
+ "(143,ARRAY[5.8,2.7,5.1,1.9],'Iris-virginica'),\n",
+ "(144,ARRAY[6.8,3.2,5.9,2.3],'Iris-virginica'),\n",
+ "(145,ARRAY[6.7,3.3,5.7,2.5],'Iris-virginica'),\n",
+ "(146,ARRAY[6.7,3.0,5.2,2.3],'Iris-virginica'),\n",
+ "(147,ARRAY[6.3,2.5,5.0,1.9],'Iris-virginica'),\n",
+ "(148,ARRAY[6.5,3.0,5.2,2.0],'Iris-virginica'),\n",
+ "(149,ARRAY[6.2,3.4,5.4,2.3],'Iris-virginica'),\n",
+ "(150,ARRAY[5.9,3.0,5.1,1.8],'Iris-virginica');\n",
+ "\n",
+ "SELECT * FROM iris_data ORDER BY id;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Create a test/validation dataset from the training data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>120</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(120L,)]"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS iris_train, iris_test;\n",
+ "\n",
+ "-- Set seed so results are reproducible\n",
+ "SELECT setseed(0);\n",
+ "\n",
+ "SELECT madlib.train_test_split('iris_data', -- Source table\n",
+ " 'iris', -- Output table root name\n",
+ " 0.8, -- Train proportion\n",
+ " NULL, -- Test proportion (0.2)\n",
+ " NULL, -- Strata definition\n",
+ " NULL, -- Output all columns\n",
+ " NULL, -- Sample without replacement\n",
+ " TRUE -- Separate output tables\n",
+ " );\n",
+ "\n",
+ "SELECT COUNT(*) FROM iris_train;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"pp\"></a>\n",
+ "# 2. Call preprocessor for deep learning\n",
+ "Training dataset (uses training preprocessor):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "2 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>independent_var_shape</th>\n",
+ " <th>dependent_var_shape</th>\n",
+ " <th>buffer_id</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[60, 4]</td>\n",
+ " <td>[60, 3]</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[60, 4]</td>\n",
+ " <td>[60, 3]</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[([60, 4], [60, 3], 0), ([60, 4], [60, 3], 1)]"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS iris_train_packed, iris_train_packed_summary;\n",
+ "\n",
+ "SELECT madlib.training_preprocessor_dl('iris_train', -- Source table\n",
+ " 'iris_train_packed', -- Output table\n",
+ " 'class_text', -- Dependent variable\n",
+ " 'attributes' -- Independent variable\n",
+ " ); \n",
+ "\n",
+ "SELECT independent_var_shape, dependent_var_shape, buffer_id FROM iris_train_packed ORDER BY buffer_id;"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>source_table</th>\n",
+ " <th>output_table</th>\n",
+ " <th>dependent_varname</th>\n",
+ " <th>independent_varname</th>\n",
+ " <th>dependent_vartype</th>\n",
+ " <th>class_values</th>\n",
+ " <th>buffer_size</th>\n",
+ " <th>normalizing_const</th>\n",
+ " <th>num_classes</th>\n",
+ " <th>distribution_rules</th>\n",
+ " <th>__internal_gpu_config__</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>iris_train</td>\n",
+ " <td>iris_train_packed</td>\n",
+ " <td>class_text</td>\n",
+ " <td>attributes</td>\n",
+ " <td>character varying</td>\n",
+ " <td>[u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica']</td>\n",
+ " <td>60</td>\n",
+ " <td>1.0</td>\n",
+ " <td>3</td>\n",
+ " <td>all_segments</td>\n",
+ " <td>all_segments</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'iris_train', u'iris_train_packed', u'class_text', u'attributes', u'character varying', [u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica'], 60, 1.0, 3, 'all_segments', 'all_segments')]"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT * FROM iris_train_packed_summary;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Validation dataset (uses validation preprocessor):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "2 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>independent_var_shape</th>\n",
+ " <th>dependent_var_shape</th>\n",
+ " <th>buffer_id</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[15, 4]</td>\n",
+ " <td>[15, 3]</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[15, 4]</td>\n",
+ " <td>[15, 3]</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[([15, 4], [15, 3], 0), ([15, 4], [15, 3], 1)]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS iris_test_packed, iris_test_packed_summary;\n",
+ "\n",
+ "SELECT madlib.validation_preprocessor_dl('iris_test', -- Source table\n",
+ " 'iris_test_packed', -- Output table\n",
+ " 'class_text', -- Dependent variable\n",
+ " 'attributes', -- Independent variable\n",
+ " 'iris_train_packed' -- From training preprocessor step\n",
+ " ); \n",
+ "\n",
+ "SELECT independent_var_shape, dependent_var_shape, buffer_id FROM iris_test_packed ORDER BY buffer_id;"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>source_table</th>\n",
+ " <th>output_table</th>\n",
+ " <th>dependent_varname</th>\n",
+ " <th>independent_varname</th>\n",
+ " <th>dependent_vartype</th>\n",
+ " <th>class_values</th>\n",
+ " <th>buffer_size</th>\n",
+ " <th>normalizing_const</th>\n",
+ " <th>num_classes</th>\n",
+ " <th>distribution_rules</th>\n",
+ " <th>__internal_gpu_config__</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>iris_test</td>\n",
+ " <td>iris_test_packed</td>\n",
+ " <td>class_text</td>\n",
+ " <td>attributes</td>\n",
+ " <td>character varying</td>\n",
+ " <td>[u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica']</td>\n",
+ " <td>15</td>\n",
+ " <td>1.0</td>\n",
+ " <td>3</td>\n",
+ " <td>all_segments</td>\n",
+ " <td>all_segments</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'iris_test', u'iris_test_packed', u'class_text', u'attributes', u'character varying', [u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica'], 15, 1.0, 3, 'all_segments', 'all_segments')]"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT * FROM iris_test_packed_summary;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"load\"></a>\n",
+ "# 3. Define and load model architecture\n",
+ "Import Keras libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Using TensorFlow backend.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Couldn't import dot_parser, loading of dot files will not be possible.\n"
+ ]
+ }
+ ],
+ "source": [
+ "import keras\n",
+ "from keras.models import Sequential\n",
+ "from keras.layers import Dense"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Define model architecture with 1 hidden layer:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "_________________________________________________________________\n",
+ "Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ "dense_1 (Dense) (None, 10) 50 \n",
+ "_________________________________________________________________\n",
+ "dense_2 (Dense) (None, 10) 110 \n",
+ "_________________________________________________________________\n",
+ "dense_3 (Dense) (None, 3) 33 \n",
+ "=================================================================\n",
+ "Total params: 193\n",
+ "Trainable params: 193\n",
+ "Non-trainable params: 0\n",
+ "_________________________________________________________________\n"
+ ]
+ }
+ ],
+ "source": [
+ "model1 = Sequential()\n",
+ "model1.add(Dense(10, activation='relu', input_shape=(4,)))\n",
+ "model1.add(Dense(10, activation='relu'))\n",
+ "model1.add(Dense(3, activation='softmax'))\n",
+ " \n",
+ "model1.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'{\"class_name\": \"Sequential\", \"keras_version\": \"2.1.6\", \"config\": [{\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_1\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"batch_input_shape\": [null, 4], \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_2\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_3\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"softmax\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 3, \"use_bias\": true, \"activity_regularizer\": null}}], \"backend\": \"tensorflow\"}'"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model1.to_json()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Define model architecture with 2 hidden layers:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "_________________________________________________________________\n",
+ "Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ "dense_4 (Dense) (None, 10) 50 \n",
+ "_________________________________________________________________\n",
+ "dense_5 (Dense) (None, 10) 110 \n",
+ "_________________________________________________________________\n",
+ "dense_6 (Dense) (None, 10) 110 \n",
+ "_________________________________________________________________\n",
+ "dense_7 (Dense) (None, 3) 33 \n",
+ "=================================================================\n",
+ "Total params: 303\n",
+ "Trainable params: 303\n",
+ "Non-trainable params: 0\n",
+ "_________________________________________________________________\n"
+ ]
+ }
+ ],
+ "source": [
+ "model2 = Sequential()\n",
+ "model2.add(Dense(10, activation='relu', input_shape=(4,)))\n",
+ "model2.add(Dense(10, activation='relu'))\n",
+ "model2.add(Dense(10, activation='relu'))\n",
+ "model2.add(Dense(3, activation='softmax'))\n",
+ " \n",
+ "model2.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'{\"class_name\": \"Sequential\", \"keras_version\": \"2.1.6\", \"config\": [{\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_4\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"batch_input_shape\": [null, 4], \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_5\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_6\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_7\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"softmax\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 3, \"use_bias\": true, \"activity_regularizer\": null}}], \"backend\": \"tensorflow\"}'"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model2.to_json()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Load into model architecture table"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "2 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>model_id</th>\n",
+ " <th>model_arch</th>\n",
+ " <th>model_weights</th>\n",
+ " <th>name</th>\n",
+ " <th>description</th>\n",
+ " <th>__internal_madlib_id__</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>{u'class_name': u'Sequential', u'keras_version': u'2.1.6', u'config': [{u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_1', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'dtype': u'float32', u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'batch_input_shape': [None, 4], u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_2', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_3', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'softmax', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 3, u'use_bias': True, u'activity_regularizer': None}}], u'backend': u'tensorflow'}</td>\n",
+ " <td>None</td>\n",
+ " <td>Sophie</td>\n",
+ " <td>MLP with 1 hidden layer</td>\n",
+ " <td>__madlib_temp_96702431_1576708421_6956281__</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>{u'class_name': u'Sequential', u'keras_version': u'2.1.6', u'config': [{u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_4', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'dtype': u'float32', u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'batch_input_shape': [None, 4], u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_5', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_6', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_7', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'softmax', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 3, u'use_bias': True, u'activity_regularizer': None}}], u'backend': u'tensorflow'}</td>\n",
+ " <td>None</td>\n",
+ " <td>Maria</td>\n",
+ " <td>MLP with 2 hidden layers</td>\n",
+ " <td>__madlib_temp_85244704_1576708422_1853942__</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(1, {u'class_name': u'Sequential', u'keras_version': u'2.1.6', u'config': [{u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_1', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'dtype': u'float32', u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'batch_input_shape': [None, 4], u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_2', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_3', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'softmax', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 3, u'use_bias': True, u'activity_regularizer': None}}], u'backend': u'tensorflow'}, None, u'Sophie', u'MLP with 1 hidden layer', u'__madlib_temp_96702431_1576708421_6956281__'),\n",
+ " (2, {u'class_name': u'Sequential', u'keras_version': u'2.1.6', u'config': [{u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_4', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'dtype': u'float32', u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'batch_input_shape': [None, 4], u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_5', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_6', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_7', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'softmax', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 3, u'use_bias': True, u'activity_regularizer': None}}], u'backend': u'tensorflow'}, None, u'Maria', u'MLP with 2 hidden layers', u'__madlib_temp_85244704_1576708422_1853942__')]"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS model_arch_library;\n",
+ "\n",
+ "SELECT madlib.load_keras_model('model_arch_library', -- Output table,\n",
+ " \n",
+ "$$\n",
+ "{\"class_name\": \"Sequential\", \"keras_version\": \"2.1.6\", \"config\": [{\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_1\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"batch_input_shape\": [null, 4], \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_2\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_3\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"softmax\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 3, \"use_bias\": true, \"activity_regularizer\": null}}], \"backend\": \"tensorflow\"}\n",
+ "$$\n",
+ "::json, -- JSON blob\n",
+ " NULL, -- Weights\n",
+ " 'Sophie', -- Name\n",
+ " 'MLP with 1 hidden layer' -- Descr\n",
+ ");\n",
+ "\n",
+ "SELECT madlib.load_keras_model('model_arch_library', -- Output table,\n",
+ " \n",
+ "$$\n",
+ "{\"class_name\": \"Sequential\", \"keras_version\": \"2.1.6\", \"config\": [{\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_4\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"batch_input_shape\": [null, 4], \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_5\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_6\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_7\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"softmax\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 3, \"use_bias\": true, \"activity_regularizer\": null}}], \"backend\": \"tensorflow\"}\n",
+ "$$\n",
+ "::json, -- JSON blob\n",
+ " NULL, -- Weights\n",
+ " 'Maria', -- Name\n",
+ " 'MLP with 2 hidden layers' -- Descr\n",
+ ");\n",
+ "\n",
+ "SELECT * FROM model_arch_library ORDER BY model_id;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"def_mst\"></a>\n",
+ "# 4. Define and load model selection tuples"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Select the model(s) from the model architecture table that you want to run, along with the compile and fit parameters. Permutations will be created for the set of model selection parameters will be loaded:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "12 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>mst_key</th>\n",
+ " <th>model_id</th>\n",
+ " <th>compile_params</th>\n",
+ " <th>fit_params</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=4,epochs=1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=8,epochs=1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=4,epochs=1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>4</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=8,epochs=1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>5</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=4,epochs=1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>6</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=8,epochs=1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>7</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=4,epochs=1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>8</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=8,epochs=1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>9</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=4,epochs=1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>10</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=8,epochs=1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>11</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=4,epochs=1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>12</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=8,epochs=1</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(1, 1, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']\", u'batch_size=4,epochs=1'),\n",
+ " (2, 1, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']\", u'batch_size=8,epochs=1'),\n",
+ " (3, 1, u\"loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']\", u'batch_size=4,epochs=1'),\n",
+ " (4, 1, u\"loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']\", u'batch_size=8,epochs=1'),\n",
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+ " (7, 2, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']\", u'batch_size=4,epochs=1'),\n",
+ " (8, 2, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']\", u'batch_size=8,epochs=1'),\n",
+ " (9, 2, u\"loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']\", u'batch_size=4,epochs=1'),\n",
+ " (10, 2, u\"loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']\", u'batch_size=8,epochs=1'),\n",
+ " (11, 2, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']\", u'batch_size=4,epochs=1'),\n",
+ " (12, 2, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']\", u'batch_size=8,epochs=1')]"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS mst_table, mst_table_summary;\n",
+ "\n",
+ "SELECT madlib.load_model_selection_table('model_arch_library', -- model architecture table\n",
+ " 'mst_table', -- model selection table output\n",
+ " ARRAY[1,2], -- model ids from model architecture table\n",
+ " ARRAY[ -- compile params\n",
+ " $$loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']$$,\n",
+ " $$loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']$$,\n",
+ " $$loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']$$\n",
+ " ],\n",
+ " ARRAY[ -- fit params\n",
+ " $$batch_size=4,epochs=1$$,\n",
+ " $$batch_size=8,epochs=1$$\n",
+ " ]\n",
+ " );\n",
+ " \n",
+ "SELECT * FROM mst_table ORDER BY mst_key;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This is the name of the model architecture table that corresponds to the model selection table:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>model_arch_table</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>model_arch_library</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'model_arch_library',)]"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT * FROM mst_table_summary;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"train\"></a>\n",
+ "# 5. Train\n",
+ "Train multiple models:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>madlib_keras_fit_multiple_model</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td></td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[('',)]"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS iris_multi_model, iris_multi_model_summary, iris_multi_model_info;\n",
+ "\n",
+ "SELECT madlib.madlib_keras_fit_multiple_model('iris_train_packed', -- source_table\n",
+ " 'iris_multi_model', -- model_output_table\n",
+ " 'mst_table', -- model_selection_table\n",
+ " 10, -- num_iterations\n",
+ " FALSE -- use gpus\n",
+ " );"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "View the model summary:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>source_table</th>\n",
+ " <th>validation_table</th>\n",
+ " <th>model</th>\n",
+ " <th>model_info</th>\n",
+ " <th>dependent_varname</th>\n",
+ " <th>independent_varname</th>\n",
+ " <th>model_arch_table</th>\n",
+ " <th>num_iterations</th>\n",
+ " <th>metrics_compute_frequency</th>\n",
+ " <th>warm_start</th>\n",
+ " <th>name</th>\n",
+ " <th>description</th>\n",
+ " <th>start_training_time</th>\n",
+ " <th>end_training_time</th>\n",
+ " <th>madlib_version</th>\n",
+ " <th>num_classes</th>\n",
+ " <th>class_values</th>\n",
+ " <th>dependent_vartype</th>\n",
+ " <th>normalizing_const</th>\n",
+ " <th>metrics_iters</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>iris_train_packed</td>\n",
+ " <td>None</td>\n",
+ " <td>iris_multi_model</td>\n",
+ " <td>iris_multi_model_info</td>\n",
+ " <td>class_text</td>\n",
+ " <td>attributes</td>\n",
+ " <td>model_arch_library</td>\n",
+ " <td>10</td>\n",
+ " <td>10</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>2019-12-18 22:33:49.706384</td>\n",
+ " <td>2019-12-18 22:35:34.547961</td>\n",
+ " <td>1.17-dev</td>\n",
+ " <td>3</td>\n",
+ " <td>[u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica']</td>\n",
+ " <td>character varying</td>\n",
+ " <td>1.0</td>\n",
+ " <td>[10]</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'iris_train_packed', None, u'iris_multi_model', u'iris_multi_model_info', u'class_text', u'attributes', u'model_arch_library', 10, 10, False, None, None, datetime.datetime(2019, 12, 18, 22, 33, 49, 706384), datetime.datetime(2019, 12, 18, 22, 35, 34, 547961), u'1.17-dev', 3, [u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica'], u'character varying', 1.0, [10])]"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT * FROM iris_multi_model_summary;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "View results for each model:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "12 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>mst_key</th>\n",
+ " <th>model_id</th>\n",
+ " <th>compile_params</th>\n",
+ " <th>fit_params</th>\n",
+ " <th>model_type</th>\n",
+ " <th>model_size</th>\n",
+ " <th>metrics_elapsed_time</th>\n",
+ " <th>metrics_type</th>\n",
+ " <th>training_metrics_final</th>\n",
+ " <th>training_loss_final</th>\n",
+ " <th>training_metrics</th>\n",
+ " <th>training_loss</th>\n",
+ " <th>validation_metrics_final</th>\n",
+ " <th>validation_loss_final</th>\n",
+ " <th>validation_metrics</th>\n",
+ " <th>validation_loss</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>4</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=8,epochs=1</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.7900390625</td>\n",
+ " <td>[0.148514986038208]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.975000023842</td>\n",
+ " <td>0.12241948396</td>\n",
+ " <td>[0.975000023841858]</td>\n",
+ " <td>[0.122419483959675]</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>10</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=8,epochs=1</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>1.2197265625</td>\n",
+ " <td>[0.172315120697021]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.975000023842</td>\n",
+ " <td>0.123081341386</td>\n",
+ " <td>[0.975000023841858]</td>\n",
+ " <td>[0.123081341385841]</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>9</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=4,epochs=1</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>1.2197265625</td>\n",
+ " <td>[0.274233102798462]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.925000011921</td>\n",
+ " <td>0.171397775412</td>\n",
+ " <td>[0.925000011920929]</td>\n",
+ " <td>[0.171397775411606]</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>5</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=4,epochs=1</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.7900390625</td>\n",
+ " <td>[0.155992984771729]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.925000011921</td>\n",
+ " <td>0.51177251339</td>\n",
+ " <td>[0.925000011920929]</td>\n",
+ " <td>[0.511772513389587]</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=4,epochs=1</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.7900390625</td>\n",
+ " <td>[0.220170021057129]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.908333361149</td>\n",
+ " <td>0.214677110314</td>\n",
+ " <td>[0.908333361148834]</td>\n",
+ " <td>[0.214677110314369]</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>12</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=8,epochs=1</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>1.2197265625</td>\n",
+ " <td>[0.191344022750854]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.833333313465</td>\n",
+ " <td>0.524632036686</td>\n",
+ " <td>[0.833333313465118]</td>\n",
+ " <td>[0.524632036685944]</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>8</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=8,epochs=1</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>1.2197265625</td>\n",
+ " <td>[0.181636810302734]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.758333325386</td>\n",
+ " <td>0.393412530422</td>\n",
+ " <td>[0.758333325386047]</td>\n",
+ " <td>[0.393412530422211]</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>7</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=4,epochs=1</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>1.2197265625</td>\n",
+ " <td>[0.181061029434204]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.658333361149</td>\n",
+ " <td>0.474381148815</td>\n",
+ " <td>[0.658333361148834]</td>\n",
+ " <td>[0.474381148815155]</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=8,epochs=1</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.7900390625</td>\n",
+ " <td>[0.20294713973999]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.658333361149</td>\n",
+ " <td>0.475430130959</td>\n",
+ " <td>[0.658333361148834]</td>\n",
+ " <td>[0.475430130958557]</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>6</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=8,epochs=1</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.7900390625</td>\n",
+ " <td>[0.207202911376953]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.574999988079</td>\n",
+ " <td>0.885546028614</td>\n",
+ " <td>[0.574999988079071]</td>\n",
+ " <td>[0.885546028614044]</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>11</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=4,epochs=1</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>1.2197265625</td>\n",
+ " <td>[0.374184846878052]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.433333337307</td>\n",
+ " <td>0.82793289423</td>\n",
+ " <td>[0.433333337306976]</td>\n",
+ " <td>[0.827932894229889]</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=4,epochs=1</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.7900390625</td>\n",
+ " <td>[0.216787099838257]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.316666662693</td>\n",
+ " <td>1.10255157948</td>\n",
+ " <td>[0.316666662693024]</td>\n",
+ " <td>[1.1025515794754]</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
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+ " (10, 2, u\"loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']\", u'batch_size=8,epochs=1', u'madlib_keras', 1.2197265625, [0.172315120697021], [u'accuracy'], 0.975000023842, 0.123081341386, [0.975000023841858], [0.123081341385841], None, None, None, None),\n",
+ " (9, 2, u\"loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']\", u'batch_size=4,epochs=1', u'madlib_keras', 1.2197265625, [0.274233102798462], [u'accuracy'], 0.925000011921, 0.171397775412, [0.925000011920929], [0.171397775411606], None, None, None, None),\n",
+ " (5, 1, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']\", u'batch_size=4,epochs=1', u'madlib_keras', 0.7900390625, [0.155992984771729], [u'accuracy'], 0.925000011921, 0.51177251339, [0.925000011920929], [0.511772513389587], None, None, None, None),\n",
+ " (3, 1, u\"loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']\", u'batch_size=4,epochs=1', u'madlib_keras', 0.7900390625, [0.220170021057129], [u'accuracy'], 0.908333361149, 0.214677110314, [0.908333361148834], [0.214677110314369], None, None, None, None),\n",
+ " (12, 2, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']\", u'batch_size=8,epochs=1', u'madlib_keras', 1.2197265625, [0.191344022750854], [u'accuracy'], 0.833333313465, 0.524632036686, [0.833333313465118], [0.524632036685944], None, None, None, None),\n",
+ " (8, 2, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']\", u'batch_size=8,epochs=1', u'madlib_keras', 1.2197265625, [0.181636810302734], [u'accuracy'], 0.758333325386, 0.393412530422, [0.758333325386047], [0.393412530422211], None, None, None, None),\n",
+ " (7, 2, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']\", u'batch_size=4,epochs=1', u'madlib_keras', 1.2197265625, [0.181061029434204], [u'accuracy'], 0.658333361149, 0.474381148815, [0.658333361148834], [0.474381148815155], None, None, None, None),\n",
+ " (2, 1, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']\", u'batch_size=8,epochs=1', u'madlib_keras', 0.7900390625, [0.20294713973999], [u'accuracy'], 0.658333361149, 0.475430130959, [0.658333361148834], [0.475430130958557], None, None, None, None),\n",
+ " (6, 1, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']\", u'batch_size=8,epochs=1', u'madlib_keras', 0.7900390625, [0.207202911376953], [u'accuracy'], 0.574999988079, 0.885546028614, [0.574999988079071], [0.885546028614044], None, None, None, None),\n",
+ " (11, 2, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']\", u'batch_size=4,epochs=1', u'madlib_keras', 1.2197265625, [0.374184846878052], [u'accuracy'], 0.433333337307, 0.82793289423, [0.433333337306976], [0.827932894229889], None, None, None, None),\n",
+ " (1, 1, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']\", u'batch_size=4,epochs=1', u'madlib_keras', 0.7900390625, [0.216787099838257], [u'accuracy'], 0.316666662693, 1.10255157948, [0.316666662693024], [1.1025515794754], None, None, None, None)]"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT * FROM iris_multi_model_info ORDER BY training_metrics_final DESC, training_loss_final;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"eval\"></a>\n",
+ "# 6. Evaluate\n",
+ "\n",
+ "Now run evaluate using model we built above:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>loss</th>\n",
+ " <th>metric</th>\n",
+ " <th>metrics_type</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>0.15500420332</td>\n",
+ " <td>0.966666638851</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(0.15500420331955, 0.966666638851166, [u'accuracy'])]"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS iris_validate;\n",
+ "SELECT madlib.madlib_keras_evaluate('iris_multi_model', -- model\n",
+ " 'iris_test_packed', -- test table\n",
+ " 'iris_validate', -- output table\n",
+ " NULL, -- use gpus\n",
+ " 3 -- mst_key to use\n",
+ " );\n",
+ "\n",
+ "SELECT * FROM iris_validate;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"pred\"></a>\n",
+ "# 7. Predict\n",
+ "\n",
+ "Now predict using model we built. We will use the validation data set for prediction as well, which is not usual but serves to show the syntax. The prediction is in the estimated_class_text column:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "30 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>id</th>\n",
+ " <th>estimated_class_text</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>5</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>7</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>8</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>10</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>19</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>25</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>26</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>28</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>38</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>44</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>45</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>51</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>53</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>57</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>59</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>62</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>69</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>75</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>77</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>97</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>102</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>107</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>114</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>118</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>120</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>122</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>132</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>146</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>147</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(3, u'Iris-setosa'),\n",
+ " (5, u'Iris-setosa'),\n",
+ " (7, u'Iris-setosa'),\n",
+ " (8, u'Iris-setosa'),\n",
+ " (10, u'Iris-setosa'),\n",
+ " (19, u'Iris-setosa'),\n",
+ " (25, u'Iris-setosa'),\n",
+ " (26, u'Iris-setosa'),\n",
+ " (28, u'Iris-setosa'),\n",
+ " (38, u'Iris-setosa'),\n",
+ " (44, u'Iris-setosa'),\n",
+ " (45, u'Iris-setosa'),\n",
+ " (51, u'Iris-versicolor'),\n",
+ " (53, u'Iris-versicolor'),\n",
+ " (57, u'Iris-versicolor'),\n",
+ " (59, u'Iris-versicolor'),\n",
+ " (62, u'Iris-versicolor'),\n",
+ " (69, u'Iris-virginica'),\n",
+ " (75, u'Iris-versicolor'),\n",
+ " (77, u'Iris-versicolor'),\n",
+ " (97, u'Iris-versicolor'),\n",
+ " (102, u'Iris-virginica'),\n",
+ " (107, u'Iris-virginica'),\n",
+ " (114, u'Iris-virginica'),\n",
+ " (118, u'Iris-virginica'),\n",
+ " (120, u'Iris-virginica'),\n",
+ " (122, u'Iris-virginica'),\n",
+ " (132, u'Iris-virginica'),\n",
+ " (146, u'Iris-virginica'),\n",
+ " (147, u'Iris-virginica')]"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS iris_predict;\n",
+ "\n",
+ "SELECT madlib.madlib_keras_predict('iris_multi_model', -- model\n",
+ " 'iris_test', -- test_table\n",
+ " 'id', -- id column\n",
+ " 'attributes', -- independent var\n",
+ " 'iris_predict', -- output table\n",
+ " 'response', -- prediction type\n",
+ " FALSE, -- use gpus\n",
+ " 3 -- mst_key to use\n",
+ " );\n",
+ "\n",
+ "SELECT * FROM iris_predict ORDER BY id;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Count missclassifications"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(1L,)]"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT COUNT(*) FROM iris_predict JOIN iris_test USING (id) \n",
+ "WHERE iris_predict.estimated_class_text != iris_test.class_text;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Percent missclassifications"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>test_accuracy_percent</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>96.67</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(Decimal('96.67'),)]"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT round(count(*)*100/(150*0.2),2) as test_accuracy_percent from\n",
+ " (select iris_test.class_text as actual, iris_predict.estimated_class_text as estimated\n",
+ " from iris_predict inner join iris_test\n",
+ " on iris_test.id=iris_predict.id) q\n",
+ "WHERE q.actual=q.estimated;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"class2\"></a>\n",
+ "# Classification with Other Parameters\n",
+ "\n",
+ "<a id=\"val_dataset\"></a>\n",
+ "# 1. Validation dataset\n",
+ "\n",
+ "Now use a validation dataset and compute metrics every 2nd iteration using the 'metrics_compute_frequency' parameter. This can help reduce run time if you do not need metrics computed at every iteration."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>madlib_keras_fit_multiple_model</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td></td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[('',)]"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS iris_multi_model, iris_multi_model_summary, iris_multi_model_info;\n",
+ "\n",
+ "SELECT madlib.madlib_keras_fit_multiple_model('iris_train_packed', -- source_table\n",
+ " 'iris_multi_model', -- model_output_table\n",
+ " 'mst_table', -- model_selection_table\n",
+ " 10, -- num_iterations\n",
+ " FALSE, -- use gpus\n",
+ " 'iris_test_packed', -- validation dataset\n",
+ " 3, -- metrics compute frequency\n",
+ " FALSE, -- warm start\n",
+ " 'Sophie L.', -- name\n",
+ " 'Model selection for iris dataset' -- description\n",
+ " );"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "View the model summary:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>source_table</th>\n",
+ " <th>validation_table</th>\n",
+ " <th>model</th>\n",
+ " <th>model_info</th>\n",
+ " <th>dependent_varname</th>\n",
+ " <th>independent_varname</th>\n",
+ " <th>model_arch_table</th>\n",
+ " <th>num_iterations</th>\n",
+ " <th>metrics_compute_frequency</th>\n",
+ " <th>warm_start</th>\n",
+ " <th>name</th>\n",
+ " <th>description</th>\n",
+ " <th>start_training_time</th>\n",
+ " <th>end_training_time</th>\n",
+ " <th>madlib_version</th>\n",
+ " <th>num_classes</th>\n",
+ " <th>class_values</th>\n",
+ " <th>dependent_vartype</th>\n",
+ " <th>normalizing_const</th>\n",
+ " <th>metrics_iters</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>iris_train_packed</td>\n",
+ " <td>iris_test_packed</td>\n",
+ " <td>iris_multi_model</td>\n",
+ " <td>iris_multi_model_info</td>\n",
+ " <td>class_text</td>\n",
+ " <td>attributes</td>\n",
+ " <td>model_arch_library</td>\n",
+ " <td>10</td>\n",
+ " <td>3</td>\n",
+ " <td>False</td>\n",
+ " <td>Sophie L.</td>\n",
+ " <td>Model selection for iris dataset</td>\n",
+ " <td>2019-12-18 22:35:49.962345</td>\n",
+ " <td>2019-12-18 22:37:51.230499</td>\n",
+ " <td>1.17-dev</td>\n",
+ " <td>3</td>\n",
+ " <td>[u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica']</td>\n",
+ " <td>character varying</td>\n",
+ " <td>1.0</td>\n",
+ " <td>[3, 6, 9, 10]</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'iris_train_packed', u'iris_test_packed', u'iris_multi_model', u'iris_multi_model_info', u'class_text', u'attributes', u'model_arch_library', 10, 3, False, u'Sophie L.', u'Model selection for iris dataset', datetime.datetime(2019, 12, 18, 22, 35, 49, 962345), datetime.datetime(2019, 12, 18, 22, 37, 51, 230499), u'1.17-dev', 3, [u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica'], u'character varying', 1.0, [3, 6, 9, 10])]"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT * FROM iris_multi_model_summary;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "View performance of each model:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "12 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>mst_key</th>\n",
+ " <th>model_id</th>\n",
+ " <th>compile_params</th>\n",
+ " <th>fit_params</th>\n",
+ " <th>model_type</th>\n",
+ " <th>model_size</th>\n",
+ " <th>metrics_elapsed_time</th>\n",
+ " <th>metrics_type</th>\n",
+ " <th>training_metrics_final</th>\n",
+ " <th>training_loss_final</th>\n",
+ " <th>training_metrics</th>\n",
+ " <th>training_loss</th>\n",
+ " <th>validation_metrics_final</th>\n",
+ " <th>validation_loss_final</th>\n",
+ " <th>validation_metrics</th>\n",
+ " <th>validation_loss</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>6</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=8,epochs=1</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.7900390625</td>\n",
+ " <td>[0.400555849075317, 0.175060987472534, 0.161082029342651, 0.159379005432129]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.958333313465</td>\n",
+ " <td>0.370426625013</td>\n",
+ " <td>[0.841666638851166, 0.875, 0.958333313465118, 0.958333313465118]</td>\n",
+ " <td>[0.597030103206635, 0.467845916748047, 0.394165992736816, 0.370426625013351]</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.32715767622</td>\n",
+ " <td>[0.866666674613953, 0.933333337306976, 1.0, 1.0]</td>\n",
+ " <td>[0.587784588336945, 0.432697623968124, 0.352933287620544, 0.32715767621994]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=4,epochs=1</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.7900390625</td>\n",
+ " <td>[0.157984018325806, 0.146160840988159, 0.446839094161987, 0.217149972915649]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.916666686535</td>\n",
+ " <td>0.176682218909</td>\n",
+ " <td>[0.958333313465118, 0.891666650772095, 0.841666638851166, 0.916666686534882]</td>\n",
+ " <td>[0.340974450111389, 0.224177747964859, 0.315857976675034, 0.176682218909264]</td>\n",
+ " <td>0.966666638851</td>\n",
+ " <td>0.146555349231</td>\n",
+ " <td>[0.966666638851166, 0.933333337306976, 0.866666674613953, 0.966666638851166]</td>\n",
+ " <td>[0.306026995182037, 0.204480707645416, 0.291850447654724, 0.146555349230766]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=4,epochs=1</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.7900390625</td>\n",
+ " <td>[0.158334016799927, 0.492121934890747, 0.168816804885864, 0.160614013671875]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.949999988079</td>\n",
+ " <td>0.137093007565</td>\n",
+ " <td>[0.75, 0.808333337306976, 0.941666662693024, 0.949999988079071]</td>\n",
+ " <td>[0.861838400363922, 0.306531131267548, 0.267581582069397, 0.137093007564545]</td>\n",
+ " <td>0.966666638851</td>\n",
+ " <td>0.0812632590532</td>\n",
+ " <td>[0.533333361148834, 0.733333349227905, 1.0, 0.966666638851166]</td>\n",
+ " <td>[1.17265951633453, 0.347328811883926, 0.0795030668377876, 0.0812632590532303]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>10</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=8,epochs=1</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>1.2197265625</td>\n",
+ " <td>[0.206979990005493, 0.175852060317993, 0.18351411819458, 0.173283100128174]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.841666638851</td>\n",
+ " <td>0.319059103727</td>\n",
+ " <td>[0.833333313465118, 0.916666686534882, 0.958333313465118, 0.841666638851166]</td>\n",
+ " <td>[0.375581055879593, 0.235803470015526, 0.119093284010887, 0.319059103727341]</td>\n",
+ " <td>0.866666674614</td>\n",
+ " <td>0.294114112854</td>\n",
+ " <td>[0.866666674613953, 0.966666638851166, 0.933333337306976, 0.866666674613953]</td>\n",
+ " <td>[0.332203418016434, 0.206457450985909, 0.09817935526371, 0.294114112854004]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>4</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=8,epochs=1</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.7900390625</td>\n",
+ " <td>[0.154335021972656, 0.14276385307312, 0.160094022750854, 0.147177934646606]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.833333313465</td>\n",
+ " <td>0.315035998821</td>\n",
+ " <td>[0.850000023841858, 0.966666638851166, 0.966666638851166, 0.833333313465118]</td>\n",
+ " <td>[0.39260533452034, 0.207864001393318, 0.14202418923378, 0.315035998821259]</td>\n",
+ " <td>0.833333313465</td>\n",
+ " <td>0.287047833204</td>\n",
+ " <td>[0.833333313465118, 0.966666638851166, 0.933333337306976, 0.833333313465118]</td>\n",
+ " <td>[0.350265830755234, 0.179627984762192, 0.119969591498375, 0.287047833204269]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>9</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=4,epochs=1</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>1.2197265625</td>\n",
+ " <td>[0.183771848678589, 0.442173957824707, 0.196517944335938, 0.183962106704712]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.683333337307</td>\n",
+ " <td>0.773626208305</td>\n",
+ " <td>[0.983333349227905, 0.783333361148834, 0.841666638851166, 0.683333337306976]</td>\n",
+ " <td>[0.323956668376923, 0.355609774589539, 0.289077579975128, 0.773626208305359]</td>\n",
+ " <td>0.733333349228</td>\n",
+ " <td>0.598832905293</td>\n",
+ " <td>[0.966666638851166, 0.733333349227905, 0.866666674613953, 0.733333349227905]</td>\n",
+ " <td>[0.292185336351395, 0.310099214315414, 0.278687566518784, 0.598832905292511]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>11</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=4,epochs=1</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>1.2197265625</td>\n",
+ " <td>[0.215842962265015, 0.183883190155029, 0.181258201599121, 0.233398914337158]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.658333361149</td>\n",
+ " <td>0.501300632954</td>\n",
+ " <td>[0.341666668653488, 0.658333361148834, 0.658333361148834, 0.658333361148834]</td>\n",
+ " <td>[0.947986364364624, 0.807084918022156, 0.549242556095123, 0.501300632953644]</td>\n",
+ " <td>0.699999988079</td>\n",
+ " <td>0.459856539965</td>\n",
+ " <td>[0.300000011920929, 0.699999988079071, 0.699999988079071, 0.699999988079071]</td>\n",
+ " <td>[0.971994161605835, 0.821518063545227, 0.513974606990814, 0.459856539964676]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=8,epochs=1</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.7900390625</td>\n",
+ " <td>[0.181059837341309, 0.156504154205322, 0.154800891876221, 0.165037870407104]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.675000011921</td>\n",
+ " <td>0.500130057335</td>\n",
+ " <td>[0.658333361148834, 0.908333361148834, 0.908333361148834, 0.675000011920929]</td>\n",
+ " <td>[0.822371363639832, 0.354260504245758, 0.206746637821198, 0.5001300573349]</td>\n",
+ " <td>0.699999988079</td>\n",
+ " <td>0.511800050735</td>\n",
+ " <td>[0.699999988079071, 0.933333337306976, 0.966666638851166, 0.699999988079071]</td>\n",
+ " <td>[0.784473180770874, 0.314396589994431, 0.171932756900787, 0.511800050735474]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>5</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=4,epochs=1</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.7900390625</td>\n",
+ " <td>[0.16503119468689, 0.165420055389404, 0.163087844848633, 0.157285213470459]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.600000023842</td>\n",
+ " <td>0.536593079567</td>\n",
+ " <td>[0.625, 0.491666674613953, 0.508333325386047, 0.600000023841858]</td>\n",
+ " <td>[0.877406716346741, 0.665770947933197, 0.563206613063812, 0.536593079566956]</td>\n",
+ " <td>0.600000023842</td>\n",
+ " <td>0.50565046072</td>\n",
+ " <td>[0.566666662693024, 0.533333361148834, 0.600000023841858, 0.600000023841858]</td>\n",
+ " <td>[0.898801684379578, 0.642534494400024, 0.529698371887207, 0.505650460720062]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>12</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=8,epochs=1</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>1.2197265625</td>\n",
+ " <td>[0.180193901062012, 0.230684041976929, 0.202606916427612, 0.182677030563354]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.5</td>\n",
+ " <td>1.01774513721</td>\n",
+ " <td>[0.341666668653488, 0.491666674613953, 0.524999976158142, 0.5]</td>\n",
+ " <td>[1.10608339309692, 1.06158423423767, 1.02908384799957, 1.01774513721466]</td>\n",
+ " <td>0.5</td>\n",
+ " <td>1.01636135578</td>\n",
+ " <td>[0.300000011920929, 0.466666668653488, 0.466666668653488, 0.5]</td>\n",
+ " <td>[1.10331404209137, 1.05365967750549, 1.02413082122803, 1.01636135578156]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>7</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=4,epochs=1</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>1.2197265625</td>\n",
+ " <td>[0.181950092315674, 0.197594881057739, 0.187069177627563, 0.183701992034912]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.316666662693</td>\n",
+ " <td>1.10080897808</td>\n",
+ " <td>[0.316666662693024, 0.341666668653488, 0.341666668653488, 0.316666662693024]</td>\n",
+ " <td>[1.1043815612793, 1.11140048503876, 1.09834468364716, 1.10080897808075]</td>\n",
+ " <td>0.40000000596</td>\n",
+ " <td>1.09380173683</td>\n",
+ " <td>[0.400000005960464, 0.300000011920929, 0.300000011920929, 0.400000005960464]</td>\n",
+ " <td>[1.09075009822845, 1.09998726844788, 1.10155093669891, 1.09380173683167]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>8</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=8,epochs=1</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>1.2197265625</td>\n",
+ " <td>[0.182392835617065, 0.206873893737793, 0.192094087600708, 0.185320854187012]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.341666668653</td>\n",
+ " <td>1.10410153866</td>\n",
+ " <td>[0.341666668653488, 0.316666662693024, 0.341666668653488, 0.341666668653488]</td>\n",
+ " <td>[1.10291886329651, 1.10132431983948, 1.10635650157928, 1.10410153865814]</td>\n",
+ " <td>0.300000011921</td>\n",
+ " <td>1.10918176174</td>\n",
+ " <td>[0.300000011920929, 0.400000005960464, 0.300000011920929, 0.300000011920929]</td>\n",
+ " <td>[1.10382485389709, 1.09316170215607, 1.1332186460495, 1.10918176174164]</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(6, 1, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']\", u'batch_size=8,epochs=1', u'madlib_keras', 0.7900390625, [0.400555849075317, 0.175060987472534, 0.161082029342651, 0.159379005432129], [u'accuracy'], 0.958333313465, 0.370426625013, [0.841666638851166, 0.875, 0.958333313465118, 0.958333313465118], [0.597030103206635, 0.467845916748047, 0.394165992736816, 0.370426625013351], 1.0, 0.32715767622, [0.866666674613953, 0.933333337306976, 1.0, 1.0], [0.587784588336945, 0.432697623968124, 0.352933287620544, 0.32715767621994]),\n",
+ " (3, 1, u\"loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']\", u'batch_size=4,epochs=1', u'madlib_keras', 0.7900390625, [0.157984018325806, 0.146160840988159, 0.446839094161987, 0.217149972915649], [u'accuracy'], 0.916666686535, 0.176682218909, [0.958333313465118, 0.891666650772095, 0.841666638851166, 0.916666686534882], [0.340974450111389, 0.224177747964859, 0.315857976675034, 0.176682218909264], 0.966666638851, 0.146555349231, [0.966666638851166, 0.933333337306976, 0.866666674613953, 0.966666638851166], [0.306026995182037, 0.204480707645416, 0.291850447654724, 0.146555349230766]),\n",
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+ " (9, 2, u\"loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']\", u'batch_size=4,epochs=1', u'madlib_keras', 1.2197265625, [0.183771848678589, 0.442173957824707, 0.196517944335938, 0.183962106704712], [u'accuracy'], 0.683333337307, 0.773626208305, [0.983333349227905, 0.783333361148834, 0.841666638851166, 0.683333337306976], [0.323956668376923, 0.355609774589539, 0.289077579975128, 0.773626208305359], 0.733333349228, 0.598832905293, [0.966666638851166, 0.733333349227905, 0.866666674613953, 0.733333349227905], [0.292185336351395, 0.310099214315414, 0.278687566518784, 0.598832905292511]),\n",
+ " (11, 2, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']\", u'batch_size=4,epochs=1', u'madlib_keras', 1.2197265625, [0.215842962265015, 0.183883190155029, 0.181258201599121, 0.233398914337158], [u'accuracy'], 0.658333361149, 0.501300632954, [0.341666668653488, 0.658333361148834, 0.658333361148834, 0.658333361148834], [0.947986364364624, 0.807084918022156, 0.549242556095123, 0.501300632953644], 0.699999988079, 0.459856539965, [0.300000011920929, 0.699999988079071, 0.699999988079071, 0.699999988079071], [0.971994161605835, 0.821518063545227, 0.513974606990814, 0.459856539964676]),\n",
+ " (2, 1, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']\", u'batch_size=8,epochs=1', u'madlib_keras', 0.7900390625, [0.181059837341309, 0.156504154205322, 0.154800891876221, 0.165037870407104], [u'accuracy'], 0.675000011921, 0.500130057335, [0.658333361148834, 0.908333361148834, 0.908333361148834, 0.675000011920929], [0.822371363639832, 0.354260504245758, 0.206746637821198, 0.5001300573349], 0.699999988079, 0.511800050735, [0.699999988079071, 0.933333337306976, 0.966666638851166, 0.699999988079071], [0.784473180770874, 0.314396589994431, 0.171932756900787, 0.511800050735474]),\n",
+ " (5, 1, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']\", u'batch_size=4,epochs=1', u'madlib_keras', 0.7900390625, [0.16503119468689, 0.165420055389404, 0.163087844848633, 0.157285213470459], [u'accuracy'], 0.600000023842, 0.536593079567, [0.625, 0.491666674613953, 0.508333325386047, 0.600000023841858], [0.877406716346741, 0.665770947933197, 0.563206613063812, 0.536593079566956], 0.600000023842, 0.50565046072, [0.566666662693024, 0.533333361148834, 0.600000023841858, 0.600000023841858], [0.898801684379578, 0.642534494400024, 0.529698371887207, 0.505650460720062]),\n",
+ " (12, 2, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']\", u'batch_size=8,epochs=1', u'madlib_keras', 1.2197265625, [0.180193901062012, 0.230684041976929, 0.202606916427612, 0.182677030563354], [u'accuracy'], 0.5, 1.01774513721, [0.341666668653488, 0.491666674613953, 0.524999976158142, 0.5], [1.10608339309692, 1.06158423423767, 1.02908384799957, 1.01774513721466], 0.5, 1.01636135578, [0.300000011920929, 0.466666668653488, 0.466666668653488, 0.5], [1.10331404209137, 1.05365967750549, 1.02413082122803, 1.01636135578156]),\n",
+ " (7, 2, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']\", u'batch_size=4,epochs=1', u'madlib_keras', 1.2197265625, [0.181950092315674, 0.197594881057739, 0.187069177627563, 0.183701992034912], [u'accuracy'], 0.316666662693, 1.10080897808, [0.316666662693024, 0.341666668653488, 0.341666668653488, 0.316666662693024], [1.1043815612793, 1.11140048503876, 1.09834468364716, 1.10080897808075], 0.40000000596, 1.09380173683, [0.400000005960464, 0.300000011920929, 0.300000011920929, 0.400000005960464], [1.09075009822845, 1.09998726844788, 1.10155093669891, 1.09380173683167]),\n",
+ " (8, 2, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']\", u'batch_size=8,epochs=1', u'madlib_keras', 1.2197265625, [0.182392835617065, 0.206873893737793, 0.192094087600708, 0.185320854187012], [u'accuracy'], 0.341666668653, 1.10410153866, [0.341666668653488, 0.316666662693024, 0.341666668653488, 0.341666668653488], [1.10291886329651, 1.10132431983948, 1.10635650157928, 1.10410153865814], 0.300000011921, 1.10918176174, [0.300000011920929, 0.400000005960464, 0.300000011920929, 0.300000011920929], [1.10382485389709, 1.09316170215607, 1.1332186460495, 1.10918176174164])]"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT * FROM iris_multi_model_info ORDER BY validation_metrics_final DESC;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Plot validation results"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib notebook\n",
+ "import matplotlib.pyplot as plt\n",
+ "from matplotlib.ticker import MaxNLocator\n",
+ "from collections import defaultdict\n",
+ "import pandas as pd\n",
+ "import seaborn as sns\n",
+ "sns.set_palette(sns.color_palette(\"hls\", 20))\n",
+ "plt.rcParams.update({'font.size': 12})\n",
+ "pd.set_option('display.max_colwidth', -1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "7 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
+ " if (typeof(WebSocket) !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support.' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
+ "\n",
+ " this.ws = websocket;\n",
+ "\n",
+ " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ "\n",
+ " if (!this.supports_binary) {\n",
+ " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
+ "\n",
+ " this.context = undefined;\n",
+ " this.message = undefined;\n",
+ " this.canvas = undefined;\n",
+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
+ " if (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+ " }\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " }\n",
+ "\n",
+ " this.imageObj.onload = function() {\n",
+ " if (fig.image_mode == 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onunload = function() {\n",
+ " fig.ws.close();\n",
+ " }\n",
+ "\n",
+ " this.ws.onmessage = this._make_on_message_function(this);\n",
+ "\n",
+ " this.ondownload = ondownload;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option)\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width, fig.canvas.height);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var width = this.canvas.width/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " event.shiftKey = false;\n",
+ " // Send a \"J\" for go to next cell\n",
+ " event.which = 74;\n",
+ " event.keyCode = 74;\n",
+ " manager.command_mode();\n",
+ " manager.handle_keydown(event);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i<ncells; i++) {\n",
+ " var cell = cells[i];\n",
+ " if (cell.cell_type === 'code'){\n",
+ " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " var data = cell.output_area.outputs[j];\n",
+ " if (data.data) {\n",
+ " // IPython >= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
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+ ]
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\" width=\"1000\">"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.legend.Legend at 0x12e9ae7d0>"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_results = %sql SELECT * FROM iris_multi_model_info ORDER BY validation_loss ASC LIMIT 7;\n",
+ "df_results = df_results.DataFrame()\n",
+ "\n",
+ "df_summary = %sql SELECT * FROM iris_multi_model_summary;\n",
+ "df_summary = df_summary.DataFrame()\n",
+ "\n",
+ "#set up plots\n",
+ "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(10,5))\n",
+ "fig.legend(ncol=4)\n",
+ "fig.tight_layout()\n",
+ "\n",
+ "ax_metric = axs[0]\n",
+ "ax_loss = axs[1]\n",
+ "\n",
+ "ax_metric.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
+ "ax_metric.set_xlabel('Iteration')\n",
+ "ax_metric.set_ylabel('Metric')\n",
+ "ax_metric.set_title('Validation metric curve')\n",
+ "\n",
+ "ax_loss.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
+ "ax_loss.set_xlabel('Iteration')\n",
+ "ax_loss.set_ylabel('Loss')\n",
+ "ax_loss.set_title('Validation loss curve')\n",
+ "\n",
+ "iters = df_summary['metrics_iters'][0]\n",
+ "\n",
+ "for mst_key in df_results['mst_key']:\n",
+ " df_output_info = %sql SELECT validation_metrics,validation_loss FROM iris_multi_model_info WHERE mst_key = $mst_key\n",
+ " df_output_info = df_output_info.DataFrame()\n",
+ " validation_metrics = df_output_info['validation_metrics'][0]\n",
+ " validation_loss = df_output_info['validation_loss'][0]\n",
+ " \n",
+ " ax_metric.plot(iters, validation_metrics, label=mst_key, marker='o')\n",
+ " ax_loss.plot(iters, validation_loss, label=mst_key, marker='o')\n",
+ "\n",
+ "plt.legend()\n",
+ "# fig.savefig('./lc_keras_fit.png', dpi = 300)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"pred_prob\"></a>\n",
+ "# 2. Predict probabilities\n",
+ "\n",
+ "Predict with probabilities for each class:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "30 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>id</th>\n",
+ " <th>prob_Iris-setosa</th>\n",
+ " <th>prob_Iris-versicolor</th>\n",
+ " <th>prob_Iris-virginica</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>0.9999416</td>\n",
+ " <td>5.8360623e-05</td>\n",
+ " <td>3.9093355e-12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>5</td>\n",
+ " <td>0.99998116</td>\n",
+ " <td>1.8880675e-05</td>\n",
+ " <td>2.5342377e-13</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>7</td>\n",
+ " <td>0.99994814</td>\n",
+ " <td>5.1881765e-05</td>\n",
+ " <td>2.5964983e-12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>8</td>\n",
+ " <td>0.99996114</td>\n",
+ " <td>3.8810744e-05</td>\n",
+ " <td>1.176443e-12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>10</td>\n",
+ " <td>0.99992573</td>\n",
+ " <td>7.4317446e-05</td>\n",
+ " <td>5.4237942e-12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>19</td>\n",
+ " <td>0.9999845</td>\n",
+ " <td>1.5514812e-05</td>\n",
+ " <td>1.034207e-13</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>25</td>\n",
+ " <td>0.99992156</td>\n",
+ " <td>7.845682e-05</td>\n",
+ " <td>3.7364413e-12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>26</td>\n",
+ " <td>0.9998591</td>\n",
+ " <td>0.00014085071</td>\n",
+ " <td>2.0146884e-11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>28</td>\n",
+ " <td>0.9999734</td>\n",
+ " <td>2.6542659e-05</td>\n",
+ " <td>4.8342347e-13</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>38</td>\n",
+ " <td>0.99992573</td>\n",
+ " <td>7.4317446e-05</td>\n",
+ " <td>5.4237942e-12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>44</td>\n",
+ " <td>0.99990726</td>\n",
+ " <td>9.278052e-05</td>\n",
+ " <td>6.9040372e-12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>45</td>\n",
+ " <td>0.999964</td>\n",
+ " <td>3.6013742e-05</td>\n",
+ " <td>5.7615945e-13</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>51</td>\n",
+ " <td>0.00025041687</td>\n",
+ " <td>0.99780566</td>\n",
+ " <td>0.0019439155</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>53</td>\n",
+ " <td>1.843269e-05</td>\n",
+ " <td>0.9889865</td>\n",
+ " <td>0.010995116</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>57</td>\n",
+ " <td>2.4158675e-05</td>\n",
+ " <td>0.99005336</td>\n",
+ " <td>0.00992243</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>59</td>\n",
+ " <td>0.00011159414</td>\n",
+ " <td>0.9942708</td>\n",
+ " <td>0.0056176083</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>62</td>\n",
+ " <td>0.00014697485</td>\n",
+ " <td>0.99189115</td>\n",
+ " <td>0.007961868</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>69</td>\n",
+ " <td>8.6406266e-07</td>\n",
+ " <td>0.6961896</td>\n",
+ " <td>0.30380967</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>75</td>\n",
+ " <td>0.0005239165</td>\n",
+ " <td>0.9965855</td>\n",
+ " <td>0.0028905326</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>77</td>\n",
+ " <td>1.5155997e-05</td>\n",
+ " <td>0.97978914</td>\n",
+ " <td>0.020195633</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>97</td>\n",
+ " <td>0.00023696794</td>\n",
+ " <td>0.9938279</td>\n",
+ " <td>0.005935215</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>102</td>\n",
+ " <td>1.3247301e-09</td>\n",
+ " <td>0.18419608</td>\n",
+ " <td>0.8158039</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>107</td>\n",
+ " <td>2.5100556e-08</td>\n",
+ " <td>0.30281228</td>\n",
+ " <td>0.69718766</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>114</td>\n",
+ " <td>3.2222575e-10</td>\n",
+ " <td>0.08682407</td>\n",
+ " <td>0.913176</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>118</td>\n",
+ " <td>5.33606e-11</td>\n",
+ " <td>0.34179842</td>\n",
+ " <td>0.6582016</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>120</td>\n",
+ " <td>9.134116e-09</td>\n",
+ " <td>0.27099058</td>\n",
+ " <td>0.72900945</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>122</td>\n",
+ " <td>2.9710499e-09</td>\n",
+ " <td>0.21993305</td>\n",
+ " <td>0.7800669</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>132</td>\n",
+ " <td>5.2177818e-09</td>\n",
+ " <td>0.8370931</td>\n",
+ " <td>0.16290687</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>146</td>\n",
+ " <td>1.4404147e-09</td>\n",
+ " <td>0.2293714</td>\n",
+ " <td>0.7706286</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>147</td>\n",
+ " <td>3.8019614e-09</td>\n",
+ " <td>0.2240861</td>\n",
+ " <td>0.77591395</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(3, 0.9999416, 5.8360623e-05, 3.9093355e-12),\n",
+ " (5, 0.99998116, 1.8880675e-05, 2.5342377e-13),\n",
+ " (7, 0.99994814, 5.1881765e-05, 2.5964983e-12),\n",
+ " (8, 0.99996114, 3.8810744e-05, 1.176443e-12),\n",
+ " (10, 0.99992573, 7.4317446e-05, 5.4237942e-12),\n",
+ " (19, 0.9999845, 1.5514812e-05, 1.034207e-13),\n",
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+ " (26, 0.9998591, 0.00014085071, 2.0146884e-11),\n",
+ " (28, 0.9999734, 2.6542659e-05, 4.8342347e-13),\n",
+ " (38, 0.99992573, 7.4317446e-05, 5.4237942e-12),\n",
+ " (44, 0.99990726, 9.278052e-05, 6.9040372e-12),\n",
+ " (45, 0.999964, 3.6013742e-05, 5.7615945e-13),\n",
+ " (51, 0.00025041687, 0.99780566, 0.0019439155),\n",
+ " (53, 1.843269e-05, 0.9889865, 0.010995116),\n",
+ " (57, 2.4158675e-05, 0.99005336, 0.00992243),\n",
+ " (59, 0.00011159414, 0.9942708, 0.0056176083),\n",
+ " (62, 0.00014697485, 0.99189115, 0.007961868),\n",
+ " (69, 8.6406266e-07, 0.6961896, 0.30380967),\n",
+ " (75, 0.0005239165, 0.9965855, 0.0028905326),\n",
+ " (77, 1.5155997e-05, 0.97978914, 0.020195633),\n",
+ " (97, 0.00023696794, 0.9938279, 0.005935215),\n",
+ " (102, 1.3247301e-09, 0.18419608, 0.8158039),\n",
+ " (107, 2.5100556e-08, 0.30281228, 0.69718766),\n",
+ " (114, 3.2222575e-10, 0.08682407, 0.913176),\n",
+ " (118, 5.33606e-11, 0.34179842, 0.6582016),\n",
+ " (120, 9.134116e-09, 0.27099058, 0.72900945),\n",
+ " (122, 2.9710499e-09, 0.21993305, 0.7800669),\n",
+ " (132, 5.2177818e-09, 0.8370931, 0.16290687),\n",
+ " (146, 1.4404147e-09, 0.2293714, 0.7706286),\n",
+ " (147, 3.8019614e-09, 0.2240861, 0.77591395)]"
+ ]
+ },
+ "execution_count": 30,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS iris_predict;\n",
+ "\n",
+ "SELECT madlib.madlib_keras_predict('iris_multi_model', -- model\n",
+ " 'iris_test', -- test_table\n",
+ " 'id', -- id column\n",
+ " 'attributes', -- independent var\n",
+ " 'iris_predict', -- output table\n",
+ " 'prob', -- prediction type\n",
+ " FALSE, -- use gpus\n",
+ " 3 -- mst_key to use\n",
+ " );\n",
+ "\n",
+ "SELECT * FROM iris_predict ORDER BY id;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"warm_start\"></a>\n",
+ "# 3. Warm start\n",
+ "\n",
+ "Next, use the warm_start parameter to continue learning, using the coefficients from the run above. Note that we don't drop the model table or model summary table:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>madlib_keras_fit_multiple_model</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td></td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[('',)]"
+ ]
+ },
+ "execution_count": 31,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT madlib.madlib_keras_fit_multiple_model('iris_train_packed', -- source_table\n",
+ " 'iris_multi_model', -- model_output_table\n",
+ " 'mst_table', -- model_selection_table\n",
+ " 3, -- num_iterations\n",
+ " FALSE, -- use gpus\n",
+ " 'iris_test_packed', -- validation dataset\n",
+ " 1, -- metrics compute frequency\n",
+ " TRUE, -- warm start\n",
+ " 'Sophie L.', -- name\n",
+ " 'Simple MLP for iris dataset' -- description\n",
+ " );"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "View summary:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>source_table</th>\n",
+ " <th>validation_table</th>\n",
+ " <th>model</th>\n",
+ " <th>model_info</th>\n",
+ " <th>dependent_varname</th>\n",
+ " <th>independent_varname</th>\n",
+ " <th>model_arch_table</th>\n",
+ " <th>num_iterations</th>\n",
+ " <th>metrics_compute_frequency</th>\n",
+ " <th>warm_start</th>\n",
+ " <th>name</th>\n",
+ " <th>description</th>\n",
+ " <th>start_training_time</th>\n",
+ " <th>end_training_time</th>\n",
+ " <th>madlib_version</th>\n",
+ " <th>num_classes</th>\n",
+ " <th>class_values</th>\n",
+ " <th>dependent_vartype</th>\n",
+ " <th>normalizing_const</th>\n",
+ " <th>metrics_iters</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>iris_train_packed</td>\n",
+ " <td>iris_test_packed</td>\n",
+ " <td>iris_multi_model</td>\n",
+ " <td>iris_multi_model_info</td>\n",
+ " <td>class_text</td>\n",
+ " <td>attributes</td>\n",
+ " <td>model_arch_library</td>\n",
+ " <td>3</td>\n",
+ " <td>1</td>\n",
+ " <td>True</td>\n",
+ " <td>Sophie L.</td>\n",
+ " <td>Simple MLP for iris dataset</td>\n",
+ " <td>2019-12-18 22:37:57.948805</td>\n",
+ " <td>2019-12-18 22:38:43.967187</td>\n",
+ " <td>1.17-dev</td>\n",
+ " <td>3</td>\n",
+ " <td>[u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica']</td>\n",
+ " <td>character varying</td>\n",
+ " <td>1.0</td>\n",
+ " <td>[1, 2, 3]</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'iris_train_packed', u'iris_test_packed', u'iris_multi_model', u'iris_multi_model_info', u'class_text', u'attributes', u'model_arch_library', 3, 1, True, u'Sophie L.', u'Simple MLP for iris dataset', datetime.datetime(2019, 12, 18, 22, 37, 57, 948805), datetime.datetime(2019, 12, 18, 22, 38, 43, 967187), u'1.17-dev', 3, [u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica'], u'character varying', 1.0, [1, 2, 3])]"
+ ]
+ },
+ "execution_count": 32,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT * FROM iris_multi_model_summary;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "View performance of each model:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "12 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>mst_key</th>\n",
+ " <th>model_id</th>\n",
+ " <th>compile_params</th>\n",
+ " <th>fit_params</th>\n",
+ " <th>model_type</th>\n",
+ " <th>model_size</th>\n",
+ " <th>metrics_elapsed_time</th>\n",
+ " <th>metrics_type</th>\n",
+ " <th>training_metrics_final</th>\n",
+ " <th>training_loss_final</th>\n",
+ " <th>training_metrics</th>\n",
+ " <th>training_loss</th>\n",
+ " <th>validation_metrics_final</th>\n",
+ " <th>validation_loss_final</th>\n",
+ " <th>validation_metrics</th>\n",
+ " <th>validation_loss</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>6</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=8,epochs=1</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.7900390625</td>\n",
+ " <td>[0.17091703414917, 0.163390159606934, 0.155634164810181]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.958333313465</td>\n",
+ " <td>0.31917694211</td>\n",
+ " <td>[0.958333313465118, 0.958333313465118, 0.958333313465118]</td>\n",
+ " <td>[0.348434448242188, 0.334388434886932, 0.319176942110062]</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.272621482611</td>\n",
+ " <td>[1.0, 1.0, 1.0]</td>\n",
+ " <td>[0.306039541959763, 0.28966349363327, 0.272621482610703]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>10</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=8,epochs=1</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>1.2197265625</td>\n",
+ " <td>[0.172316074371338, 0.188217163085938, 0.503840208053589]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.899999976158</td>\n",
+ " <td>0.193531006575</td>\n",
+ " <td>[0.958333313465118, 0.925000011920929, 0.899999976158142]</td>\n",
+ " <td>[0.147025644779205, 0.144938006997108, 0.193531006574631]</td>\n",
+ " <td>0.966666638851</td>\n",
+ " <td>0.153077676892</td>\n",
+ " <td>[0.966666638851166, 0.966666638851166, 0.966666638851166]</td>\n",
+ " <td>[0.132363379001617, 0.116448685526848, 0.153077676892281]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>4</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=8,epochs=1</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.7900390625</td>\n",
+ " <td>[0.147105932235718, 0.158121824264526, 0.174723863601685]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.966666638851</td>\n",
+ " <td>0.100400544703</td>\n",
+ " <td>[0.966666638851166, 0.908333361148834, 0.966666638851166]</td>\n",
+ " <td>[0.112152323126793, 0.197978660464287, 0.100400544703007]</td>\n",
+ " <td>0.966666638851</td>\n",
+ " <td>0.0844493880868</td>\n",
+ " <td>[0.933333337306976, 0.966666638851166, 0.966666638851166]</td>\n",
+ " <td>[0.0945712551474571, 0.170254677534103, 0.0844493880867958]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>9</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=4,epochs=1</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>1.2197265625</td>\n",
+ " <td>[0.224463939666748, 0.412797927856445, 0.193319797515869]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.958333313465</td>\n",
+ " <td>0.139601364732</td>\n",
+ " <td>[0.966666638851166, 0.966666638851166, 0.958333313465118]</td>\n",
+ " <td>[0.122705578804016, 0.0809410735964775, 0.139601364731789]</td>\n",
+ " <td>0.966666638851</td>\n",
+ " <td>0.131209135056</td>\n",
+ " <td>[0.966666638851166, 0.966666638851166, 0.966666638851166]</td>\n",
+ " <td>[0.115778811275959, 0.0698963403701782, 0.131209135055542]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=4,epochs=1</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.7900390625</td>\n",
+ " <td>[0.160850048065186, 0.224483013153076, 0.163106918334961]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.966666638851</td>\n",
+ " <td>0.0839553326368</td>\n",
+ " <td>[0.966666638851166, 0.908333361148834, 0.966666638851166]</td>\n",
+ " <td>[0.124577566981316, 0.196399554610252, 0.0839553326368332]</td>\n",
+ " <td>0.966666638851</td>\n",
+ " <td>0.074150800705</td>\n",
+ " <td>[0.966666638851166, 0.866666674613953, 0.966666638851166]</td>\n",
+ " <td>[0.137340381741524, 0.232466518878937, 0.0741508007049561]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=4,epochs=1</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.7900390625</td>\n",
+ " <td>[0.14374303817749, 0.154287099838257, 0.17367696762085]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.966666638851</td>\n",
+ " <td>0.0860244855285</td>\n",
+ " <td>[0.966666638851166, 0.841666638851166, 0.966666638851166]</td>\n",
+ " <td>[0.0824147835373878, 0.337884455919266, 0.0860244855284691]</td>\n",
+ " <td>0.933333337307</td>\n",
+ " <td>0.0704526007175</td>\n",
+ " <td>[0.966666638851166, 0.866666674613953, 0.933333337306976]</td>\n",
+ " <td>[0.0690516456961632, 0.295713990926743, 0.0704526007175446]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=8,epochs=1</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.7900390625</td>\n",
+ " <td>[0.155812978744507, 0.158360004425049, 0.159363031387329]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.833333313465</td>\n",
+ " <td>0.344228476286</td>\n",
+ " <td>[0.666666686534882, 0.675000011920929, 0.833333313465118]</td>\n",
+ " <td>[1.01126325130463, 1.33927237987518, 0.344228476285934]</td>\n",
+ " <td>0.800000011921</td>\n",
+ " <td>0.305708706379</td>\n",
+ " <td>[0.699999988079071, 0.699999988079071, 0.800000011920929]</td>\n",
+ " <td>[1.02303433418274, 1.36952638626099, 0.305708706378937]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>11</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=4,epochs=1</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>1.2197265625</td>\n",
+ " <td>[0.187958955764771, 0.186024904251099, 0.501762866973877]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.725000023842</td>\n",
+ " <td>0.423261642456</td>\n",
+ " <td>[0.658333361148834, 0.658333361148834, 0.725000023841858]</td>\n",
+ " <td>[0.46866175532341, 0.445532470941544, 0.423261642456055]</td>\n",
+ " <td>0.699999988079</td>\n",
+ " <td>0.378630697727</td>\n",
+ " <td>[0.699999988079071, 0.699999988079071, 0.699999988079071]</td>\n",
+ " <td>[0.422465175390244, 0.398104608058929, 0.378630697727203]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>5</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=4,epochs=1</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.7900390625</td>\n",
+ " <td>[0.176413059234619, 0.169157981872559, 0.15624213218689]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.675000011921</td>\n",
+ " <td>0.470171242952</td>\n",
+ " <td>[0.641666650772095, 0.658333361148834, 0.675000011920929]</td>\n",
+ " <td>[0.504463493824005, 0.486825525760651, 0.470171242952347]</td>\n",
+ " <td>0.699999988079</td>\n",
+ " <td>0.436036229134</td>\n",
+ " <td>[0.699999988079071, 0.699999988079071, 0.699999988079071]</td>\n",
+ " <td>[0.470719456672668, 0.452698260545731, 0.436036229133606]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>12</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=8,epochs=1</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>1.2197265625</td>\n",
+ " <td>[0.164397954940796, 0.486438035964966, 0.192479133605957]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.550000011921</td>\n",
+ " <td>0.975017726421</td>\n",
+ " <td>[0.508333325386047, 0.533333361148834, 0.550000011920929]</td>\n",
+ " <td>[1.00239539146423, 0.986684203147888, 0.975017726421356]</td>\n",
+ " <td>0.466666668653</td>\n",
+ " <td>0.981434583664</td>\n",
+ " <td>[0.5, 0.466666668653488, 0.466666668653488]</td>\n",
+ " <td>[1.00223970413208, 0.989481270313263, 0.98143458366394]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>8</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=8,epochs=1</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>1.2197265625</td>\n",
+ " <td>[0.467766046524048, 0.198179006576538, 0.186810970306396]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.341666668653</td>\n",
+ " <td>1.10613942146</td>\n",
+ " <td>[0.316666662693024, 0.316666662693024, 0.341666668653488]</td>\n",
+ " <td>[1.1275190114975, 1.10920584201813, 1.10613942146301]</td>\n",
+ " <td>0.300000011921</td>\n",
+ " <td>1.10817503929</td>\n",
+ " <td>[0.400000005960464, 0.400000005960464, 0.300000011920929]</td>\n",
+ " <td>[1.10070872306824, 1.09047472476959, 1.10817503929138]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>7</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=4,epochs=1</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>1.2197265625</td>\n",
+ " <td>[0.467660903930664, 0.195011138916016, 0.185934066772461]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.341666668653</td>\n",
+ " <td>1.10524618626</td>\n",
+ " <td>[0.316666662693024, 0.341666668653488, 0.341666668653488]</td>\n",
+ " <td>[1.10246300697327, 1.09976887702942, 1.10524618625641]</td>\n",
+ " <td>0.300000011921</td>\n",
+ " <td>1.10809886456</td>\n",
+ " <td>[0.400000005960464, 0.300000011920929, 0.300000011920929]</td>\n",
+ " <td>[1.09229254722595, 1.09808218479156, 1.10809886455536]</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(6, 1, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']\", u'batch_size=8,epochs=1', u'madlib_keras', 0.7900390625, [0.17091703414917, 0.163390159606934, 0.155634164810181], [u'accuracy'], 0.958333313465, 0.31917694211, [0.958333313465118, 0.958333313465118, 0.958333313465118], [0.348434448242188, 0.334388434886932, 0.319176942110062], 1.0, 0.272621482611, [1.0, 1.0, 1.0], [0.306039541959763, 0.28966349363327, 0.272621482610703]),\n",
+ " (10, 2, u\"loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']\", u'batch_size=8,epochs=1', u'madlib_keras', 1.2197265625, [0.172316074371338, 0.188217163085938, 0.503840208053589], [u'accuracy'], 0.899999976158, 0.193531006575, [0.958333313465118, 0.925000011920929, 0.899999976158142], [0.147025644779205, 0.144938006997108, 0.193531006574631], 0.966666638851, 0.153077676892, [0.966666638851166, 0.966666638851166, 0.966666638851166], [0.132363379001617, 0.116448685526848, 0.153077676892281]),\n",
+ " (4, 1, u\"loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']\", u'batch_size=8,epochs=1', u'madlib_keras', 0.7900390625, [0.147105932235718, 0.158121824264526, 0.174723863601685], [u'accuracy'], 0.966666638851, 0.100400544703, [0.966666638851166, 0.908333361148834, 0.966666638851166], [0.112152323126793, 0.197978660464287, 0.100400544703007], 0.966666638851, 0.0844493880868, [0.933333337306976, 0.966666638851166, 0.966666638851166], [0.0945712551474571, 0.170254677534103, 0.0844493880867958]),\n",
+ " (9, 2, u\"loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']\", u'batch_size=4,epochs=1', u'madlib_keras', 1.2197265625, [0.224463939666748, 0.412797927856445, 0.193319797515869], [u'accuracy'], 0.958333313465, 0.139601364732, [0.966666638851166, 0.966666638851166, 0.958333313465118], [0.122705578804016, 0.0809410735964775, 0.139601364731789], 0.966666638851, 0.131209135056, [0.966666638851166, 0.966666638851166, 0.966666638851166], [0.115778811275959, 0.0698963403701782, 0.131209135055542]),\n",
+ " (1, 1, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']\", u'batch_size=4,epochs=1', u'madlib_keras', 0.7900390625, [0.160850048065186, 0.224483013153076, 0.163106918334961], [u'accuracy'], 0.966666638851, 0.0839553326368, [0.966666638851166, 0.908333361148834, 0.966666638851166], [0.124577566981316, 0.196399554610252, 0.0839553326368332], 0.966666638851, 0.074150800705, [0.966666638851166, 0.866666674613953, 0.966666638851166], [0.137340381741524, 0.232466518878937, 0.0741508007049561]),\n",
+ " (3, 1, u\"loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']\", u'batch_size=4,epochs=1', u'madlib_keras', 0.7900390625, [0.14374303817749, 0.154287099838257, 0.17367696762085], [u'accuracy'], 0.966666638851, 0.0860244855285, [0.966666638851166, 0.841666638851166, 0.966666638851166], [0.0824147835373878, 0.337884455919266, 0.0860244855284691], 0.933333337307, 0.0704526007175, [0.966666638851166, 0.866666674613953, 0.933333337306976], [0.0690516456961632, 0.295713990926743, 0.0704526007175446]),\n",
+ " (2, 1, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']\", u'batch_size=8,epochs=1', u'madlib_keras', 0.7900390625, [0.155812978744507, 0.158360004425049, 0.159363031387329], [u'accuracy'], 0.833333313465, 0.344228476286, [0.666666686534882, 0.675000011920929, 0.833333313465118], [1.01126325130463, 1.33927237987518, 0.344228476285934], 0.800000011921, 0.305708706379, [0.699999988079071, 0.699999988079071, 0.800000011920929], [1.02303433418274, 1.36952638626099, 0.305708706378937]),\n",
+ " (11, 2, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']\", u'batch_size=4,epochs=1', u'madlib_keras', 1.2197265625, [0.187958955764771, 0.186024904251099, 0.501762866973877], [u'accuracy'], 0.725000023842, 0.423261642456, [0.658333361148834, 0.658333361148834, 0.725000023841858], [0.46866175532341, 0.445532470941544, 0.423261642456055], 0.699999988079, 0.378630697727, [0.699999988079071, 0.699999988079071, 0.699999988079071], [0.422465175390244, 0.398104608058929, 0.378630697727203]),\n",
+ " (5, 1, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']\", u'batch_size=4,epochs=1', u'madlib_keras', 0.7900390625, [0.176413059234619, 0.169157981872559, 0.15624213218689], [u'accuracy'], 0.675000011921, 0.470171242952, [0.641666650772095, 0.658333361148834, 0.675000011920929], [0.504463493824005, 0.486825525760651, 0.470171242952347], 0.699999988079, 0.436036229134, [0.699999988079071, 0.699999988079071, 0.699999988079071], [0.470719456672668, 0.452698260545731, 0.436036229133606]),\n",
+ " (12, 2, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']\", u'batch_size=8,epochs=1', u'madlib_keras', 1.2197265625, [0.164397954940796, 0.486438035964966, 0.192479133605957], [u'accuracy'], 0.550000011921, 0.975017726421, [0.508333325386047, 0.533333361148834, 0.550000011920929], [1.00239539146423, 0.986684203147888, 0.975017726421356], 0.466666668653, 0.981434583664, [0.5, 0.466666668653488, 0.466666668653488], [1.00223970413208, 0.989481270313263, 0.98143458366394]),\n",
+ " (8, 2, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']\", u'batch_size=8,epochs=1', u'madlib_keras', 1.2197265625, [0.467766046524048, 0.198179006576538, 0.186810970306396], [u'accuracy'], 0.341666668653, 1.10613942146, [0.316666662693024, 0.316666662693024, 0.341666668653488], [1.1275190114975, 1.10920584201813, 1.10613942146301], 0.300000011921, 1.10817503929, [0.400000005960464, 0.400000005960464, 0.300000011920929], [1.10070872306824, 1.09047472476959, 1.10817503929138]),\n",
+ " (7, 2, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']\", u'batch_size=4,epochs=1', u'madlib_keras', 1.2197265625, [0.467660903930664, 0.195011138916016, 0.185934066772461], [u'accuracy'], 0.341666668653, 1.10524618626, [0.316666662693024, 0.341666668653488, 0.341666668653488], [1.10246300697327, 1.09976887702942, 1.10524618625641], 0.300000011921, 1.10809886456, [0.400000005960464, 0.300000011920929, 0.300000011920929], [1.09229254722595, 1.09808218479156, 1.10809886455536])]"
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT * FROM iris_multi_model_info ORDER BY validation_metrics_final DESC;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Plot validation results:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "7 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
+ " if (typeof(WebSocket) !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support.' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
+ "\n",
+ " this.ws = websocket;\n",
+ "\n",
+ " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ "\n",
+ " if (!this.supports_binary) {\n",
+ " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
+ "\n",
+ " this.context = undefined;\n",
+ " this.message = undefined;\n",
+ " this.canvas = undefined;\n",
+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
+ " if (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+ " }\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " }\n",
+ "\n",
+ " this.imageObj.onload = function() {\n",
+ " if (fig.image_mode == 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onunload = function() {\n",
+ " fig.ws.close();\n",
+ " }\n",
+ "\n",
+ " this.ws.onmessage = this._make_on_message_function(this);\n",
+ "\n",
+ " this.ondownload = ondownload;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option)\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width, fig.canvas.height);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var width = this.canvas.width/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " event.shiftKey = false;\n",
+ " // Send a \"J\" for go to next cell\n",
+ " event.which = 74;\n",
+ " event.keyCode = 74;\n",
+ " manager.command_mode();\n",
+ " manager.handle_keydown(event);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i<ncells; i++) {\n",
+ " var cell = cells[i];\n",
+ " if (cell.cell_type === 'code'){\n",
+ " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " var data = cell.output_area.outputs[j];\n",
+ " if (data.data) {\n",
+ " // IPython >= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
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4LK4Bv9BasH8vduASKIdt8UbNByv1Fu0cfsaXN7X/+IXv1ABaf3FDNTqaXFqfL3qNGME+8DfsUfteswBb2DxlhVvW7USrBDX31vXQOuEo9SaJx7eCOONTiQKjCkc18bbWzzc9JZICnHwGNWOpPrrX3sD6+/oKY514DiKv6Or4a6tXhasRwIkQAIkEJgA7Tradd67I5Ddqtee9v4sgHhwmlcWhDu8EIedjZf1sNdwrFWzP/3t0CeeeEJ9zoB9gReySFCAF8q+J1uCub/RDsLNINQHvO30Fj32vF4hzvelve8Y4OWGUDf+jp7iqC6O7EKMwzFgb6EDzhAQF3Gdv5feWj9a/GR4GcLuZSGBSBCAkAaPU3wmxB4MtuBatAEHFIQg6q9QiOuPEP+elAQ0V3FfYUV7c4VjghCr/BW4WUOs04K8og4ewjj2hocJBDnchCjwuPNOHhCKEAfXbxztROlLIEGGTBgF/oQ4GB/w9tFcbdEWhAUo/ZrLrZYdFfHcvAPU6nlwo71AAg+OAOAoAPqB626gNwva+NEW3ipo/wHq6b8vw6G/Ddxfkglcr3k++Qbh9W47kGCGtyL4kKC3+PYRrBAHzz1klApUAs0X3l6IdYf1wTr154qtdz7aMRAcQ8Be1lsiKcQF8nTUxuKdjAHxTvDmWisQJyFSwmsOx1O9S7hrq5cF65EACZAACfRNgHYd7TpthwSyG/Xa059++qnn5R3+jZhyKHgZDBEMp0+0AlsJnv6IyQvvL7zo9y546Q/vOcSi0xIN4O+IUYsjmBDpEMM2mKKFewnmWCraD9ee1q5HeBF/L/K95+CdjAGxhb3jCuMzFsK2+J4gAR+EV/Hn2BCID2JEw65lIYFIEIC3Jk7C4P7U49Hm2yfuC8RKxGdsJHnrr1CI648Q/56UBDQvF0xOS68NFRtuqLiJfD+MaxDwEEaMCLwhQ9IDHHGFmOft9YZ4BdrxOy1TZX/GQV8PSL2GQ19CHDzE4BUEAwFzw1s0b6UenljaHPpLVx5oQ1CIu0j++te/qsQR3kYBPMHwHzsC1yJRQ7AlWCEOgWtxTbDrpCUtiLQQp8U3jKcQ53sf+mOjxYZEIgrEk0OBkI6MxDAKV69erRI2eJdw1zbYvcD6JEACJEAC/gnQrqNd15+trdeeDiTEae3DnoYYh6OXSLSgZVuFQIXQIt5xjrVr8FkDYUMQYgUvvCFUaQX2B04O6C2a6BwvIU5vCBxkQoUIhyOsOMqK8tFHH6mY1ig4UYSEDFrBS1EIcfiOuNh4OcxCArEkAA9N7D/vfRls/9jX2vHU/q6lENcfIf49KQnAgwqu5vC80h6AWgYiPEjhQePtyaZB0I6w4UGBOt7ZlLQ6yJ4J7zOUSAhxel3pEbMCD3/fB+S2bduUso+C2Bb+3rzhPx7tLV6khTjvo6l9eSb1dzQ11CxP/W3gaHvE4dgCkjTAoIBhEWyJlRAHoQneeCiRPJqKTKjIchvs0VQt+xbGgyOtuN5fgVcnjjL7CpB6Peq0Njdu3KiywXrfJzjugGDNEOiRedbXSzDctQ12L7A+CZAACZCAfwK063pySWW7Ts/RVIhoJ5xwgt/NFOhoqr/KeHmP46F40Qr7GXYCXsjD6ytQwQd91IVthEyrKPC8h4ecnnLxxRcrmyfYo6neGeCRSMufx493XOdQPw9oc9A44nMVHBiQIAOx7RDjDvYT2vctONKHMYAnXmCzkEAsCeCzB4qvZ2swYwimDQpxwZBl3aQi8P/+3/9Tcd4gyO3du1e5oSM+2nnnndfD7dx70lqsh75EBS0mFq6LhBDnHVwWHng33XST33VAkH7EFvMVrP7v//5PZauEcQCDwd+RQ7jML1y4ULXr++DVBBrEZoNHYKASbrIGcH/ttdeUYQQDSSvhutL3t2mjLcRByIGgg30GzziIuMEUzeBCrA2sU6CiGS+hesTh7SySNWC/9ZfcIJjxexu0wSRrOHTokCcAMTJrIR6bb4HnmnZkJFwhDm1rgZivvfZaFRMQ/yfgzTgy8iLhhm8Jd22D4ci6JEACJEACfROgXdfNJ5XtunCTNSBp1n333adewuEFsp6CF/tImgAbCi+WsRf7K7DJcXwN3veIRXz33Xf3d4n6u/eL62CSNaxfv15mzJih2sDLRcRj8y1ImobkaSjhCnEIcwKGSHKG5BdXXXWVir2FE0j4/IWffYv2WQbjhPccCwnEkkAwIlqgcQXTBoW4WK4u+zIUAbiWa0H98dYFGZRQ8BDSEjb4Dvill14SBOXH2x28sfENtgpPInicaS7nkRDiMAYtzhbEFnjI+QaAhFiAo7J46PkKcd4P3s8++0wmT57cY1owHjBmiET+HrxwoT/++OPV33CEFR5I/kpfgpbmRo904xiPbwBWPGzRhz8DJtGFOKwXPK3wBhSBfrV95o8hhE6soXdSBwikMKgR4BdHXwOVcIU4tKsdI4A3KLwn/aWND/YmxltXxFKDsQhBGOuJ+0dPwbFQJPhARixkxvIt2njx+0gIcegDgX/BEuPUvEfhLaclTPEeQ7hrq4cB65AACZAACegjQLvOzSnV7bq+7EYtXAZOtMBzzTfjKGxhhJdBAi1vcQzxy2CfBXqZCs/8/Px8gc3jLTLBroNdHqhAiIMzQKAXfv6uQxxqJNTCkdhgjqdijIi/DAEQp4AgOHoX2OB46al5qoUrxKFtTdSEsIbPWvh8hRf7+HzkL2GFd+IwOD/g80+ggljdiM+t16bU978Ia6UygWBEtECcgmmDQlwq7zbO3ePxAi8xCCWad1ygAI14+OENEo5AQFSAQAKRAQ8vPPjxBgwPb3jzoERKiMMRA4g5eIjiYfbII4+osWPMeGghQCweyDC+fIU4GA4YM964QVjA8Uh8h1EBzzMcZ4XYoY3Z98Hr7SmFN4RwK/dX+hLiIOYhSxQ4IcgsjtFOmDBB/QyPKcRVg7CJ30Gow4NVK4kuxGEe2vFM/BtGIDypIEqiwCCCpxi8AZHFF9+1N5b4u/bmEy79//73vwO6S0dCiMMRUHiFYR8hm9Xvfvc7+c53vqOEU+w1CFJIvoFkHhDB9BbvRBw4SoHjGGgD8ehgpGIfIsMWMmnhftIKkiZgDNjT8NJD9lLsDRjKEDVfeOEFtY9xP0ZCiMMehPiHewZrgAy8uM/w4S5QCWdt9fJjPRIgARIgAX0ENE9m2nWpa9f1ZTfCzoEnPUQcfIcdryUoQzK2BQsWCEK6QKiDbaaFoIGdAfsEwtA555yjrtUENsSEQpgb2DoQhRAaQ7NlYOsh7AdOfeCFufYyG+IbTrnAnkdB/DrE0tVbYI9rQhq8yCByaeE18NIcYXLQNhwIvIVAJE1ArDqIhhC6EL8N4iJeLEJ4RCgVZERFiYQQ5+0MAOcH/IzEcjjBE6hoJ0FwD8NeRrILLTwJ1gE2GeaFzzNg7yum6mXIeiTgSyAYES0QvWDaoBDHPZjSBDSRQ4OA7JgQCfoqmteMVgcZU/EGDIICPInwUMcDFyVSQhzaQmBYiB9apiL0C8EA4hzEAzyEYUD4i6WGBxY8qiCmoMAQgICBa9EO/q7FpvB98KI+BBAEQkaBt5b20EN2TognKP0d8VyxYoXKDqWNH2/lwAwPVRQIPxAVfQNkJoMQB+44soxEGVrBGkDggtDpnSEK4o/mqYm6cOlHrJGGhgYlXIG9ZshhT+BDB0okhDi0AyMM+xfCLQpEaRhsEJi1o8kPPfSQ5yiz3v9AILRBqNbWH/sUCUIg+mnzR6Zi77gq+BsMZBha2liwXzXB+dlnn1UGGgS0SAhx6OPMM88UxDXUir+3xt5zDmdt9bJjPRIgARIgAX0EaNfRruvPbkS8Z4hXEONQYIvAJoaAhYIwHYjdpgl0+B1sVe+XxLCNYMfiJbhmx0I4wotmLdSLt22Gf8OGwzX4zKAleMDvYR/ecccd+ja4Vy3EUfvNb37jycaq2ZWwkTR7H3a+9ykUnNzBvGBbokCEw9/BAmODQAZHA5RICHFoBx6G8D7UCmxX7XOSv0mDJ8Kx4HODVrBGGCtsZm1u+BscJLBeLCQQCQLBiGiB+gumDQpxkVg1tpGwBCBuIF6B9hD1zeATaGLwWkIMKbyVwQMBQhg+wEPIQ5vw7EKJpBCH9hATC2/Q8Obs8OHDaux4M7do0SLlvYP06YGSGiBTEx70eLBivhBukAUSY0YMB83A8CfE4UGOa/Hw/Oqrr5SAh+KdIbQ/IQ71kTUKHk54U4fArXBPh/D23e9+V3naQWTxLf0ZVKjvbSD5G39fG1TPuMEIMcLwVhPj8Vf0JFXA0WCIWFg/HNWEsIVjxuPGjVOx8fCW0Nvw0/qBJ9rSpUuV5xhia2hilvdcIyXEoU8YOjAmsc+xZlhvtI/gpYgdgreVobyBhGcn3uLCCEZ8Ehil8ELFcWkc+YZhjD3hXeCpibnjTTP2DHjBqw6eaHgjHWjewSZr0PrE8V8t6yzebMNb1F9SFt89EOraJux/nhw4CZAACRiQAO062nV67EY822GPvvHGGx57BF5XODoJLyycQvAuEOoQMxmJGd5//311DV4Cwo7BqZNvfvOb6oSJb0I01IXNg1i3EMFwDV4+wvZBSBaIdrg21ILQNDgxALsSNhLEKnw2gPcdvN8wH4zRu+BUAcKk4OU3bErYOHgZf/PNN6txBfo8oIerv3nAfoYdjQLRDJ+N9MRLBrPHH39c2b64Bs4HsD3xGQvMMD/tdEmo/HgdCXgTCEZEC0QumDYoxHH/kQAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJpCSBYEQ0CnEpuUU4aRIgARIgARIgARIgARIgARIgARIgARIggUgQoBAXCYpsgwRIgARIgARIgARIgARIgARIgARIgARIgAT6IYAQOjg6Pnbs2JBY4Qg7kr0gtM2oUaP6bYNHU/tFxAokQAIkQAIkQAIkQAIkQAIkQAIkQAIkQALJSADxu5G4BCKad7ZhvXNFwhfEj0TiE8Rq7K9QiOuPEP9OAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiSQlASQCBGJXJBJGEKaXjEOXnTNzc2eZCxI/OKdZTkQLApxSbmNOCkSIAESIAESIAESIAESIAESIAESIAESIIH+COBoaVVVlTQ2NqqqFotFzGZzn5fhGmT0RUGG4vLycsnPz++vK3d9F65mIQESIAESIAESIAESIAESIAESIAESIAESIIEUJABpDMdT4R3X0dEh/UllEN8QEy4nJ0fy8vJ0e9FRiEvBzcUpkwAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJxIcAPeLiw529kgAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJpBgBCnFeC15XVyf//Oc/RW+AvRTbK5wuCZAACZAACZBAAAJtbW2ya9cuOe2006S4uJicwiRAmyxMgLycBEiABEiABFKYgNHtMgpxXpvzmWeekblz56bwduXUSYAESIAESIAEwiGwcuVKueSSS8JpgteKCG0ybgMSIAESIAESIIFwCRjVLqMQ57Wy77//vnzjG98QLNaECRPCXXNeTwIkQAIkQAIkkCIENm/erF7mvffee3LCCSekyKyjN03aZNFjy5ZJgARIgARIINkJGN0uoxDntQP/+9//yvTp02X9+vUybdq0ZN+bnB8JkAAJkAAJkECECNCGiBDIrmbIM7I82RoJkAAJkAAJpBIBo9sRFOIoxKXS/ci5kgAJkAAJkEBUCBjd4IvKpKPYKHlGES6bJgESIAESIIEkJ2B0O4JCHIW4JL8FOT0SIAESIAESiD4Boxt80ScQ2R7IM7I82RoJkAAJkAAJpBIBo9sRFOIoxKXS/ci5kgAJkAAJkEBUCBjd4IvKpKPYKHlGES6bJgESIAESIIEkJ2B0O4JCHIW4JL8FOT0SIAESIAESiD4Boxt80ScQ2R7IM7I82RoJkAAJkAAJpBIBo9sRFOIoxKXS/ci5kgAJkAAJkEBUCBjd4IvKpKPYKHlGES6bJgESIAESIIEkJ2B0O4JCHIW4JJW1h9oAACAASURBVL8FOT0SIAESIAESiD4Boxt80ScQ2R7IM7I82RoJkAAJkAAJpBIBo9sRFOIoxKXS/ci5kgAJkAAJkEBUCBjd4IvKpKPYKHlGES6bJgESIAESIIEkJ2B0O4JCHIW4JL8FOT0SIAESIAESiD4Boxt80ScQ2R7IM7I82RoJkAAJkAAJpBIBo9sRFOIoxKXS/ci5kgAJkAAJkEBUCBjd4IvKpKPYKHlGES6bJgESIAESIIEkJ2B0O4JCHIW4JL8FOT0SIAESIAESiD4Boxt80ScQ2R7IM7I82RoJkAAJkAAJpBIBo9sRFOIoxKXS/ci5kgAJkAAJkEBUCBjd4IvKpKPYKHlGES6bJgESIAESIIEkJ2B0O4JCHIW4JL8FOT0SIAESIAESiD4Boxt80ScQ2R7IM7I82RoJkAAJkAAJpBIBo9sRFOIoxKXS/ci5kgAJkAAJkEBUCBjd4IvKpKPYKHlGES6bJgESIAESIIEkJ2B0O4JCHIW4JL8FOT0SIAESIAESiD4Boxt80ScQ2R7IM7I82RoJkAAJkAAJpBIBo9sRQQlx69evlzfffFPWrVunvvbv36/W0uVyhbSmDQ0NsnjxYnn11VelqqpKysvLZc6cOep3+fn5ftt0OBxy//33yxNPPCHbt2+X3NxcmT17tixZskQmTJgQ0ji0i4y+WGFNjheTAAmQAAmQAAlEjQBtiMiiJc/I8mRrJEACJEACJJBKBIxuRwQlxJ133nny2muv9Vq/UIS4uro6Of7445WYNmrUKJkxY4Zs2rRJfY0dO1b+85//SGFhYY++nE6nXHDBBfLKK68ooe6UU04RtPPOO+9IVlaWrFmzRo499tiQ95fRFyvkifFCEiABEiABEiCBqBKgDRFZvOQZWZ5sjQRIgARIgARSiYDR7YighLi77rpLWlpaZObMmeprxIgR0tHREZJH3Ny5c+WZZ56R7373u/LXv/5VrFar2hc/+9nP5A9/+IPMnz9fnnrqqR575bHHHpMFCxbImDFj5N1335WysjL195deekkJdJWVlbJ582ZPW8FutGguVm3HbtnRvE5szlZJN2dLZe6xUpwxPNghsj4JkEAcCPD+jQN0dkkCESQQi3s4mjZEBFEkTFPR5LmvY7d82rxO2pytkmXOlmNyj5UK2mQJszc4UBIgARIgARLoj0A07Yj++tbz96CEON8GMzMzQxLiDh48KBUVFUow27Nnj0dQQ/sQ9oYOHSr19fVy4MABKS0t9XQ7ceJEJbTBIw7eed7l3HPPlVWrVsmLL74o559/vp6596oTjcVqtFXJWzUPy4H2Lb36G5w5Xr5VulDy08tDGi8vIgESiC4B3r/R5cvWSSDaBGJ5D0fDhog2HyO3Hw2eNbYqWVnzsGz3Y5NVZo6XuaULpZQ2mZG3BcdGAiRAAiRAAroIRMOO0NWxzkpxEeKefPJJueyyy9TR0rfeeqvXUH/0ox+pGHCod+mll6q/79y5Ux1hxRHUpqYmSUtL63HdihUrZN68eX496XSykEgvVqPtoDy/7xZpdx4JOIRMc558r2IpxTi9i8R6JBAjArx/YwSa3ZBAlAjE+h6OtA0RJSwJ02ykedbYDsrd+26Rlj5sshxznlxfsZRiXMLsEg6UBEiABEiABPwTiLQdEWnOcRHifv7zn8t9990n119/vfz2t7/tNac//vGP8pOf/ESuueYaWb58ufo7EjogkQOOxCJRhG9BbLlJkybJ1KlTlaAWSon0Yr24b7FfTzjfscEz7oKKxaEMmdeQAAlEiQDv3yiBZbMkECMCsb6HI21DxAiTYbuJNM/l+xb79YTzBQDPuGtpkxl2X3BgJEACJEACJKCHQKTtCD19BlMnLkIc4sLheCnEOMSE8y1ICIGjp6iH+G8oyJR69dVXKzHu5Zdf7nUNvOSQwAEJHg4dOhQMA0/dSC4W4tH8Ze8Nusdx8dC7GDNONy1WJIHoEuD9G12+bJ0Eok0gHvdwJG2IaPNJhPYjyRMx4e4Iwia7cehdjBmXCJuEYyQBEiABEiCBAAQiaUdEA3JchLhvf/vb8uabb8qjjz4ql19+ea954bjqqaeeqr5Wr16t/n7HHXfITTfdJJdccomsXLmy1zV2u10dV8WXzWbrlxXi1OHLuyD+HJJIrF+/XqZNm9ZvG31V+ODQC7KuwS0i6inZlnzJsRboqco6JEACUSbQYm+QVkej7l54/+pGxYokEBMCwd7DxxVeIPgKpxjd4AtnbvG4NpI8/3boBfl7EDZZrmWAjMocIyVp5VKSVub5XmAtFovJEg8c7JMESIAESIAESCAIApG0I4LoVnfVlBXiFi9eLEuWLPELKhJC3Du1T8unTW/oXghWJAESIAESIAESiA+BYwaeId8smR9W50Y3+MKaXBwujiTPF2qfljURsMnMYpHitBIlzBUrgc4t0pWmlUthWomkmXrGL44DNnZJAiRAAiRAAiQgEvH4/5GGGhchzghHU+kRF+mtxPZIIHkIBOtNQ4+45Fl7ziQ5CAR7D9MjznjrHkkhLliPuAGWfLG5OqTd2aYbjElMUmAt6vKe0zzp3EJdcVqpZJgzdbfFiiRAAiRAAiRAAuERiKQdEd5I/F8dFyEuFZI1xCM+TTQ2CNskgVQkwPs3FVedc04mAvG4h41u8CXa+kaSZygx4oakD1MZVms7q6Wms0rqOqultrNKam3u7819ZF/1x3qgpaDHMVd41MGTDkJdliU70ZaH4yUBEiABEiABQxOIpB0RjYnGRYh78skn5bLLLpNTTjlFEA/Ot/zoRz+SJ554QlDv0ksvVX/euXOnjBo1SrKysgSJGRALzrusWLFC5s2bJ/Pnz5ennnoqJFaRXqxYZ2wLadK8iARIwC8B3r/cGCSQ2ARifQ9H2oZIbPrhjz7SPCOdNbXN0apEOiXOqS/82/1zk6MhKAC55ryuo65dnnTp7uOuEOvwN5PJFFR7rEwCJEACJEACqU4g0nZEpHnGRYjDsdCKigqxWq2yd+9eKS0t9cyro6NDhg4dKvX19XLgwIEef5s4caIgoQIyriKrqnc599xzZdWqVfLiiy/K+eefHxKnSC9Wo61Knt93s7T38dY005wn36tYKvnp5SGNmReRAAlEhwDv3+hwZaskECsCsb6HI21DxIqTUfuJNM8aW5Xcve9m5eUWqOSY8+T6iqVSGqZN1uFsl7rOGi+hzi3Qwauu3l4nLnHpxp5pzvJKGqEljnALdvCyo0inGyUrkgAJkAAJpBCBSNsRkUYXVSHugQceEHzNmTNHli1b1mPsyE76zDPPKNHsueeeU6IcytVXXy3333+/X8+2xx57TBYsWCBjxoyR9957zyPSvfzyy6qdyspKJdRpbQULKxqLhQ8Cb9U8LAfat/QazuDM8fKt0oUU4YJdKNYngRgR4P0bI9DshgSiRCCW93A0bIgoYUmIZqPBE2LcypqHZbsfm6wyc7zMLV0YtgjXH9xOV6fUd9Z6HXft9qqr66wVpzj6a8Lz9zRTeoDjrmWCDK9mk1l3W6xIAiRAAiRAAslEIBp2RCT5BCXEvf7667J06VJP/+vWrROXyyXHHXec53c333yznHnmmepnLTOpv+OidXV18rWvfU127Ngho0ePlhkzZsimTZtk48aNSmj74IMPpLCwsMdcnU6nXHDBBcojrqCgQB1tRTtvv/22ZGZmypo1a3qMJVhQ0Vysuo7dsqPlI+lwtEiGJUdG58yU4ozhwQ6R9UmABOJAgPdvHKCzSxKIIIFY3MPRtCEiiCJhmoomT8SM+6zlI2l1tEi2JUem5MyUCgPYZA6XQxrsh/wed4U3HUQ8vcUiFilKK/XyputOIIHfW03uF+AsJEACJEACJJCMBKJpR0SCV1BCHGKv/fCHP+yzX++4bn0JcWgEx09R59VXX5Xq6mopKytT3nNLliyR/Px8v/04HA657777VAw5iHg5OTkye/ZsdQ2OroZTjL5Y4cyN15IACZAACZAACUSPAG2IyLIlz548nS6nHHY0Kk86LWlEnb07gUS7K7gMr4XWki5vup7HXZHhNd2cEdnFZGskQAIkQAIkEGMCRrcjghLiYswu5t0ZfbFiDoQdkgAJkAAJkAAJ6CJAG0IXJt2VyFM3KnU6pdlxuFdMOncSiZo+4+L56yXfUtiVPMIt0iFmXrG1TErSyyTLzAyv+leGNUmABEiABOJFwOh2BIU4r51h9MWK1yZmvyRAAiRAAiRAAn0ToA0R2R1CnpHj2epo9srq2jPTK7zsgim5lgE94tJBqEPiCHzPMecyeUQwMFmXBEiABEggagSMbkdQiKMQF7XNz4ZJgARIgARIIFUIGN3gS7R1IM/YrFi7yvAKca47s6vbk65axasLJsMrvOW8hTm3QOcW6QZY8inSxWZJ2QsJkAAJkICIGN2OoBBHIY43KgmQAAmQAAmQQJgEjG7whTm9mF9OnjFH3qvDTqdNDtlru5JHaJ50btHuUGeNOMWpe5Dppoyex13Tyrp+LpcCaxEzvOomyYokQAIkQAJ6CBjdjqAQRyFOzz5mHRIgARIgARIggT4IGN3gS7TFI09jr5jDZZd6e133kVeb24vOnUCiWuxBZHi1irUrw6uWOKL7e1FaiViY4dXYm4GjIwESIAEDEjC6HUEhjkKcAW8bDokESIAESIAEEouA0Q2+xKJp/CMlicYzluNFhtdGe71bmOs68qodd8XvOlztuodjFrMUWovV8dbitDIpRUy6dLdQV2RFhtd03W2xIgmQAAmQQOoQMLpdRiGOQlzq3I2cKQmQAAmQAAlEiYDRDb4oTTtqzZJn1NDGtWFkeD3iaPKT4dV99LXV2RLU+PKthe7Mrl1CnRaTDt8zzVlBtcXKJEACJEACyUPA6HYEhTgKcclzt3EmJEACJEACJBAnAkY3+OKEJeRuyTNkdAl9YYsnwyuOunYdd+3yqjvsaApqbnmWgT4ZXrUjr+WSY8kNqi1WJgESIAESSCwCRrcjKMRRiEusO4qjJQESIAESIAEDEjC6wWdAZH0OiTwTbcWiP952Z5vPcddqqemsUsdfkeE1mJJtzuk+6tqV2RVHX90ZXgcyw2swMFmXBEiABAxIwOh2BIU4CnEGvG04JBIgARIgARJILAJGN/gSiyZjxCXaesV7vDaV4bXGK8NrtdSqBBJVKqlEMBleM0wZnph03Uddy5V3HY7Cmk3meE+X/ZMACZAACfRDwOh2GYU4CnG8iUmABEiABEiABMIkYHSDL8zpxfxy8ow58qTtEBleD3Uiw6tbmIMHXU3XcddDnTViF7vuuVtNaVJsLVVCXUma9t2dQKLQigyvFt1tsSIJkAAJkED0CBjdjqAQRyEuerufLZMACZAACZBAihAwusGXaMtAnom2Yok53u4Mr26RThPo3Nleq8Xm6tA9MWR4LUor6RLp3MdcteOuxdYSSWOGV90sWZEESIAEwiVgdDuCQhyFuHD3OK8nARIgARIggZQnYHSDL9EWiDwTbcWSb7zI8HrY0djzuGtXAgmIdm3OVt2TNolJCqxFXcJcd9IIHHeFWJdpztTdFiuSAAmQAAn0T8DodgSFOApx/e9i1iABEiABEiABEuiTgNENvkRbPvJMtBVLrfFCpGtxNvfI7IrEEVqm12bH4aCADLDkd2V47SnSQajLZobXoFiyMgmQAAmAgNHtCApxFOJ4p5IACZAACZAACYRJwOgGX5jTi/nl5Blz5OwwggTgLacdb62xVUmdXUseUS2Njvqgesox53aJdN1HXSHQlaaVS65lADO8BkWTlUmABFKFgNHtCApxFOJS5V7kPEmABEiABEggagTiZfC1tbXJsmXL5LnnnpM9e/ZIYWGhnH766bJ06VIZMmRIyPPdtm2bTJ48Wdrb2+WUU06Rt956y29bDodD7r//fnniiSdk+/btkpubK7Nnz5YlS5bIhAkTQu4/XjxDHjAvJAGdBGzODqnrRIZXxKHr9qLDz/X2WnGJS2dLIhmmzB4x6dxZXsukJL1cBloKmOFVN0lWJAESSDYCRrcjKMRRiEu2e47zIQESIAESIIGYE4iHwQeRDKLXBx98IIMGDZITTzxRdu3aJevWrZOSkhL1+1GjRoXEAu2+/fbbgiN4gYQ4p9MpF1xwgbzyyiuSn5+v6tXV1ck777wjWVlZsmbNGjn22GND6j8ePEMaKC8igQgSsKsMr5pIpwl17u/4vUMcuntLQ4ZXTZhTWV7d8ejgSVdgLWaGV90kWZEESCARCRjdjqAQRyEuEe8rjpkESIAESIAEDEUgHgbfokWL5Pbbb5fjjz9eVq9erbzRUJYvXy7XXXedzJo1S9auXRs0p8cff1wuv/xyueKKK+SRRx4JKMQ99thjsmDBAhkzZoy8++67UlZWpvp66aWXlEBXWVkpmzdvFqvVGvQY4sEz6EHyAhKIIQFkeG2w1ylPOsSj046+anHpOl023aMxi0WK00o8WV3dnnRusa4orVQg4rGQAAmQQCITMLodQSGOQlwi318cOwmQAAmQAAkYgkCsDT6bzSalpaXS1NSkAhJPnTq1B4cpU6bIhg0b5OOPP5bp06frZlRdXa2OlM6YMUNuvPFG5XEXyCNu4sSJSmiDR9x5553Xo49zzz1XVq1aJS+++KKcf/75uvvXKsaaZ9AD5AUkYCAC8FxtcjT4Pe4Koa7d2aZ7tFqGV02Y8/5enFYqGczwqpslK5IACcSPgNHtCApxFOLid3ewZxIgARIgARJIEgKxNvhw7PPkk0+W0aNHq9hsvgUx4m655Ra59dZbZfHixbopX3TRRfLaa6/J559/Lvv27QsoxO3cuVMde8URVIiBaWk9PWhWrFgh8+bNk/nz58tTTz2lu38KcUGj4gUk0CcBiHTNziPKg05ldrUhLl11l0ddlfpbMAWx5zQPOu3oK4674t/Zlpxgmgq57r6O3fJp8zpBUowsc7Yck3usVGQMD7k9XkgCJJB8BGJtlwVLkEIchbhg9wzrkwAJkAAJkAAJ+BCItcF37733yjXXXCMXXnihPP/8873W4/XXX5ezzjpL5syZIy+//LKu9fr73/8uZ555pvzmN7+Rm2++WR1rDeQR9+qrr6q2Z86cqWLS+ZZNmzbJpEmTlKce2ARbYs0z2PGxPgkkC4FWR0uvY67acVd42QVTcsx5HpHOkzgCR17TyyXXnBd2hldkoF1Z87Bsb9/Sa1iVmeNlbulCKU0vD2bIrEsCJJCkBIxuR1CIoxCXpLcep0UCJEACJEACsSMQa4Pv2muvlXvuuUeJcYgJ51s+++wzOeaYY2TatGmyfv36fkG0tLTIUUcdJZmZmepIa3p6ep9CHDKlXn311QGFPnjJIYEDsrgeOnSo3/59K8SaZ9AD5AUkkAIEOpztXRle3V503pleG+yHgsrwmmnO8hHpEJPOHZdugCW/3wyvNbaDcve+W6SlDw8+CIHXVyylGJcCe5NTJIH+CBjdjqAQRyGuvz3Mv5MACZAACZAACfRDINYGHxIpPProo3LTTTfJbbfd1mt0OK6KJAr42rp1a7/rB0EPXnY48nrSSSep+n15xN1xxx2q70suuURWrlzZq3273a6Oq+IL8ez6KgcPHhR8eRfEnps7d64SESEmspAACRiLQKerU+o7a93HXdVX93HXus5acQaV4TXd67hrqRLocNwVIh0yvJpNZlm+b7FfTzhfKvCMu7ZC/3F8Y1HlaEiABCJFINZ2WbDjphBHIS7YPcP6JEACJEACJEACPgRibfBFUohDQofjjjtOCV9PP/20Z2axEuIQw27JkiV+9xSFON5qJJB4BBwuhyfDqybSeYt1EPH0FotYZIC1QLWnt9w49C7GjNMLi/VIIEkJxNouCxYjhTgKccHuGdYnARIgARIgARKIsxAXqaOp8FxDnLc9e/bIli1bpKSkRJcQF8mjqfSI4+1EAqlDwOlyujO8ImmEvVpqbTjyqh19rZIOV3vYMKbmHCez88+QAmuR5FsLxGKyht0mGyABEkgsAhTiEmi9jL5YCYSSQyUBEiABEiCBlCIQaxsiUskadu3aJSNHjpTy8nIZN25cjzVrbGwUxJpDrLcpU6aov8FLDoXJGlJqe3OyJBATAirDq+Nw13HX7qOuW9o2yhFHU0hjMIlJxaArsBZKvrVIiXNuga7Q8++BSqyzhNQ+LyIBEjAmgVjbZcFSoEecFzGjL1awi8v6JEACJEACJEACsSEQaxsCsdxOPvlkGT16tCAenG9ZunSp3HLLLXLrrbcKjn4GKpoQp5cSPiij7Ny5U0aNGiVZWVmCxAyIBeddVqxYIfPmzZP58+fLU089pbd5T71Y8wx6gLyABEggZgT+dugF+XvDS1Hrr1us04Q6iHTF3WJdWpES8yjWRW0J2DAJRJyA0e0ICnEU4iK+6dkgCZAACZAACaQagVgbfEiAUFpaqkSwTz75RGVI9S7wYEP2U8R/mz59ekjL0VeMODQ4ceJEQVKFV155Rc4777wefZx77rmyatUqefHFF+X8888Puv9Y8wx6gLyABEggZgT2deyWO/beoLu//ym+TKwmqyCzq/dXo/2QdLg6dLfjXdEsZhlgzZcCS5EUpPX2qoOXnZ7sryF1zotIgASCJmB0O4JCHIW4oDc1LyABEiABEiABEuhJIB4G36JFi+T222+Xr3/967J69WrJyclRg1q+fLlcd911MmvWLM9RUvz+gQceUF9z5syRZcuW9buE/Qlxjz32mCxYsEBlZn3vvfeUMIjy8ssvK/GtsrJSCXVWa/DxmeLBs18grEACJBA3ApHImgqP3jZnqzTa61Xyhwb13S3WuX/n/rctDLEOx1x9j756H4elWBe3LcSOU4yA0e0ICnEU4lLsluR0SYAESIAESCDyBOJh8LW3t8tJJ50kH374oQwaNEhOPPFE2b17t/oZSRc++OADdXxUK1p2Ur3HRfsT4pxOp1xwwQXKI66goEBOOeUUqaurk7ffflsyMzMFx2eRjTWUEg+eoYyT15AACcSGQI2tSu7ed7O0OI8E7DDHnCfXVyyV0vTykAflFutapN5LnIMnnVuk6xbrOl22kPowi0UlkPAV67rj1xVKnmWgmE3mkNrnRSRAAm4CRrcjKMRRiOO9SgIkQAIkQAIkECaBeBl8bW1tyrvt2Weflb1790phYaGcfvrpghhxFRUVPWYVaSEOjTscDrnvvvvkiSeekB07diivvNmzZ8uSJUvU0dVQS7x4hjpeXkcCJBB9AhDjVtY8LNvbt/TqrDJzvMwtXRiWCKd3BhDrWp0tfo6+uoU6TbjrdHXqbbJHPW+xzi3Yeceuc/871zKAYl1IdHlRqhAwuh1BIY5CXKrci5wnCZAACZAACUSNgNENvqhNPEoNk2eUwLJZEkgCAogZ91nLR9LqaJFsS45MyZkpFRnDDTUziHUtzmavo689vercYl292EMU66xilYEq82t39ld3Jthiz+8g1plMJkNx4WBIIFYEjG5HUIijEBere4H9kAAJkAAJkEDSEjC6wZdo4Mkz0VaM4yUBEgiWAMS6ZucRjwedOvraqcWs645dZxd7sE2r+hDr3OKcj1edJ9lEkeSa8yjWhUSXFxmdgNHtCApxFOKMfg9xfCRAAiRAAiRgeAJGN/gMD9BngOSZaCvG8ZIACUSDgBLrHIe94tPVeSWW6D4K6xBHSN1bTWnKg859/NXbu677OCxi79GzLiS8vCiOBIxuR1CIoxAXx9uDXZMACZAACZBAchAwusGXaJTJM9FWjOMlARKIFwGny9kl1nUnlOhOMOHOCIuvUMW6NFO6l1gH7zpNsNOOwhZJtjmHYl28NgD79UvA6HYEhTgKcbx1SYAESIAESIAEwiRgdIMvzOnF/HLyjDlydkgCJJDEBCDWHVGedb2Pvnb/rkGcIXrWpZsyugS63l51EO4KrUWSRbEuiXeY8aZmdDuCQhyFOOPdNRwRCZAACZAACSQYAaMbfAmGU8gz0VaM4yUBEkh0AhDrDjsavY6+dsep08S6JjvEOmdIU81QYp37yGtPrzr8rlj9LsucTc+6kOjyIl8CRrcjKMRRiONdSwIkQAIkQAIkECYBoxt8YU4v5peTZ8yRs0MSIAES6JeAJtZ1e9G549R5f0Gsc4mr37b8VcgwZXqJdFqcOm/hrkiyLNkhtc2LUouA0e0ICnEU4lLrjuRsSYAESIAESCAKBIxu8EVhylFtkjyjipeNkwAJkEDUCDhcDuVZhwywjY7uTLAQ6xCrDt+bHKGLdZmmLClIK5J8S2GP7/C0074yzVlRmx8bTgwCRrcjKMRRiEuMO4mjJAESIAESIAEDEzC6wWdgdH6HRp6JtmIcLwmQAAnoJwCxDp5z3uKcd4KJBnu9EvNC9ayDEOctzHkfh9WOx2aaM/UPmDUTjoDR7QgKcTES4vZtflqq2teKw2wXi9Mq5VmzpWL8vITb0BwwCaQigfrtq6SpdrW4pENMkiEDS0+TwtFnpyIKzpkEEpLAvu1PS1XLWnGY7GJxWaU8Z7ZUVEb2GWx0gy/RFo48E23FOF4SIAESiCwBh8suTfbGXkdfNa86fA9HrEPyiIKuDLDdMet6HofNoFgX2UWNYWtGtyMoxEVZiKvesUq+tD0r7Wm9d11mp8i49IulbPQ5MdyS7IoESEAvgaa9b0tt9Z9EcvwEpW0xS0nZlTJw6Cy9zbEeCZBAjAlU71klX7Y+K+1WP89gu8i47IulbFhknsFGN/hijD7s7sgzbIRsgARIgASSnoBdiXU47uo+8qqJdN7ZYQ87mkLmkG3O8SSY0ES7nskmiiTdnBFy+7wwegSMbkdQiIuiEFe9/VX53PWcOE2BN5jZJXK0mWJc9G5BtkwCoRFo2rNWapseFvHzAd7Tol2kJP8qinGhIeZVJBBVAtW7X5XPbTqewRmREeOMbvBFFXYUGifPKEBlkyRAAiSQggQg1kGgk74bIAAAIABJREFU8z366i3cHQlDrMsx5/bIBKsdfXUfjS1UQl66OT0Fycd3yka3IyjERVGIe2fzRX494Xy3JDzjvjnhufjuVPZOAiTQg8D2jy/27wnny6nFLJUzniU9EiABgxF4Z8tFfj3hej2D7SLfHB/+M9joBp/Blqff4ZBnv4hYgQRIgARIIEIEOl2dyrOu3surzle4a3YcDrm3HHNeD7FOE+nc3nVuwS6NYl3IfP1daHQ7gkJclIQ4xIT7Iu0N3Ztpov07jBmnmxYrkkB0CSAmXH2nfnEt6+Axkp09MbqDYuskQAK6CdR3rpcdg77UXX+ifCfsmHFGN/h0wzBIRfI0yEJwGCRAAiRAAopAp9PWnQXWKxtsd8y6Q9LsPBIyrVzLAK+Yde5YdVrsukJrkQyEWGfyE+8q5B6T+0Kj2xEU4qIkxH38yQ+lPq9N9+4usIkU2umyqhsYK5JAFAm4TLa+j6T69t2Jp3MUB8SmSYAEgiJQny3SGIStWtSSLdOnPBFUH76VjW7whTW5OFxMnnGAzi5JgARIgATCImCDWGc/5BWzDv92/6x52LU4m0PuI88y0CPO9faqg1hXQLGui67R7QgKcVES4j787AfSlKP/k/kAh0ixn3jwId+lvJAESIAESIAEUpRAnVnksEX/5Ae2pslxk1fov8BPTaMbfGFNLg4Xk2ccoLNLEiABEiCBqBOwOTt8Eku4E024Y9a5v7c6W0IexwAl1vXM/uoW7bSYdYViMfUVBDvkrnVfuK9jt3zavE7anK2SZc6WY3KPlYqM4bqv11PR6HYEhbgoCXH0iNNze7AOCcSBgMslLpdLxOUUUd9dvQcBT5ogvGmUN5wrmAviMG92SQIpROBQeqc0BXFL0iPOeJvD6Aa08YhxRCRAAiRAAslCoMPZ7icDrCbYub+3hSjWmcQk8Kzzzf6qiXU4DptvLYiKWFdjq5KVNQ/L9vYtvZaqMnO8zC1dKKXp5RFZRqPbERTioiTEBRsjbvjhaTJu2i8jsunYCAmQgJuA026Tjqqd0r5/q7Tv2yrt+7eJo6UxIB5TWoZkDq4UV7FI+9BNujEWpl8ihaPP1l2fFUmABKJHoLFtq3yy/07plFbdnTBGnG5UMatodAM6ZiDYEQmQAAmQAAn4IdCuxDrNk67bq67bu65eeZyFUiDWDbDkB4xZB9EOx2AtJv3HD2psB+XufbdISx9x9JDU4vqKpRER44xuR1CIi5IQh2b1Zk1VQ7CLVDQcI+OP/YWYLfF1FQ3lZuU1JGAEAvYj9Up0a+sS3TqqvhJxOgIOzZpfKplDxkrWkLGSOWSMpJcOE5PZ/UBh1lQjrCjHQAL6CTicNtlR/4LsavwbXFR1X5jJrKm6WcWyotEN6FiyYF8kQAIkQAIkEAqBdmdbV7y6uh4edlqCCYh2qBNKgVg30FLgE7OuSArSiiTfUqi+Q8zTxLrl+xb79YTz7RuecddWLA5lSD2uMbodQSEuikJc9Y5V8rnzWXGa+thH+Kzg9fesukw5etjPJX/wMWFvPjZAAslMwOWwS0fNbuXl5vZ22yr2w3UBp2yypEnGoFFKeIPohu/W3PyA9Zv2vi21jQ/1nbTBLlKSf5UMHDormVFzbiRgeAJN7TtkY81D0mLb5xlroalSGp3b+3wGm10iR2dcLGXDzgl7jkY3+MKeYIwbIM8YA2d3JEACJEACKUkAXnMNnYhP19OrzvvnDld7SGzMYpYB1nzJNufKAdse3W3cOPSusGPGGd2OoBAXRSEOTUOM+9L2rLT7iVWT2SkyLv1iaT3SIDss/xBnlvsNvqlTZGjTTBk782p6x+m+XVkx2Qk4Wg8r0a2t65hpx8Ed4rLbAk7bklsgmRWat9tYySgbISZrEEGjRESJcdV/Esnxk0mlxSwlZVdShEv2jcf5GZqA02WXr+pfkp0Nr4lL3PdpprVYjiq9Uoqyj5bqPavky9Znpd2Pozk84cZlR0aEQ79GN/gMvZB+BkeeibZiHC8JkAAJkEAyEkBsbbdnnZYBtvdxWByR7XB1RGz6ZxZeIPgKpxjdjqAQF2UhTmt+35Y/S3XrWrFbOsXqSJOy7JOkYvw8T++tDXtkw6Y75HB5d/yq7JpsmTzqOhlQflQ4e5DXkkDCEXA5nWKr2+cV222rdDZUBZ6H2SIZpcOV8Ob2eBsr1gFFYjL15Y6qH0v9jv+VwzWrxSntYpZMGVD6bcaE04+PNUkgKgSOdOyWjdUPyhHbbk/7QwacLOOK54rVnN2jz33b/yzVLWvFbuoUqytNynJOkorK7mdwJAZodIMvEnOMZRvkGUva7IsESIAESIAEQicAsU551vlkf23o8rLb27EzqEywsweeIReWzA99QAnwgpRCXIyEOD27yOl0yq5PnpCvMt4SZ6b7CpNNZHjz16Vyxk/EbDbraYZ1SCDhCDjaW6T9wPZu4e3AdnHZAscrMGflSZaX6IYjp+a0jISbNwdMAiQQPAGnyyG7Gl6THfUviUvcMSAzLAUysfQKKcmZGnyDEbqCwlGEQHY1Q56R5cnWSIAESIAESCBeBP526AX5e8NLurunR5xuVMlR0ShGX0vdV/LZl8ukueyIB2xOda5MHnuD5JWMSQ7YnEXKEsAbk876g27RTX1tE1st4joFCq5ukvTSoR5PN3i7pRWURczbLWUXghMngQQk0Gzbp7zgDnd85Rn9oLwTZXzxfEmz5MZ1RkaxIeIKIYKdk2cEYbIpEiABEiABEogjgX0du+WOvTfoHgFjxOlGlRwVjWT0wTvuq/UPy87sd8TV5ehj7hAZ0TpLRk2/kt5xybHlUmIWTlu7tB/c4RHdILw527pFZl8I5oxsyRw8puuY6RjJHFwp+B0LCZBA6hJwuZyyq/F12VH/vDhdnQpEumWgTCxZIKW5MwwBxkg2hCGAhDkI8gwTIC8nARIgARIgAQMRYNbUnovBo6lePIxo9B2p+VI2bP+ttJS2eEaaWz1Apoz/leQUjTLQrcWhkIAIvN3sTbXuTKZdHm8d1btFXH6SHXQBSysc3C26DRkr6cVDxGTiMWzuJxIgATeBFtsBlRG1qX2bB0lZ7tdkQsllkm4ZYBhMRrQhDAMnhIGQZwjQeAkJkAAJkAAJGJRAja1K7t53s7Q4Aztk5Jjz5PqKpVKaXh72LIxuR1CIM7gQh+E5HXbZ/vEfZXfef8SV7h6wuV1kVMepMmLqD+kdF/ZtygZCJeCyd0p71c4ex0wdzQ0BmzOlZUjmoNHdSRUGjxFLdl6o3fM6EiCBJCYAL7g9Tf+QbYeeE6fLnSE5zZynBLjyvOMNN3OjG3yGA9bPgMgz0VaM4yUBEiABEiCBvglAjFtZ87Bsb9/Sq2Jl5niZW7owIiIcGje6HUEhLgGEOG2Ih6s2yoadv5fWku4g9gOq8mXyUTdKdsEw3vckEHUC9uYGad+nxXbbqkQ4cdgD9msdWNIV2w1HTcdJRukwMZktUR8nOyABEkhsAq2d1bKp+mFpaN/smUhJzgyZWHK5ZFjzDTk5oxt8hoTWx6DIM9FWjOMlARIgARIgAX0EEDPus5aPpNXRItmWHJmSM1MqMobru1hnLaPbERTiEkiIw1DhHbf1o3tk78D14kpzD97capJKxxkyYuo8nduS1UigfwIuh106avZ4ebttFXtTXeALLVbJLB8lmUPcohu+W3ML+u+INUiABEigiwCOt+87/JZsrVspDleH+q3VnCPjSy6VQbnfMHSSFqMbfIm2ycgz0VaM4yUBEiABEiAB4xAwuh1BIS7BhDhtuI37P5UNe++R9mL3BxWUgQeLZMqURZI5YJBx7gCOJGEIOFoPe8V226YSLLg6u/eX70QsuQXdohuSK5SPFJO1Sx1OmFlzoCRAAkYh0N5ZJ5tq/iSH2j73DKk4+xiZWHqFZFoLjTLMgOMwusFneIA+AyTPRFsxjpcESIAESIAEjEPA6HYEhbgEFeIwbIfdJl+u+73sK/xMxOqeiKXFJGNM58qwyRcZ5y7gSAxHALGXbHX7pX3flx7xrbP+YOBxmsySUTbC65jpWLEOKDa0d4rhoHNAJEACfgnAC+7AkbXyZd2fxe50h16wmLJkXMkPZEje7IT5f8boBl+ibT/yTLQV43hJgARIgARIwDgEjG5HUIhLYCFOG3r9no9k48E/SHuRO5g1SsHBUjn6mJslM6/EOHcDRxI3As6OVmk/sF3Fd2vbv1U6DmwX/C5QMWflub3dhoyVrIqxklE+SszpmXEbPzsmARJITgLt9nr5ouZRqWv9xDPBwqxJclTplZKVlljPL6MbfIm2g8gz0VaM4yUBEiABEiAB4xAwuh1BIS4JhDhMwdHZLps/ulsOFG0S6YqFb202yVjrhVIx6bvGuSM4kqgTgHdJZ0OVO6nCgW3qu612r4i4AvRtkvSSim5vtyFjJa1wUMJ4oUQdKDsgARKIOAH8P1XV/L5srn1S7M4W1b7ZlCFjiy+RoQO+JSaTOeJ9RrtBoxt80Z5/pNsnz0gTZXskQAIkQAIkkDoEjG5HUIhLEiFOm0bdrvdlU83D0lHY6ZlZ0cHBcvS0RZKeY/wYO6nzX0PkZurs7JCOgzvUEdO2roymzrYjATswZ2RJBmK6DRkjWUPGqn9bMrMjNyC2RAIkQAJ9EOiwN8nm2sekpuUjT638zPEyqWyhZKeVJyw7oxt8iQaWPBNtxTheEiABEiABEjAOAaPbEUELcW1tbbJs2TJ57rnnZM+ePVJYWCinn366LF26VIYMGRIU+Q8++EC19f7770tzc7MMGzZMLrzwQrnxxhslJyenV1uLFy+WJUuWBOzjhhtukDvvvDOoMXhXNvpi6Z2Yw9Yqm9bdKVWlW0W6nAqsh80yPuv7MnjC2XqbYT0DEoAXif3wIXcm0y7RraNmN9LpBhxtWkG5ZFaM7fJ4GyvpxRViMieet4kBl4NDIgESCJJAVfMHsrnmcel0ul8WmE1pMqboIhk28IyE9IJLRhsiyCWNWvVkscmiBogNkwAJkAAJkAAJBCRgdDsiKCGuvb1dZs+eLRDQBg0aJCeeeKLs2rVL1q1bJyUlJer3o0aN0rUdnnnmGZk/f744HA6ZNm2aDB8+XNavX6/EvcmTJ8u7774rAwYM6NGWJsSdcMIJUllZ2aufM888Uwl5oRajL1aw86rZsVa+aHhMbPl2z6XFVUPl6OmLJC1rYLDNsX4cCLjsndJRvUvFddPEN0dzQ8CRmKzpkjF4tDu22xCIb2PEkt3zPorDNNglCZBAihOwOY7Iltonpar53x4SAzMqZVLZjyUnfXBS0Ek2GyLei0Ke8V4B9k8CJEACJEACiUvA6HZEUELcokWL5Pbbb5fjjz9eVq9eLbm5uWplli9fLtddd53MmjVL1q5d2+9q7du3T8aMGSMQ9h5//HG57LLL1DU2m00uvfRS+ctf/iJXXHGF/OlPf/IrxD355JOqXqSL0RcrlPl2tjfLpo9vl5qynSImdwtpTRaZkDdPyseeFkqTvCaKBOzNjW7BrcvjraNqp7gc3ceMfbu2DiyWzMFjPR5vGaXDxGTpSqEbxXGyaRIgARLQS6CmZb1KyGBzNKpLTGKVyqILZXj+WWI2dQU11duYgeslow0RT9zkGU/67JsESIAESIAEEpuA0e0I3UIcRLLS0lJpamoSTGrq1Kk9VmbKlCmyYcMG+fjjj2X69Ol9rtptt90mN998s5x66qlK0PMu9fX1MmLECMER2KqqKikqKvL8WfOIoxAX/E1RtfWfsvnIn6VzYNcRRpdIafVIOWrGTZKW6RZUWWJLwOV0iK1mj9vbrSuxgr2xJvAgLFbJKBupsphqGU2teYz7F9tVY28kQAJ6CXQ6WuTLuqflwJF3PJfkZYyUSaU/lryMoXqbSZh6Rjf4EgZk10DJM9FWjOMlARIgARIgAeMQMLodoVuIW7NmjZx88skyevRo2b59ey/CiBF3yy23yK233ioQzPoqc+bMkVdffVV51yEenG/5xje+oeLGPf300zJv3jzPnynEhbexO9ua5POPb5e6QXs8DaU3WmVi/mVSWnlyeI3z6n4JOFqPuLOY7scXMppuF1dnR8DrLDn5XZ5uSKwwVjLKR4rZmt5vP6xAAiRAAvEmUNfymWyq+ZN0OOrVUExikVGF35WRBeeK2ZScXrtGN/jivSeC7Z88gyXG+iRAAiRAAiRAAhoBo9sRuoW4e++9V6655hoVg+3555/vtcKvv/66nHXWWQKR7eWXX+5zB3z729+WN998Ux588EG56qqretU999xzZdWqVXLttdfK73//+15C3A9+8AOVJAJHWysqKuSMM87o1wtPz5Y0+mLpmYOeOge2vC5bWp8R+wCnu7pTpLx2jBw189diSWf2TD0M+6vjcjnFVre/W3Tbv1U6Dx0IfJnJLBllwyUT2Uy7EitYB5aIydR1nri/Dvl3EiABEjAAAbuzTb6sWyH7D/+fZzS56cNkUtlVMiBjpAFGGL0hpIoNET2CPVsmz1iRZj8kQAIkQAIkkHwEjG5H6BbiIIrdc889SoxDTDjf8tlnn8kxxxyjEi8g6UJf5ZJLLpFnn31WAmU5RbKGzz//XM4//3x58cUXPU31lTUVdZ966ilP3LpQtpLRFyuUOQW6xtZSL5//9zY5NKhbHMqoT5OjShdK8YgTItlVSrTl7GiV9gM7uuO7HdguzvaWgHM3Z+a6j5dq2UwHjRZzemZKsOIkSYAEkpNAfesm2VjzkLTb69QETWKWEQXnyujC85PWC857JVPJhojFDibPWFBmHyRAAiRAAiSQnASMbkfoFuKQPOHRRx+Vm266SRDjzbfguCoSMOBr69atfa4mkjAsXLhQhg0bJtu2bZP09O7jdogxN3PmTHU9POf++c9/etpauXKlVFdXKw84ZFltaGiQd955R375y1/K/v375bzzzpNXXnlF1046ePCg4Mu7bN68WebOnauERAiKqVD2bXpFtnY+L/Zcl3u6DpHBdRNlwrG/FEsahSF/e8DlcklnQ3W36LZ/q9hq94q4uhj6uSi9uEIdL9WEt7TCQfR2S4UbjHMkgRQgYHe2y7ZDf5G9Td3P65y0ISoj6sDM0SlAwD1Foxt8ibYQ5JloK8bxkgAJkAAJkIBxCBjdjoiLENfc3CwTJkwQZE897bTT5He/+50S1v7zn//IggUL5MCBA2K32+X000+XN954o9/VhKB29NFHy6FDh1QbX/va1/q9pi/vulQS4gCq/UitfP7pbdIwqNrDLfNQukwa9FMpHOYWRVO5ODtt0lG1w51QQWU03SaO1sMBkZjSsyRzcKVbeIPX2+BKsWQxIUYq7yHOnQSSlUBD25eyqeYhae2s6pqiSYbnnymVhd8Tizm1Yloa3eBLtD1Inom2YhwvCZAACZAACRiHgNHtCN1CXCSPpmJ5cJQVMeUgxnmXyspKdST1rrvuku9///vqCKuecv311ytBT0+yCLRHj7jeVPdu+Ktsdb0qjpwuzy67yJD6yTL+2F+IJYWSBHQerushunVU7xJxdmWb9bMZ0wrKu0W3irGSXjxUTGaznm3LOiRAAiSQkAQcTptsr/+r7G78u4i4nxnZaeVyVOlVUpA1LiHnFO6gjW7whTu/WF9PnrEmzv5IgARIgARIIHkIGN2O0C3ERTJZg7a8ra2tKvEDIDkcDnUc9KKLLpJly5apjKo4AoujsHrKI488IldeeaXgCC2OvoZSjL5Yocwp2GvaDx+UDRtul8Zyd4wflMy6DDm64mopqEi+47ouh106qnZ6kiq07d8qjiPuLH/+ismaJhmDRivhLatirGQMHiPWnIHBYmZ9EiABEkhYAk3t22Vj9YPS0tkdY3TYwNOlsugisZpTN6QBbYjIbmnyjCxPtkYCJEACJEACqUTA6HaEbiFuzZo1cvLJJ8vo0aMF8eB8y9KlS+WWW27R7ZHW1yZAP+jvvffekxNO0Jc4AB50v/rVrwImk9Cz6Yy+WHrmEKk6uz9dKdvMr4sz2+3pYOoUqWiaLuNmXiNmizVS3cS8HXtLo1t06zpm2lH1lbjsnQHHYR1Q1OXthmOmY1VmU1MCzz/mwNkhCZBA0hBwujplR/1LsrPhNY8XXKa1RCaVLpTC7KOSZp6hToQ2RKjk/F9HnpHlydZIgARIgARIIJUIGN2O0C3E2Ww2KS0tlaamJvnkk09UhlTvMmXKFNmwYYMg2cL06dNDXmO0MXXqVBVDbuPGjbraQfD8448/Xj788ENZsWKFSrgQSjH6YoUyp3CuaW3cKxs23iGHyxs8zWTVZsmUkdfJgPJJ4TQdk2tdTodKoqCJbvB2szfWBO7bbJGM8pEqrlvWkHHqO4Q4FhIgARJIdQKHO3bKxuqHpNm2x4OiYsApMrZ4rljNWamOR82fNkRktwF5RpYnWyMBEiABEiCBVCJgdDtCtxCHRVu0aJE6Mvr1r39dVq9eLTk5OWotly9fLtddd53MmjVL1q5d61nfBx54QPA1Z84cddzUu3z66acyadIksVq7vauQtfScc86RHTt2yL/+9S+ZPXu255La2lp1jHXevHmSl5fn+T0SP/ziF79Qx1HLy8vVtdnZ2SHtMaMvVkiTCvMip9Mpuz59Ur5Kf1OcXSeOTDaRYUe+JmNm/MRQ3nGOtmZpP9Dt7dZ+cIe4bO0BCViyB3qymEJ0yygfJea01AouHub24OUkQAJJTsDpssvOhlflq/pXxIW02iKSYSmUo0qvlOKcKUk+++CmRxsiOF791SbP/gjx7yRAAiRAAiRAAoEIGN2OCEqIa29vl5NOOkl5ng0aNEhOPPFE2b17t/q5pKREPvjgAxk1apSHhZaZdP78+fLUU0/1YIR2vvjiC4EnHa7du3evynhqMpnkwQcfVNlTvcuuXbtk5MiRkpubKzNnzlT9Q5wDYGRLzc/Pl7/97W+6j7L6WzCjL1Y8b7OWQ1/Jhi13ypGy7myhOTU5Mrnyl5JXGvvA3C6XUzoPHZT2/V+qo6Zt+7ZK56H9gRGZTJJeOlyytEymQ8aKNb9U7TcWEiABEiCB3gSOdOyVjTUPypGOnZ4/Ds6bJeOK50maxf0ijqWbAG2IyO4G8owsT7ZGAiRAAiRAAqlEwOh2RFBCHBaura1NebchmynEs8LCQjn99NMFMeIqKip6rG1fQtxjjz0mK1euVGJcY2OjEuMgziH7qe+xVzR65MgR5Y0HsQ8x6urq6sRisShxDv1fc801MmTIkLD2ltEXK6zJReBieMd99d9HZFfWWnFmuBs0dYiMaP2mjJ6+UMxRzBTq7GiT9oPbu+O7HdgmzvaWgLMyZ+ZI5uAx3R5vg0aLOYPHpyKwDdgECZBAkhNwuhyyu/Fvsv3QC+ISu5ptuiVfJpYukNKc0ENPJDk2Hk2N8ALTJoswUDZHAiRAAiRAAilEwOh2RNBCXDKvndEXyyjsj9Rukw1b75KWsmbPkHKr82TKuF9LTnG3R2So40XMP3tjtcfTDcdNbTW7RVzuxBH+SlrREJXFFEdMM4eMk7SiQWIymUMdAq8jARIggZQk0GI7oDKiNnV0J2Uqz/26jC/5oaRbusNCpCScfiZNGyKyu4I8I8uTrZEACZAACZBAKhEwuh1BIc5rNxp9sYx048A7bvvHf5Tdue+LqyusmrldZGTHKTJy6o+C8o5zdtoE2Uvb92/1eLw5WpsCTteUnimZgyu7Pd4GjxFLVq6R8HAsJEACJJBQBHDcf0/TG7Lt0HOC7KgoaeY8mVh6uZTlHpdQc4nXYGlDRJY8eUaWJ1sjARIgARIggVQiYHQ7gkIchbiw7sfD1Ztlw467pbW01dPOgKp8OXriryWncLjftu2HDwkymCrhbd9W6ajeJeJ0BwH3VxDLTcV2Ux5vYyW9ZKiYzJawxs2LSYAESIAE3ARaO6tkY/XD0ti+xYOkNOdYmVDyI8mwDiQmnQSMbvDpnIZhqpGnYZaCAyEBEiABEiCBhCNgdDuCQhyFuLBvKqfDLls/vl/2DlgnrjR3c+Y2k4y2nyYjJs+VjurdXUkV3B5vEOICFZMlTTIGjeqRzdSakx/2GNkACZAACZBATwLwgtt7+C3ZWveMOF0d6o9Wc45MKPmhlOeewGQ2QW6YeBl8Wuze5557Tvbs2dMjdq/e2Ll2u11uu+02+eijjwQZ7JEMq7OzU4YOHSqnnnqq3HDDDTJ8eO+Xa5deeqk8/fTTAUk99NBDsnDhwiBJuqvHi2dIg+VFJEACJEACJEAChiJgdDuCQhyFuIjdMI0HPpXP99wjbcXuD3QoOXtFiv8rYrH578aSV+iO7TbY7fGWUTZCTBZrxMbEhkiABEiABHoTaOuslU01f5L6to2ePxZnT1UJGTKthUQWAoF4GHzIZj979myVyErLZo8s8+vWrfObzT7QtJqbmyUvL09lpp88ebIMHjxYbDabfPrpp0rcGzBggPzrX/+SGTNm9GhCE+JOO+00KS8v79X8/Pnz1fhCKfHgGco4eQ0JkAAJkAAJkIDxCBjdjqAQ57VnjL5YRtveLqdTbLV7u2K7uY+Z2hqrpWmsSMMkEVfX6VFLu0jxepGcKosS2nC81H3MdIykDSg22rQ4HhIgARJIWgJIhrP/8Br5sm6FOFxtap5Wc5aMK54vg/Nm0QsujJWPhw2xaNEilVH++OOPl9WrVyshDWX58uVy3XXXyaxZs2Tt2rX9zgoecR9++KEcd9xxYrV2vwxzOByCPu68806ZPn26fPzxx36FuDVr1qjM95Es8eAZyfGzLRIgARIgARIggfgRMLodQSGOQpzuu8PR3uJOpqAlVTiwXVw29wc532LLE6k9VqTDy7Ei/2CxTD7mZsnMK9PdJyuSAAmQAAlEhkC7vV6+qHlE6lo/9TRYlHW0HFV6pWSm8aVIuJRjbfDBY620tFSamprUMc6pU6f2mMKUKVNkw4YNSjyDiBZqgUgHbzl43zU2NsrAgd1xAzUTClixAAAgAElEQVSPOApxodLldSRAAiRAAiRAAtEgEGu7LNg5UIijEOd3zyB2UGf9QeXlpolvtrp9gfeXySTpJcOUlxs83nDc1JRbIFs+ulsOFG0U0bzjmk0yznq+VEy6INi9yvokQAIkQAIhEIAX3MEj78qWuqfF7mxRLVhMGTK2+AdSMeAUesGFwNTfJbE2+CB+nXzyyTJ69GjZvn17ryEtXbpUbrnlFrn11ltl8eLFIc8SXnEQ31pbW6WlpUWysrI8bVGICxkrLyQBEiABEiABEogigVjbZcFOhUIchThFwGlrl/YD23t4vDnbmwPuJ3NGtkd0U0dNB48W/M5fObTrP7Kx5kHpKOz0/Lnw4CCZPO0mSc+hF0awNy3rkwAJkIBeAh32Rvmi9jGpbek+UliQOUGOKrtKstNK9TbDejoIxNrgu/fee+Waa66RCy+8UJ5//vleI3z99dflrLPOkjlz5sjLL7+sYwa9q0DEXbZsmdx0001K9EOcOO+iCXE//elPBXUh2o0cOVLOPvtsGT9+fEh9ahfFmmdYg+XFJEACJEACJEAChiJgdDuCQlwKCnEwlu1NtV3ebvB42yodNbtFXK6AN09a0WB3bLcubzf8bDKZdd9sjs52+WLdnXKwZItI12XWI2YZn3mRDJ5wju52WJEESIAESEAfgaoj/5bNtU9Kp/OIusBsSpcxRd+XYQNPC+r/b329sVasDb5rr71W7rnnHiXGISacb/nss8/kmGOOkWnTpsn69et1LxAypFZXV8vhw4fV0dYdO3bIhAkTBMIeRDZ/Qpxv4yaTSa666iq57777esSc0z0IZk0NBhXrkgAJkAAJkAAJ+BCItV0W7AJQiEsBIc5pt0lH1U53bDd11HSrOFqaAu4VU1qGZA6u9AhvOG5qyXIHgA631H71tmw69KjYCuyepoqqKuToaYskPTs/3OZ5PQmQAAmkPAGb47Bsrn1Cqps/8LAYmDlGJpVeJTnpg1OeT7QAxNrgu+KKK+TRRx9V3mq33XZbr2nhuOqYMWPU19atW3VPu7KyUolvWkEW1ZUrV8rRRx/dqw0IbZmZmcpbrqKiQqqqquSNN95QCR4aGhrk5z//uRIL+ysHDx4UfHmXzZs3y9y5c5WICDGRhQRIgARIgARIgAT0Eoi1XaZ3XFo9CnFexIy+WHoX136kXoltbV3x3TqqvhJxOgJebs0v7RLdxkjWkLGSXjpMTOauoG56Ow2iXmdHs3zx0TKpLt3h8Y5LazLLhNx5Uj7u9CBaYlUSIAESIAFvAjXNH6mjqDaH+2WL2ZQmowu/JyPyz6QXXJS3SqxtiGgJcRqmuro6JYJB6INnHES/+fPn66K4adMmJZ45nU756quvZOjQoX1ehxh2S5Ys8VuHQpwu5KxEAiRAAiRAAiSQQNoOhbgEWix/d5bLYVfHSr2TKtgP1wW8CU2WNMkYNKpHfDdrbnw80aq3vyVfND0pnQO7REKXSEn1CJk04yZJy8zjfyQkQAIkQAI6CXQ6mmVL3VNy8Mh7nisGZIySSWU/ltz0Cp2tsFo4BGItxEXraKovA2RlhTcchLkvv/yyX1FNux6x61588UV58sknBbHk+ir0iAtn5/FaEiABEiABEiABXwKxtsuCXQEKcTES4jqqd0vz1nXibG8Vc2a25I47VjJKhwe7XuJoPawSKrR1HTPtOLhDXHZbwHYsuQWSWTFWebohvltG2QgxWdOC7jdaF3S2HZaNH98utYN2e7pIa7TIUfmXSWnlKdHqlu2SAAmQQNIQqG35RL6oeUQ6HA1qTiaxyOjC82VEwbliNkXPuzmRAHbs2S3/n70zgW+qTPf/L3vSNE26pTs7pewqixu7ICA4o3ccr3NdR68OjtfR2UcURHEdZ5yZO/NXHMXd0asz6qjILiCorCJL2bfSAt2bpk2zn/w/70mapFDoSXKSnKTP+/nwodB3ed7ve0Iffud5n6d9x1ZwHR2QZ2Qgc+x4aMoi/xl8oT0n2uFLRLGGzv3ed999eOGFF7B06VLceeedgo5+/vz5fKGHp556Cg899JCgMeGdEs0zYgNpABEgAkSACBABIiBZAlL3I0iIi7MQ52quRf3nS+CoPnDOQ6otq4D5mnlQ5xR2+wD7OA6uxpouud3cLbXnf9hlcl5o44sqlPqFN2VWLljSZKm3Mwc+x4GOt+HO4vymckBB/SAMH/8QlGq91M0n+4gAESACCSfg4TpwsPEtnLKuC65tUPflo+AMGnFFpoRvTqQFXXW1qF+6BI5D3fwMLq+A+a55UBd0/zM4UhMS7fCtW7eOz802cOBAsHxwZ7fFixdj4cKFePTRR8GufsbS2ByPP/44nnnmGbBiDkIaK9awZMkSvmDDz372MyFDuvRJNM+IDaQBRIAIEAEiQASIgGQJSN2PICEu7NER+7BczWdQ8+ZCcHZ/xbrumlxnQOlti3kxzuuwwXH6SEh4O30EPpf9gmM7q5jy0W5FAyBXaST7YejJMFeHBXt2LEZT0algV3WLEsPzfoL8/hN7Gk7fJwJEgAj0GgJNHXtQWf8SHB5/KgIZ5OiffR0G5PwH5DJlr+FwoY26as+g5omF4Nov8DM404DSBYtFEePE9iF6OkSXywWz2Qx2dXTnzp18hdTwNnr0aD632/bt2zFmzJieprvg96dMmYINGzbggw8+wA033NDjXE6nky8SUV1djY0bN2LChAk9jjm7Q6J5RmwgDSACRIAIEAEiQAQkS0DqfgQJcWGPjtiHVfP2om4j4c5+WhV6IxQ6A1yNTIDynedhlkGdX+aPdCsezP+uyi5MiWi3SD+dp/b9Gwed/wePIRQdV9RQgWHjfweFShvpdNSfCBABIpA2BDycA4ca30GNdXVwT3p1KV8R1agdmDb7FGMjNU8t6jYS7uy5teUVKJ0fW8QYm1NsH0IIA1ad9Mknn8QVV1yBVatWQa/3R5A///zz+OUvf4nJkydj/fr1wan+9re/gf26/vrr+WujnW3ZsmXIzs7m5wlvHR0d/PzsemlhYSEOHz6MzEx/FfUDBw5g27ZtuPHGG6HRhF4CNjQ0gBWS+Pjjj8HEQCYSRhOZnwyeQphTHyJABIgAESACRED6BKTuR5AQF/YMiXlYLCdc9avCrm909xjLNTpoiv1VTJnopikaBIU2Q/pPvEgWOtsbsWfnE2guCl3F1TSr+StXuX0vE2kVmoYIEAEikDoEmu37UVn3Iuye+oDRMvQzzcXAnB9CIVenzkYSYCnLCVe9UPjP4LLFz8acM05MH0IoIofDARattmXLFhQVFWHixImoqqri/5yfn4/NmzdjwIABwek6q5Oy6qevv/76OX9fUlLCR9YZjUbU1tbiu+++Q3NzM//nTz/9lJ+/szGBb+rUqbyAN3bsWH6906dP85VW29raUFpairVr16K8vFzodrr0SwbPqAylQUSACBABIkAEiIDkCEjdjyAhLk5CXNPGD9Cy6V+CH0i5LhP6QWOCud3UeSWQyeSCx6drx+o9H+AQ9yG8+kCkoBcobhqJoeN/DYWS/uOZrudO+yICRCBEwMu5cLjpXZxsXRGMms5QFWKE+acw6aITOdKdb9NHH6Dl38J/BudcdwPYr1hashw+u93OR7f94x//4K+C5uTkYNasWWA54pgYFt7OJ8SxK6xvvPEGf42UCXlMfNPpdBg0aBA/1/33388LfeGNiW7PPfccL/adOHECTU1NfGQcE96uvfZaPPDAA7xIF21LFs9o7aVxRIAIEAEiQASIgHQISN2PICEuTkJcw+o30Lp9ueAn0Th2NvJn3C64f2/q6LDWYveuJ2EpaghuW9uowcjSB5BdeklvQkF7JQJEoJcRsDgOY2/dC+hwnwnuvI9xNgbn3gSFPHVzgsb7GBveeR2tq5lwKawZZ8xG/s2x/QyWusMnjIR0ehFP6ZwFWUIEiAARIAJEINUISN2PICEuTkJcpBFxORNuQM7E2N7Gp9qHI1J7q3a9gyOyz+DN8EfHyTxAScvFqBj/S8gVlJw8Up7UnwgQAekS4HxuHGn6ACcsnwaj4HRKM4YXzEOObph0DU+yZT6PB22bv0Lj/70Drs0q2JpUjogTvMkU6yh1BzrFcJK5RIAIEAEiQAR6FQGp+xEkxMVJiIs0R1zZXc9CY+7bqz4c0WzWbjmFXXufhLWwOThc16DFyL6/gKl4VDRT0hgiQASIgKQIWB3HsKf+BdhcNUG7SrOmozzvFijlVLCmu8Pytrejdf0atK5ZCa+lJeLzTNUccRFvNIUGSN2BTiGUZCoRIAJEgAgQgV5HQOp+BAlxcRLi2LRCq6ZqyypQekvsFdt6y6eL4zhUffcmjqpWgNP5dy1zA2XW8Sgf+zOKjustDwLtkwikGQHO58Gx5o9wvOUj+OCvGq1V5mK4+SfIzaAXDd0dt7u+DpZVy2H9ch18Lmewi0ythlyXAW+rpcenJJWrpva4uRTuIHUHOoXRkulEgAgQASJABNKegNT9CBLi4ijEuZprUfPmAnD2tvM+6HKdAaW3LYY6pzDtPwxib9DWXIXd+55CW2FrcOqM+gyMGvhrZBUMFXs5mo8IEAEiEDcCbc4q7K17EW2uE8E1SgxTUJ53G1SK3lMxWyhg+5FDsKz4DLYd2wBfoJgPAEWWEcbpM2GcNgNemw01ixeAa7/Az+BMA0oXLIa6IPafwVJ3+ISylUo/4imVkyA7iAARIAJEgAikHgGp+xEkxMVRiGNTMzGu/vMlcFQfOOfpZZFw5mvmkQgXw+eaRccd//ZlHNeuAxe4sSVzAX3br8SgsfdBLqfKszHgpaFEgAjEmQDn8+JEyyc42vxP+ODlV9MosjHMfDfy9VSMJhy/j+Ng+3Y7LCs+hePI4S4noy4uhWnWHGRediXk6lBFbVddLeqXLoHjUDc/g8srYL5rnigiHDNG6g5fnB9l0acnnqIjpQmJABEgAkSACPQaAlL3I0iIi7MQ1zm9s74KtoPb4HXYoNDqoR8yjnLCifjPQHvjEew++CzaC0KRD/q6TIwq/y0M+YNFXImmIgJEgAiIQ6DddYqviGp1Hg1OWJQ5ARX5d0ClyBRnkTSYhXM6YN24Hq2rloNdRQ1vumEjeAEuY+RFkMlk592ts7qKj55jUXIKvR76MeOgKRM3L6vUHb5UexSIZ6qdGNlLBIgAESACREA6BKTuR5AQlyAhTjqPZPpawqLjju54ESf0G+ELBETInUA/+xQMuOQeio5L36OnnRGBlCLg83GosizDkeb3waqjsqZSZGFY/n+jIHN8Su0lnsZ6LC188YXWdavB2WyhpRQKGMZfDtOsudD07RdPEyKaW+oOX0SbkUBn4imBQyATiAARIAJEgAikKAGp+xEkxJEQl6IfrfObba3bjz1H/wCbOfQfN0NtFkYNmw99jnT+05Z24GlDRIAI9EjA5jqDyvolsDgOBvsW6C/FUPNdUCuyehzfGzo4a6r5/G9tm78CPJ7gllnxhaypV8E0fRaUObmSQyF1h09ywHowiHim2omRvUSACBABIkAEpENA6n4ECXEkxEnn0yKiJZzXg8Pb/4qThi2h6DgHMNA1E30vup2i40RkTVMRASLQMwEWBVfdugqHmv4BzufiB6jkmajIvxOFmZdf8Fplz7Onfg+fzwd75R5YVixDx95dXTakzM2D6eprkDVpKuS6QKlsCW5Z6g6fBJFd0CTimWonRvYSASJABIgAEZAOAan7ESTEkRAnnU9LHCyxnNmDPSeehz3fHpw9qzYbo0bMR4apLA4r0pREgAgQga4E7O567K1/CS32yuA38jPG8AUZNEpTr8bl83jQtuVrPgLOVX2yCwtN/4EwzZ6LzDHjIVMoJM9J6g6f5AGeZSDxTLUTI3uJABEgAkSACEiHgNT9CBLiSIiTzqclTpaw6LiDW59HTfa38Cn9iyg6ZBjEzUHfi26J06o0LREgAr2dAIvyOmVdi4ONb8Prc/A4lPIMDMm7HcWGSb06Cs5ra4d13VpY1qyA19ISelRkMugvGsMXYNCWV6QUI6k7fKn2eSSeqXZiZC8RIAJEgAgQAekQkLofQUIcCXHS+bTE2ZKWmm+xp+YvcOQ5gyuZavMwatQj0GYVxnl1mp4IEIHeRMDhaUJl/d/R1BG6ZpmbMRrDzfdAq5RefrNEnY27oR6WVZ/D+uU6+Jyhf4tlKhUME6fAdPVsqAuLE2WOqOtI3eETdbMJmIx4JgAyLUEEiAARIAJEIE0JSN2PICGOhLg0/eh1vy2vx4UDW5/Dqdw9QOCmk8ImQ7n8epSNvLFXsaDNEgEiID4BFgV3um0DDja+CQ/XwS+gkGkxJO9WlGRNS6kILzHpOI4eRsvyz2DbsRXw+YJTK7KMMF51NYzTZkBhSO1iFVJ3+MQ8z0TMRTwTQZnWIAJEgAgQASKQngSk7keQEEdCXHp+8nrYVVPVFuyt/X9w5vqTprOWfaYAoy5eAE1mXq9kQpsmAkQgNgJOTwv21b+Cho4doX9XdMMxwvwT6FTm2CZPwdE+joNt53a+AIPjcKhKLNuKqrgE2TPnIPPyCZCr1Sm4u3NNlrrDl2qQiWeqnRjZSwSIABEgAkRAOgSk7keQEEdCnHQ+LQm2xOt2YN/WZ3Emb38wOk7ZLscQ9Q9RMuz6BFtDyxEBIpCqBFgUXG371zjQ8BrcXDu/DblMg/Lc/0KZcQZkMnmqbi0quzmnE22bNvBXUN11tV3m0A0dDtOsucgYORoyeXpxkbrDF9VhJnEQ8UwifFqaCBABIkAEiECKE5C6H0FCHAlxKf4Ri938huObsK/xJTiz3cHJcs+UYOQlD0Otz4l9AZqBCBCBtCXg8lr5KLh629bgHk3aIRhhvhcZ6t6Ve9JjsaB17Uq0frEanM0vSPJNoUDm+MuRPWsONH37p+2zIHWHL9XAE89UOzGylwgQASJABIiAdAhI3Y8gIY6EOOl8WpJoidfVgcptT6M2/zAQCNJQWuWoyLgZxRVzkmgZLU0EiIBUCdS1b8W+hlfg9lp5E+UyFQbl3IS+ptm9KgrOeaqav37a9s0mwOMJHpdcp0PWlOkwTp8JVW76X/mXusMn1c/R+ewinql2YmQvESACRIAIEAHpEJC6H0FCHAlx0vm0SMCS+iNfYJ/lVbhMof9M5p/pgxFjH4FKl9qJxCWAl0wgAmlBwO1tx/6G11Db/lVwP1magRhZ8FPo1SVpsceeNsGu49r374Vl+Wfo2BOqDMvGKXPz+OqnWZOmQq7L6GmqtPm+1B2+VANNPFPtxMheIkAEiAARIALSISB1P4KEOBLipPNpkYglbkc7Krc/ifqC44DMb5SqVYFhWXegYPAMiVhJZhABIpAMAg22b7Gv/mU4vS388jIoMDDnh+iXfS3kskAp5mQYlqA1fR4P2rZ8zUfAuaqruqyq6T8ApplzkTnuUsgU6c/ibORSd/gS9IiItgzxFA0lTUQEiAARIAJEoNcRkLofQUIcCXG97kMpdMO1B1dgf/tbcBu9/iE+wFw3AMPHzYdKkyl0GupHBIhAGhBweztwsPFNnG5bH9yNQdMPI8w/hUHTJw12eOEteG02WDeshWX1Cnhbmrt01l88hi/AoC2vgEwWeHuR9kTO3aDUHb5UOxLimWonRvYSASJABIgAEZAOAan7ESTEkRAnnU+LBC1x21uxZ8cTaCysDlqnblFieM7dyB84WYIWk0lEgAiITaCxYxcq61+C0+MXoFgUXP/s6zAg53rIZUqxl5PUfO6GelhWLYd14zr4HI6gbTKVCoYrJ8M08xqoi4olZXOyjJG6w5csLtGuSzyjJUfjiAARIAJEgAgQAan7ESTEkRBHn1IBBE7v/wQHHO/BY+D8vTmgsGEIho9/CAqVVsAM1IUIEIFUI+Dh7DjU+A5qrGuCpmeqy/gouCxt+lb/ZJt1HDsCy4rP0L5tC+DzBfevMGTxxReMU2dAkUV5M8Ofaak7fKn2+SOeqXZiZC8RIAJEgAgQAekQkLofQUIcCXHS+bRI3BKXrQm7v30CzUVngpZqmlUYYb4Xuf2ukLj1ZB4RIAKREGjuqMTe+iVweBoCw2Ton/09DMy5ga+Omo7Nx3Gw7dwBy8rP4Dh0sMsWVUXFMM2cA8MVEyFXq9Nx+zHvSeoOX8wbTPAExDPBwGk5IkAEiAARIAJpREDqfgQJcSTEpdHHLTFbqdn7Txz0/AvezECUiBcobhqOoeN+TdFxiTkCWoUIxI2Al3PicNO7ONm6IrhGhqoYIwruhUk7OG7rJnNizulE21cbYFn5Odx1tV1M0VUMg2nWHGSMuhgyuTyZZkp+bak7fJIHeJaBxDPVTozsJQJEgAgQASIgHQJS9yNIiCMhTjqflhSyxNFWhz3fPYmWovqg1domNUYU34+csnEptBMylQgQgU4CFvtB7K1/ER3uTjFKhr6mazAo5z+hkKdfFJin1YLWtavQ+sVqcO1toQdBLkfm+Mt5AU7bbwA9IAIJSN3hE7gNyXQjnpI5CjKECBABItBrCfh8PrS1tcFqtcLpdIL9mVpiCbBCYEqlEnq9HgaDARqNRpABUvcjSIgjIU7Qg0yduidwcvd7OOz7N7z6wD/KHqC05SJUjP8V5Ir0TuJOzwQRSBcCXs6FI83vo8qyzF8eGYBOVYAR5nnI1g1Nl20G9+E6VQPLymVo+3oTfB538O9lWh2MU6bBOGM2VLl5abfveG9I6g5fvPcv9vzEU2yiNB8RIAJEgAhEQoCJbrW1tbBYLPwwhUIBOd0OiAShKH3ZOXg8Hn4uJsoVFhbCZDL1OLfU/QgS4kiI6/Ehpg4XJmBvPYXdu59Ca1FTsKOuUYuRfR6EqfgiwkcEiICECbQ6jmJv3QuwuU8FrSwzXo3Buf8FpTx9CrEwJ8a+vxKWFcvQsXtnlxNR5uTy4lvW5GlQZGRI+LSkbZrUHT5p0zvXOuKZaidG9hIBIkAE0osAi4I7deoUtFotiouLBUdipRcFaezG6/Wivb0ddXV1YF/369cPOp3ugsZJ3Y8gIY6EOGl8utLAihM738QR5XJwOn9EjcwNlLWORfm4Byk6Lg3Ol7aQXgQ4nwdHm/+FEy3/ho+VQQagVeZhuPknyM0YmTab9Xk8aN+6GS0rPoPr5Iku+9L07Q/T7LnIHHspZEqK4I310KXu8MW6v0SPJ56JJk7rEQEiQASIQDiBmpoa/lrqgAEDSISTyKNhs9lw8uRJGI1GXhy9UJO6H0FCHAlxEvlYpYcZHS0nsbvyKVgL/SHMrGU06DCq/6+QVTg8PTZJuyACKU7A6jzBR8G1u04Gd1KSNQ1D8m6BUp4eEWHejg5Y169F65rl8DQ3dzmxjNGX8PnfWCEGFuJPTRwCUnf4xNll4mYhnoljTSsRASJABIjAuQSOHj3KR1+Vl5cTHokQYDc8Dh8+zOeMYwIpCXESOZhYzSCnL1aCNJ4R4DgOJ3a+imOaNeACN9tkLqBv++UYNPZ/IJcrCBQRIAJJIMCi4I63/BvHmj+ED17eAo0iG8PM9yBff3ESLBJ/SXdjAyyrl8O64Qv4HI7gAjKlCoYrJ8I0cw7UxSXiL0wzgnwIcR8C4ikuT5qNCBABIkAEIiNw5MgRfsCgQYMiG0i940pA6LlI3Y+giLiwx0TqhxXXJ5omF52ArfEYdh18Gu0FoWqE+no9Rg3+LQz59GZFdOA0IRG4AIF2ZzVfEdXqPBbsVWSYhIq826BSZKY8O8exo7Cs+Azt27ewtwHB/cgNBhinXQ3jVVdDmWVM+X1KeQPkQ4h7OsRTXJ40GxEgAkSACERGQKjgE9ms1DtWAkLPRep+BAlxJMTF+lmg8RcgwKLjju1YguMZX8IXqLQscwL97ZMx4JKfUOUdenqIQJwJ+HwcTlg+w5Gm9+GDv+KSWmHEsPy7Yc4cG+fV4zu9j+PQsetbtKxYBsfB/V0WUxUW8ddPDVdMglytjq8hNDtPQOoOX6odE/FMtRMje4kAESAC6UVAqOCTXruW/m6EnovU/QgS4kiIk/6nLQ0sbGs4hN2Hfw+buT24m8y6LIyu+B30uRe+354G26ctEIGkELC5TmNv3YtodR4Orl+YeTkq8n8MtSIrKTaJsSjncqFt0wZYVn0Od+2ZLlNqhwxF9qy5yBh9MWRyuRjL0RwCCUjd4RO4Dcl0I56SOQoyhAgQASLQKwkIFXx6JZwkblrouUjdjyAhjoS4JH6MetfSLDruyPa/oirzG/gCASpyBzDAOQP9Lv4xRcf1rseBdhtHAiwK7mTrChxuehecz82vpJIbMNR8FwozL4vjyvGd2mNtRevaVWj9YhW4ttCVd8jlyBx3GR8Bp+0/ML5G0OznJSB1hy/Vjo54ptqJkb1EgAgQgfQiIFTwSa9d97yb559/Hps2bcKePXtQX18Ph8OBwsJCTJ48Gb/+9a8xcuTInieJoYfQc5G6H0FCHAlxMXwMaGg0BKy1ldh9/I/oyO8IDs+qNWHU8PnIyO4TzZQ0hggQgQCBDncdKuuWoMURuqpp1o/D0Py7oFGaUpKT6/QpWFYuQ9tXG+Hz+IVF1mRaHYyTp8E4YxZUefkpubd0MlrqDl+qsSaeqXZiZC8RIAJEIL0ICBV80mvXPe8mLy8PNpsNo0aNQkmJvwBYZWUlDh06BJVKhQ8//BBz587teaIoewg9F6n7ESTEkRAX5UeAhsVCgPN6cGjbn1Ft3A6fyj+T3C7DIM9s9Lv4tlimprFEoFcSYOXMa6yrcajxHXh9Tp6BUq5HRf4dKMqcAJlMllJc2H7sB/bxBRg6du3sYrsyJwfGGbORNfkqKDIyUmpf6Wys1B2+VGNPPFPtxMheIkAEiEB6ERAq+KTXrnvezVdffYUxY8ZAq9V26fzCCy/gvvvuQ0FBAWpqaqBUKnueLIoeQs9F6n4ECXEkxEXx+NMQsQhYTn2HPdV/hj3PEZzSeCYXo0bNh87of8NAjQgQgQsTsLsbUbm8VSAAACAASURBVFn/Eprte4Id8zIuwjDzPdAqc1IKn8/jQfu2zbCsWAZn1fEutmv69oNp1lz+GqosTs5NSsGSmLFSd/gkhqtHc4hnj4ioAxEgAkSACMSRgFDBRywTnCer0L5jK7iODsgzMpA5djw0ZX3Fmj4h8wwaNAhHjx7Frl27+Ii5eDSh5yJ1P4KEuLCnQ+qHFY8HmeZMPgEWHXdg63Ooyd4FBF4cKGwyDJZ9H31G3ZR8A8kCIiBRAixq7HTbehxsfBMezs5bqZDpMCT/VpQYpqZUFJy3owPWDWvRunoFPM1NXYizwgtMgNNVDEupPUn0sYmbWeRDiIuWeIrLk2YjAkSACBCByAgIFXwim/Xc3q66WtQvXQLHoQPnfFNbXgHzXfOgLiiMdZmEjB86dCgOHDiA/fv3o6KiIi5rCj0XqfsRJMSREBeXDwhNGjmB5pPbsPfMX+HIdQUHZ58xY+RFD0NrKIh8QhpBBNKYgMPTjH31L6OxI3RtM0c3EsPNP4FOlZcyO3c3NaJ11XK0bvgCPodfTGRNplTBcMVEvgCDupiiY1PhQKXu8KUCw3AbiWeqnRjZSwSIABFILwJCBZ9Ydu2qPYOaJxaCaw8rwnXWhPJMA0oXLJa8GPfWW2/h9ttvB4uKY0KcQqGIBc15xwo9F6n7ESTEkRAXlw8ITRodAa/bgf3bnsPp3Eog8G+Xol2GIcofonTEf0Q3KY0iAmlEgEXB1bZ/hf0Nr8HD2fidKWQalOfdjNKsGSkTMeY4cYzP/9a+dTPAccETYs6W8aqrYZw2A0pjahaXSKPHLaKtSN3hi2gzEuhMPCVwCGQCESACRKAXExAq+MSCqOapRd1Gwp09J4uMK52/KJalRB/73HPP8UUaWOEGJryxr4uLi/HJJ5/wOeTi1YSei9T9CBLiSIiL12eE5o2BQOOJr1BZvwTOnFCFxJwzRRh1ySNQ63NjmJmGEoHUJeD0WLC/YSnqbduCm8jWDsXwgnnIUEk/atTHcXzhhZYVn8FxMFTVlW1GVVAI08w5MFw5CXKNJnUPqRdbLnWHL9WOhnim2omRvUSACBCB9CIgVPCJdtcsJ1z1wt8KHl62+FlJ5YybPn061q5dG7S/b9++ePPNNzFp0iTBe4qmo9BzkbofQUIcCXHRPP80JgEEWHRc5danUZt/EJD7F1S2yVGh/RGKh16bAAtoCSIgHQK17Zuxv34p3Jw/dF8uU2Fw7o/QxzgLMlngAyIdc7tYwrlcaPv6S1hWfA537eku32NvOFn+N/1Fl0Aml/Y+JIpXMmZJ3eGTDCiBhhBPgaCoGxEgAkSACMSFQE+CT+2Sv8J9pqtfF4khHksLvK0WwUMURhOUpmzB/c/uqCoqRuG8+6Mef76BFosFe/bsweOPP441a9bgiSeewMMPPyz6Op0T9nQunf2k7keQEBf2iEj9sOL2NNPEkibQcHQDKptfhivbE7Qzr7YMI8c8ApXOKGnbyTgiECsBl7cNBxpeRW37N8GpjJrBGFFwL/Tq4linj+t4j7UVrWtXwfrFanjbrKG1ZDK+8ikT4LQDBsbVBpo8cQTIhxCXNfEUlyfNRgSIABEgApER6EnwqX70oXMq3Ee2QmJ7a/r2R9ljT8dtUbfbjcsvvxzs5/eWLVswbty4uKzV07mQEBcX7PGdlJy++PKl2aMn4Ha2o3LbU6gvOAbI/POoWhUYargNheUzo5+YRhIBCROot23nCzK4vK28lTIoMSj3h+hnulbSUXCuM6dhWbkMbZu+hM8Tul4u02qRNWkqTDNmQ5VvljB5Mi0aAuRDREPt/GOIp7g8aTYiQASIABGIjEBPgg9FxJ3Lk+WN+81vfoMFCxbwEXLxaD2dCwlx8aAe5znJ6YszYJo+ZgK1h1djv/V1uI1e/1w+wFzXH8PHPgyVNjPm+WkCIiAFAm6vDQca38CZti+D5hg0/THC/FMYNGVSMPEcG1gRCZb3rWX5Z+jY9W2X7yuyc3jxLWvyNCj0eknaT0bFToB8iNgZhs9APMXlSbMRASJABIhAZASECj6RzRrqneo54rrb92uvvYY777wT8+bNw4svvhgtmguOE3ouUvcjIr6aarfb8fTTT+O9997DyZMnkZOTg1mzZmHx4sUoKSmJCPbmzZv5ub766iu0t7ejT58++OEPf4j58+dDf4H/rLz++ut44YUXsG/fPqjValx22WV45JFHcMUVV0S0/tmdpX5YMW2OBqcNAbe9FXu3P4mGopPBPaktSgwz3QnzoGlps0/aSO8k0Gj7DpX1f4fT28wDkEGBATn/gf7Z34dcppQcFJ/Hg/btW2BZsQzOE8e62Kfu0w/Zs+Ygc/zlkCmlZ7vkYKa4QeRDiHuA8eTZ4KzC0fatcHEdUMszMChzPPI0fcXdAM1GBIgAESACKU1AqOATyyZTuWpqd/u+44478MYbb4BFxv3qV7+KBc15xwo9l3j6EWJsLCIhzuFwYOrUqWACWlFRESZOnIgTJ05g69atyM/P5/9+wIABgux65513cPvtt8Pr9eKSSy4Bq7KxY8cOXtwbNWoUNm7ciKysrHPmevDBB/GXv/wFOp0OV199NZhNrFoHi0b45z//ieuuu07Q+t11kvphRb0xGpiWBE4fWIYDHe/Ak8X598cBBfWDMWL8Q1CoM9Jyz7Sp9CXg4ew42PgWTlm/CG4yU92HzwWXpekvuY1z9g60bliH1tXL4Wlq7GJfxqiLYJo1B7qhIyCTBe6SS24HZJDYBMiHEJdoPHhaXLVYU78Epx0HzjG2WFuB6eZ5MKkLxd0IzUYEiAARIAIpSUCo4BPL5lx1tahZvABcu78YWXdNnmlA6YLFUBck/+cTC6Bqa2vjdRh5WJExlh9uyZIlYFqNRqPBwYMHUVYWn1ssQs8lHn5ELGd99tiIhDgWdfbkk0/ySfhWrVqFzEz/Vbjnn38ev/zlLzF58mSsX7++R/tqamowePBgXkRbunQpH77ImsvlAlNR3333Xdxzzz146aWXuszFqnDMmDEDubm5+Oabb/g5WGNfT5kyBRkZGTh+/DhMJlOPNnTXQeqHFdWmaFBaE3DZmrHn2yfQVBSq2KNpUWFY3k+Q339CWu+dNpc+BJo69qKyfgkcHr+gJYMc/bK/j4E5P5BcFJy7qZEX36wbvgBnt4cOQalE1hUTYZx5DTQl8XE80ufE03Mn5EOIe65i87S4zuD9moVwBCovd2etVm7AjaWLSYwT9yhpNiJABIhAShIQKvjEujkmxtUvXQLHoXNfEmnLK2C+a54kRDi2T3Yz8cc//jHy8vIwZswYXpdpbGzkq6aeOXMGWq2Wj4i78cYbY8Vy3vFCz0VsP0LsDQkW4phIZjab0draylfCuPjii7vYMnr0aOzevRvbt2/nD+VCjZW0ZQn8mKjGBL3w1tzcjH79+oFdga2treUPt7Ndc801WL58Of70pz/xamt4e+CBB/C///u/+MMf/sCLgtE0qR9WNHuiMb2DQE3lRzjkfh+eTJ9/w16gqHEoho3/LRQqbe+AQLtMOQIezoHDTf9AdWvo54BeVYIRBT+FUSutaqKOE8dhWfEZ2rdtBryBHI0A5PpMGKfNgPGqmVBG+RIo5Q6ODO6WAPkQ4j4YYvP8Z82ibiPhzraaRcbdULpI3M3QbESACBABIpByBIQKPmJtzFldBduObfDabHxOYf2YcdCUSSttAgt6euWVV7BhwwYcO3aMF+FYqjCm30ybNg0/+9nPMGjQILGQdDuP0HMR248Qe1OChbh169bxcAcOHIjOzYcbw3LELVy4EI8++igWLbqwA3P99dfj448/5qPrWD64s9uECRP4vHFMTb3tttv4bzNhLjs7G06nE9XV1SgtLe0yjF1lnTRpkuCovO5ASv2wxD58mi+9CDjaGrDnuyfQUlQX3JimSY0Rhfcht++l6bVZ2k3KE2ixH8De+hdhd3c+rzL0Nc3BoJwboZCrJbE/H8ehY/dOPv+b/cC+LjapCgphuvoaGCZMglxDYrckDizJRpAPIe4BiMmT5YR7t/q3gg38r7JnKWecYFrUkQgQASKQngSECj7puXvp7krouYjpR8SDhmAh7s9//jN+/vOf88UU3n///XNsWbZsGebOnQsmsn344YcXtJXdKV69ejVfcOHee+89p+/3v/99fPLJJ/jFL36BP/7xj/z3v/vuOz4Kj+Wiq6+vP2eMzWbjr8oysY5F1UXTpH5Y0eyJxvQ+AtW7/w+HfB/Dqw9Fx5U0jULF+F9BoZSGwNH7ToV23EnAy7lwpPn/UGX53F/2F0CGqhDDzfciWzdEEqA4lwttX2+EZeUyuM+Ern0z47TlQ2CaORf6i8dAFpYbQxKGkxFJJUA+hLj4xeS5uekDbG35l2AD+2dcguHGachQmKBXmKBTGqGUqQSPp45EgAgQASKQ+gSECj6pv9PU2oHQcxHTj4gHIcFCHBPF2JVQJsaxnHBnt127duGiiy7iCy+wogsXajfffDP+8Y9/4Le//S2eeeaZc7qyYg3snvEPfvADvgADa0yYYwIdE+MY1O4aE+EsFgusVisMBkPEvKR+WBFviAb0WgIO6xns3v0kLIWhJPLaRg1Glj6A7NJLei0X2nhyCVgch1FZ9yJs7pC41cc4C4Nyb4JSnvyoMq/VitZ1q9G6ZiW8bdYQLJkMmWMv5QswaAf6c5NSIwJnEyAfQtxnQkyeXza8ge9al8dkoEau9wtzSr84l6E08X/OUBj5v+O/Vpqgk2dCJpPHtBYNJgJEgAgQgeQTECr4JN/S3mWB0HMR04+IB2HBQhwrnvDyyy/j4YcfBsvxdnZjQFjxBPbr0KFDF7SVFWGYN28e+vTpg8OHD/P3ijsbyzE3btw4/o8scm7lypX810y4YwLelVdeiU2bNnU7P7uueurUKf5XcXHxBW1gyQTZr/C2f/9+3HLLLbyQyARFakQg1QlUffc2DsuXgcvwRx7J3EBp6xgMGfdzyBXKVN8e2Z8iBDifG0eb/4njLZ8Eo+C0ynyMMM9DTsbwpO/CdeY0LCs/R9tXG+Bzu4P2yDQaZE2aBtPVs6HKNyfdTjJA2gSS5fCx1B1PP/003nvvPb7yfE5ODmbNmgWWMqSkpEQQNI/Hw/t227ZtA/OFGhoawCqgsYpnLJ8ve3HKqtt317xeL5+j99VXX+VTl7DbCVOnTsVjjz2GoUOHClq/u05i8ow0Ii5qowPFZpg4x0Q5XrAL/5oX7IxBIU8l01Jl5Vhg01giQASIQBwJCBV84mgCTd0NAaHnIqYfEY+DSIoQ197ezjtnrHrqzJkz+QILzMFj1U/vvvtunD59GswpZI4kK87AmthCHMtjx5zE7hoJcfF41GjOZBHosFRj996nYC1sCZqga9BhZL9fwFQ0Mllm0bq9hIDVeRx7615Eu+tkcMelWVehPO8WKOW6pFHw+Xx8dSpWgMH23beAL3CVG4DClA3TjFnImnIVFHp/dXBqRKAnAslw+Fj1eSZ6bd68GUVFRZg4cSJOnDiBrVu38qk82N8PGDCgJ9PB/DJ2k4CJaOxWAnuZyYp0sbQgTNzLysrC2rVrMXbs2C5zcRyHG264AR999BFfsf6qq67iEzd/+eWX0Ol0YPmFx48f3+P63XUQk2ekOeKuNt8HlVyHDq/F/8tjgc3byv/O/mzzWuD1hUT7qDYIQCnTnBNRp2fCXSC6LhR5Z4RCRi/PouVM44gAESAC0RAQKvhEMzeNiZ6A0HMR04+I3trzjxQsxIl5NZWZw66yspxyTIwLb6zKBruS+uyzz+JHP/oRL8CxJvbVVIqIi8fjRHNKlQD7z9KJ717DMfVqcIEbgDIX0KftUgweez9Fx0n14FLYLs7nwfGWj3Gs+SP4WBlfABpFDoabf4I8/eik7czn9aJ9+xZegHMeP9bFDnVZH5hmzYXh0isgU9J/epN2SCm6cDIcvkceeYQvfHX55ZfzVeiZkMYaSyHCKshPnjwZ69ev75Eoe/m5ZcsWXHrppVCGPfss2o2twdKIjBkzBuzWQnhjldPYC1R2G4IVzSooKOC//a9//YsX6JhPxyLswufs0ZhAB7F5ilk1lQn5Ls4Om7clIM618uJcSKgLiXYdXnbNPST0C93/2f20cgMylIFoOl6oC0XWdeayY3+npaux0SKmcUSACBCBLgSECj6ELbEEhJ6L2H6E2LsULMSJWayhcxMdHR184QcGiTl77DroTTfdxF+xYI4luybBrsKyRsUaxD56mq83ErA1HcPuA8+grSCU/0pfr8fIgb9CVkH0V4h6I0va8/kJtDmrsbf+BbQ5jwc7FRsmY0jebVAp9ElBx9ntsH75BSyrlsPTFMqdyIzJGDmaF+B0w0bQNbGknE56LJpoh49FrJnNZrS2tvJ+FMuhG95Gjx6N3bt38+IZE9GibUykY9FyLPqO5eE1Go3BqYYNG8YLbSwi7rrrruuyRGfhLZbrl71gjbSJzdPiqsX7NQvg4NrOawoTu24sXQyTujBSc8/bn/N5Yfe2+SPpAhF1HYHoupB45xfyXFxHzOvKoYAumLfuPFdkAxF3agnk5ox5wzQBESACRCBOBIQKPnFanqY9DwGh5yK2HyH2gQgW4tj1gmnTpmHgwIF8DpCzG8tFsnDhQjz66KNg1z5jaWwdth7LBcdywrHGcqCwYgxOp5OPojs77wl7Eztp0iTBb3+7s0/qhxULUxpLBDoJsOi4Y9/+HSd068Fp/H/LouP62SZi4Jh7IadKkPSwREmA/YfzRMunfD44Hzz8LGqFCcPMd8Osj14IiNIcfpinuQmW1StgXb8GTIwLNqUShssnwDRzDjSlZbEsQWOJAE8g0T5Eovwy9qKUiW/s5SmrUM+unLJ2/Phx/tor+zMTA1WqrlVF33rrLdx22224/fbb8frrr0f8lMSDJxPj1tQvwWnHgXPsKdZWYLp5nqgiXKSb9nCuwNXXsGuwYddh/RF3rbygxwX+jY10jfD+KnY1tkvRiexA/jr/9djOq7E6RRZdjY0FNI0lAkQgJQkIFXxScnMpbLTQc4mHHyEmNsFCXPib1507d/IVUsObWG9e2dtb9laX5ZDbu3dvlzWuueYaPmccq9764IMPdvneAw88wCcLZvnm2HWMaJrUDyuaPdEYInA+Am0Nh7H70LOwFbQHu2TWGTBqyG+RmTeIwBGBiAjYXKf4XHCtztCLmsLMK1CR/2OoFZFXsY5o8W46O6uOo2XFMrRv/Qbw+q/GsibX62GcdjWMV82E0mSKdRkaTwSCBBLtQ8TjpsLZx8muYLJbCux2AntJyvLEdbaPP/4Y119/PV9gi+WkO7tVVlZixIgRF6x2f6HHJ548G51VOGrbBqfXBo1Cj4H6ccjTdF+MQoqPODsXJ2cLinLnz2XXCruIV2ND1WHDr8gywS6U185/NVYmRWxkExEgAkQgIgJCBZ+IJqXOMRMQei7x9CNi3gQLhPGxn+YCW2cukiuuuILPRaLX+68YnS8Xyd/+9jewX8xRY45ceGNXTZmDFp43hF1v+N73voejR4/yzh5LQBze1qxZw1fvys3N5Qs7sJwkrLGvWV/2Vpa9oWUJg6NpUj+saPZEY4jAhQiw6Lgj2/8fqjK/gi9QvFjuAPo7p6H/xf9N0XH0+PRIwOfjUGVZjiPN74FVR2VNJTdgmPm/UZB5aY/jxezg4zh07NnF53+z76/sMrXKXADj1dcga+JkyDWBRIliLk5z9XoCifYhxM7d23mArEJqXV0drFYrf7WV+WTs5eiyZcvQv3//4Dmzl5/sJSjz8T788MNzzp9FyTF/jFVxbWpqivj5SDTPiA1MkQH+q7HWsBx2/oi6TvHO5mkNfs3y3sXa2NVYlquOLzjBouoClWJDX4fy26noamysuGk8ESACcSQgVPCJowk0dTcEhJ6L1P2IiIQ4lh9kypQpfELfzupcVVVV/J+7q87VWZm0u2sJbJ59+/aBRdKxsdXV1bygxt6ivfDCC3zy3+4ai4T7y1/+goyMDF6UY5F6q1evBtMTWR6Ss3OURPL0Sv2wItkL9SUCkRCw1u3H7qPPocMcyk9jqDNi1ND50OekTpRAJHumvrET6HDVYm/9i7A4DgYnM+vHY2j+XdAoQ3mkYl/pwjNwLhfavtkEy8plcJ8+1aWzdlA5TLPnQn/xWMjo2nW8j6JXz59oH+Kee+7Byy+/zEersZy6ZzfmqLIXluzXoUOHBJ8NK7DAxLfOxqqovv322xg5smuV7aeeeopf++abb+a/f3ZjueXYdVX2i/lqF2pUQEvw8cS1o5tzdq0SG6wYywpR+AW7zrx2XKAITywGqWQ66DtFO2XgKiyLrgt+7RftMhRGyGWKWJaisUSACBCBiAkIFXwinpgGxERA6Lkk2i+LdFMRCXFscparjUW3sWqmTDxjbzpnzZoFliOutLS0y/oXEuJYpS3muDExjiX/ZWIcE+d+/etfn3Pt9exNsVwjLNKORdCp1WpcdtllWLBgAVikXixN6ocVy95oLBHoiQDn9eDQ9r+iOmsLfIFUP3I7MNAzC/0uup2umvQEsBd9n0XBVbeuwqGmd8H5nPzOlXI9hubfCXYdNVHXkrxtVrR+sRqta1fBa20NnYBMBv2Y8cieNRfaQf7IaWpEIN4EEu1DxEuI6+TU2NiIHTt28GIbi4xjoh97sdrZxBTiOv3F7s6I2cCKeVGTDoHOq7FdKsWGRdb5I+78wp3/amysTQadwhCIsjMGouw6I+5C12JZTjuNXJ+wn0Gx7orGR0/AebIK7Tu2guvogDwjA5ljx0NTRi+OoydKI7sjIFTwIXqJJSD0XBLtl0VKIWIhLtIFUqm/1A8rlViSralLwHJ6N/ZUPQ97viO4CWNtDkaNeBg6U0nqbowsF4WA3V2PyvqX0GwPXf3My7gEw813Q6PMFmWNniZx1Z6BZdXnaNu0Ab6wSBuZRoOsiVNhuno22FVUakQgkQQS7UPE62rq2czYFVMWDceEuYMHD6KszF/cRMyrqRQRl8gnNbFreX0eXow7O6LOX3SipUtxCrcv5HdEa6UcSj6Kji80wVeHZV9nn/V37HtGqOSBilXRLkbjEk7AVVeL+qVL4Dh0bsEVbXkFzHfNg7pAvKrHCd8gLSgpAkIFH0kZ3QuMEXouifbLIkVPQlwYMakfVqSHS/2JQLQEWHTcga1/xKnsnfAp/bMoOmQY5JuLvqNvjnZaGpfCBFgExCnrFzjY+Ba8gf8sKeU6DMm7HcWGyXGPQGDrM8fbsmIZbN/tAMLSmypM2TBNn4WsqVdBoc9MYcpkeioTSLQPkYhiDZ3ncd999/FpQ5YuXYo777yT/+tULtaQys9ZOtvu4hyBq7GBa7DBXHZd89oxUU+Mq7FquS4sl103ee0CV2RZ1Vi6Gpv8J4+9hKt5YiG49rbzGiPPNKB0wWIS45J/XGlhgVDBJy02m0KbEHouifbLIkVIQhwJcZE+M9S/FxFoqd6OPaf+Ckee//oha6Yz+Rh10cPQGuiNY295FByeJlTW/x1NHbuCW87VjcJw8z3QqvLiisHn9fLXT1gBBuexUN4qtqi6tA9Ms+bAcNmVkCkDinFcraHJicD5CSTa4Vu3bh1fyXTgwIHodErDrWMpQxYuXIhHH30U7OpnLI3N8fjjj+OZZ54BK+bAGiuONWDAAL5QFouaY7ngwttbb72F2267jb/OylKKRNoSzTNS+6h/8giw9AgOVjU2INTZ2DXY4Ndh+ew8Fji484s2wnfArsZmdakOy1eK5QtRhIpSsCg8tTwj7i+mhNudXj1rnlrUbSTc2btkkXGl82P7Ny+9yNFuoiUgVPCJdn4aFx0BoecidT+ChLiw85f6YUX3qNIoIhAbAa/Hhf1bn8Pp3D1AIFeywiZDueIHKBtxQ2yT02hJE2BRaGfaNuJA4+vwcP5CHgqZBuV5t6I066q4/meDs9th/XIdLKuXw9PY0IWTbsQoZM+aA93wUXG1QdKHQ8ZJjkCifQhWAMFsNvMi2M6dO8/Jr8uKYbHcbtu3b8eYMWNi4sVy+G7YsAEffPABbrgh9O/+sGHD+Hy9H3300TnFsr7//e/jk08+4Qtp/eAHP4h4/UTzjNhAGpASBDqvxgavwZ4nlx3La+cJ5DyNZWMKmYq/9tqlSqwiLK8dH2Xn/75SHihXH8uCvWQsywlXvdD/EkBIK1v8LOWMEwKK+lyQgFDBp7djZJXRWXX1hoaG874cFJOR0HORuh9BQlzYUyH1wxLzAaa5iECkBJpOfIO99S/AmeMODs0+U4hRFz8CTWZ8o6IitZX6x07A6bFgX8PLaLDtCJ23bhiGm+chQ2WOfYHzzOBpaebFN+u6teDsoSq+UChguHwCTDPnQFPWJ27r08REIFoCyfAhHnnkETz55JN8sapVq1ZBr9fz5j///PP45S9/icmTJ2P9+vXBLbFCV+zX9ddfzxfe6mzLli1Ddnb2OUWvOjo6+PlZYYbCwkIcPnwYmZmh69+s8Barcs8qs27atIkXBln78MMPefGNVWBlQp0yiojVZPCM9uxpXHoQ4K/Gdoms8xedCBWl8F+RtXvFuhqbEchlF1Yplo+y6yrk+a/GytMDcpS7aProA7T8+1+CR+dcdwPYL2pEIBYCQgWfWNZIh7F33HEH3nzzTbAX+OeL0hdzn0LPRep+BAlxYU+F1A9LzAeY5iIC0RDwuh3Yt/UZnMk/AAR8QmWbHEM0/4mSYd+PZkoaI0ECtW1fY3/Dq3Bz7bx1cpka5bn/hTLj1ZDF6T8D7G03u37atuVrwOsNUpHr9TBOnQ7jVTOhzM6RIC0yiQj4CSTDh3A4HHzF+S1btqCoqAgTJ05EVVUV/2dWjX7z5s389dHOdr5q9p1/X1JSwkfWGY1G1NbW4rvvvkNzczP/508//ZSfP7xxHMdHyLGIOCbkXXXVVXxRBxY9p9Vqwa7PXnrppVE9IsngGZWhNKjX8BJhtwAAIABJREFUEfBfjW3nC1DwIh375bEEBLtAfrvA37F+sTYZ/Fdj/cUnQhF1+s6rsYFcduz7LO9doiqXx7qvSMY3vPMGWlcvFzzEOGM28m8OVXkWPJA6EoEwAkIFH7GgOeuq0H5oKzhHB+TaDGQOGQ+NWdrVgNeuXYvp06eDVXL/+9//TkJcBA8DCXFhsMjpi+DJoa69mkDDsQ2obHoZrmxPkENubSlGXvII1BmmXs0mlTfv8lp5Aa6ufXNwGyZtOYab74VeXST61tibs4493/EFGOz79naZX5lvhmnmNciaMAVyrVb0tWlCIiA2gWT5EHa7nY9u+8c//oHq6mrk5ORg1qxZYDniSktLu2zzfEIcu8L6xhtvYOPGjbyQx8Q3lvuNRbSxue6//35e6Ouueb1e/OUvf8Grr76Ko0eP8lF5U6dOxWOPPQZ2dTXaliye0dpL44hAdwQ8Pjfs/HXY8Mg6JtqF5bULROF5fK6YIbKrsaGKsQHRrksuu86iFMaUuhpLEXExPxo0QRQEEiXEuZprUf/5Ejiqu6kGXFYB8zXzoM6RXm5u5n+wquoajYYv4FReXk5CXATPGQlxJMRF8LhQVyIQIuB2tmPftmdQZz4SjI5TWeWo0N+KoiGzCVWKEahr34r9DUvh8rbylstlKgzKuRF9TXNEj4Lzud1o+2YTL8C5Ttd0IaUdNBimmXOhHzMOMnnvvoqTYo9QrzeXhCNxHwHiKS5Pmk3aBNiLKbfPfzXWH2XnF+q6XIsNRNmx7/nAxbwhjVzvvwYbjKgL/zoUfaeVwNXYs3PEWc1AbQXg0QBKJ1B4AMiqDyGhHHExPx40ARAshMReSsWruZrPoObNheDsF6gGrDOg9LbFkhPjfve73+H3v/89HwVfVlaG/v37kxAXwYNCQhwJcRE8LtSVCJxLoO7IGuyzvAa3KXSdML+2L0aMeRgqXRYhkzgBt7cdBxpex5n2TUFLszQDMaLgXmSqu0bTxLoVb3sbWr9YjdY1K+G1+gU/vslk0F8yjq+Aqhs8JNZlaDwRSAoBEo7ExU48xeVJs6UPAXY11s5fjW3peh02EFnHR98FvnZytpg37r8aGyo4wUfcKTsj60zwX5H1i3hqWfyuxp741f1o9TZg97VASzepYrNPAqM+BXJzy1H6yOMx75smIAKJiIireXtRt5FwZ9PXllWg9BbpVANmkfSsEBSrjr506VKcOHGChLgIPzIkxJEQF+EjQ92JwLkE3HYr9u54Cg0FJwCZ//uqVgWGGX+MgkHTCZlECTTYdmJf/d/h9Lb49TAoMDDnBvTL/h7kskCJXBFsd9XVonXl57BuWg+fK3T1RqbWIGviFJhmzobKLL2QexG2TlP0IgIkHIl72MRTXJ40W+8k0Hk1tmsuu7BrsXyUHbs22wKvL1SMK1paSpk6kMvOeN4rskzI0ymNUMpUgpexH9iHQy89jq/vANz+mjTdNpUNmL5lOAbd/Uha5soTDIw6ikIg3kIcywlX/WoE1YDvelYSOeNYftjLLrsMx48fx4EDB5Cbm0tCXBRPHAlxJMRF8djQECLQPYHagyuw3/Ym3FmBKxMcUFA/EMPGPQSVJlRpj/gll4Db24FDjW/iVFuomqJB3RcjCn4Kg0acpLDsmo3jyCFYln8G287tgM8X3LTCaIJx+kwYp86AIqwCY3Kp0OpEIDYCJBzFxu/s0cRTXJ40GxG4EAH2M9vlsweqxoYi6vyFKPz57Tqj7FjVWB9CP9OjJeu/GhsWUcd/nc1H14UXolBZ3ah5dD42XdfabSTc2euzyLjv4U4Yp10drWk0jgjwBHoS4mr//Ve4m09HTcvT1gKvzSJ4vCLTBGVmtuD+Z3dU5RSj8Pv3Rz2+cyDLC/vggw/itddeA6uYyhpFxEWOlYS4MGbk9EX+ANEIInA2AVeHBXu+fQJNhaHcX+oWJYbn3YP8/pMIWJIJNHXsQWX9Ejg8TbwlMsjRP/s6DMj5D8hlypit83m9sO3YhpYVn8F57EiX+dSlZTDNnAPDZVdCphL+Jjxmo2gCIpAAAuRDiAuZeIrLk2YjAmIR4FjVWK+1a8GJsPx14XntRLkaywHKDsAdwfvcCa/IMezux6AdOFisbdM8vZBAT0Jc9WsPwVl7PGXIaAr7o+zHT8dk78mTJzF8+HD+Wur69aEX+iTERY6VhDgS4iJ/amgEERBA4PS+T3DA+R48hlB0XGHDEAwf/xAUKqqCKQChqF08nAOHGt9BjXV1cF69uhQjzPfCqB0Y81qcwwHrxnWwrFoOT0NYxmQAuuEjYZo1FxkjRtFVkZhJ0wRSJUDCkbgnQzzF5UmzEYFkEPBwLn/hCT6izv+7P8rurMqxXosoV2M796hrAXIa1MgbPRmGDDMylNn8VVkWccd+V8szyB9JxgORYmv2JMT1xoi4a6+9FqtWrcKuXbtQUVERPFES4iJ/uEmIIyEu8qeGRhABgQSc7Y3Ys/MJNBfVBkdomlUYYf4pcvtdLnAW6hYrgWb7flTWvQi7p1Mgk6Gf6Vo+H5xCro5pek9LMyyrV8C6fi24jrCk0AoFH/nGIuA0fcS57hqToTSYCMSZAAlH4gImnuLypNmIgJQJ8FdjObtfsAsIdS0ndqPh2/VwZgLN/WWwZ8V+HbaTgUKmgl6RHbwC2ynQ+a/GhgQ7ncIgeuV4KZ8D2daVQE9CXKy8UjFHnEwmg8lkwujRo7ts3+FwYMuWLdBqtbj00kv577333nsoLBQ/B7TQc5G6H0FCHAlxsf4bQuOJQI8Eqvd8gEPch/DqA06UFyhuGoGh438DhTI2IajHxXtxBy/nxOGm93CydQUQyOeSoSrio+BMuvKYyDirq2BZsQxtm78CvKGKufIMPbKmTodp+kwos3NiWoMGE4FUIiB1hy+VWDJbiWeqnRjZSwTEI8CKPNUsegic3Q7I5ah9aCK+lW0QvIDKKYdXwYGLMeOGHIpgxdhzhDuliRfzWLVYltdOIUJ6D8EbpI4JISBU8InFmFSrmsqEOKGNFXPo16+f0O6C+wk9F6n7ESTEkRAn+KGnjkQgFgIOay327HoSLUUNwWm0TRqMLL4f2WVjY5maxnZDwGI/hL31L6LDfSbwXRn6GGdhcO5NUMg1UTFjb6w79uyCZeUy2Cv3dJlDmW+G6erZyJo4FXItXT2OCjANSmkCUnf4Ug0u8Uy1EyN7iYA4BDiXCzWLF8BVXcVPmHvTrfBMHYF3q4VXl7zROB8di/8Gp8sKRyaQeft/wtsvjy84YfO0BK7KtgSLULh99hiNl4FFz7Frr6FrsCGhrlPEY78rY7yJEKOhNDwCAkIFnwimPKerq7kWNW8uAGdvO+80cp0BpbcthjpH/OiyWGwPH0tXUyMnSUIcCXGRPzU0ggjEQKBq1zs4IvsM3gx/dJzMA5S0XIyK8b+EXBHjq8sY7EqXoV7OhaPNH+CE5bNgFJxOacbwgnuRoxsa1TZ9bjcf+cYEOFdNdZc5NAMGIXv2XOgvGQeZQhHV/DSICKQDARKOxD1F4ikuT5qNCKQKgbqlS9C20Z8EXj92PArv+zmfz+2fNYtw2nGgx20UaytwQ+kidOyvxOnfP8FXbZfr9Shb9DRU+eZux7s5h1+k87YEqsP6f/dXi+0U7ixwcOcXS3o0LNCB5acLz1fHIupC0Xb+67Lsz2q5jvLYCYUap36JEOKY6UyMq/98CRzV5z7f2rIKmK+ZJ2kRju2BhLjIH0IS4kiIi/ypoRFEIEYCdssp7N77JFoLm4Mz6Rq0GNn3FzAVj4px9t473Oo4hj31L8DmClWsLc2agfK8m6GURx6l5m1vR+u61WhdsxLe1rDy6jIZ9JeM5QswaAeVk6PYex852jn5EHF7BkiIixtampgISJaAdcMXqH/t77x9qoJClC16CnJdBv9ni6sW79csuKAYppUbcGPpYpjU/sihlmX/RtMH7/Jfa/r2Q8nDj0Oujj4lisfnht3TGhTsmHDnLzzhF+xCFWMt8AVSgkQLWylTd8lXF8pfFybcKbOhk2dSHrtoIfcwLlFCXKcZzvoq2A5ug9dhg0Krh37IOGjMqZFnmYS4yB9CEuLIiY78qaERREAkAsd3vo6jyhXgdP4JZW6gzDoe5WN/RtFxETDmfB4ca/4Qx1s+hg/+KrVaZS6Gm+chN2NkBDP5u7rra2FZ+TmsGzfA53IGx8vUamRNmALj1bOhLiyKeF4aQATSmQAJR+KeLvEUlyfNRgSkTsBZdRw1ixfC53GD+RulC56ApqxPF7OZGLemfkm3kXEsEm66eV5QhGMDWUqN2v/9I2w7t/PzZE2aBvOd98QdBefjYPdaecHOL9SFIuw6mGAX9nccPDHZw/LYZSiNwXx1oWuw4ddkWREKI+QyurkQCexEC3GR2Ca1viTERX4iJMSFMSOnL/IHiEYQgVgJ2JqrsHvfU2grbA1OlVGfgVEDf42sguiuUsZqUyqNb3NWYW/di2hznQiaXWKYgvK826BS+N8iC232wwf5Agy2b7fxVzk6myLLCOP0WTBOmw5FpkHodNSPCPQqAuRDiHvcxFNcnjQbEZAyAa/NhupFD8HT4K/ubr77p8i6ctJ5TW50VuGobRucXhs0Cj0G6schT9N95BCbu+ax+XDX1/nnvvMnyJo0VRI4mFDo5GzBaLru8td1BEQ8t88Ro80yZCiykBGoFtt5PTa8Siy7Fsv+THns/KhJiIvxkYvTcKHnInU/goS4sAdE6ocVp2eZpiUCSSfAcRyO73wFxzVfgAvcoJS5gL7tV2DQ2P+BXC5Puo1SM4DzeXGi5RMcbf4nfPBXLdUosjHMfDfy9ZcINtfHcbDt2IaWFZ/BefRwl3Hq4lKYZs1B5mVXxnSVQ7Ax1JEIpDAB8iHEPTziKS5Pmo0ISJUA80Nq/8qi1nbwJmZNuQrmO+4W1VxW6Z0VgPC5XJCpVCh95HFo+vYXdY14T+bi7F2uv4ai7Lpej2XCXqxNI9eDXYX1F58IXYXtrBLbKeKxPHbp3IQKPunMQIp7E3ouUvcjSIgjIU6Kny+yqZcSaG88gt0Hn0V7QSgZrr4uE6PKfwtD/uBeSuXcbbe7TmFv3QuwOo8Gv1lkmICKvDugUmQK4sQ5HLBuXA/Lqs+Db6A7B+qGjeDzv2WMHE353wTRpE5EAJC6w5dqZ0Q8U+3EyF4iEB2Brnnc+qPk4cfi8vLP+tWXqH/5Bd5IVumd5Z9T6IX5TNHtLDmjPJwrUBX27GIToSqxLMquw2sNFvWK1lKVTNNjlVgm5Gn5PHayaJdJ2jihgk/SDOylCws9F6n7ESTEkRDXSz/CtG2pEmDRcUd3vIgT+o3wBfLpyp1AP/sUDLjknl4dHefzcaiyLMOR5vfB+dz8EaoVRgzNvwsFmeMFHanH0oLWNSvQum4NOFvYW1OFAoZLr4Bp5hw+oTE1IkAEIiMgdYcvst0kvzfxTP4ZkAVEIN4EIqlsKoYt9a+/Auv6NfxUGaMvQdEDv4Ksl966YDcrOryt3eewO6v4BBe4eRHtGcih5KvBnnsNNlQlln1fx+exk84tGKGCT7RcaFx0BISei9T9CBLiSIiL7hNAo4hAnAlY6/Zjz9E/wGYOiUWG2iyMGjYf+pzeJxTZXGdQWf8iLI5DQfIFmZdhaP6dUCuyejwNZ/VJWFZ8hrbNXwFe/1VW1lg1sqypV8E0fRaUObk9zkMdiAAR6J6A1B2+VDs34plqJ0b2EoHICLAXg9ULfwev1Z8juOjB30B/kfDUGpGt5u/NuVw49fQiOI8f4/+c84P/RM6110czVa8Zw14CO7j2YIGJc6vEhiLtPL5Qga9oAMkg48U4JsqdfQ3Wf002dF1WKVNFs0REY4QKPhFNSp1jJiD0XKTuR5AQF/YoSP2wYn5qaQIikGIEOK8Hh7f/DScNm0PRcQ5ggGsG+l30414RHcccoJOtK3G46V1wPhd/gip5Ji/AFRquuOCJsiTA9srdaFmxDPa9u7v0VebmwTTzGmRNnAq5Lr1zfKTYY0/mpigB8iHEPTjiKS5Pmo0ISImAz+PBqd8/AcehA7xZ2XOvQ+4NNyXERHdjA6offQicrR2QyVD8q/nIGB55hfmEGJtCizCf0+Wz+yPsWGVYPqrOgs5iE5057Tq8Fr5ARayNXXftmr+ua5XYTuFOLQ8kn45iQaGCTxRT05AYCAg9F6n7ESTEkRAXw8eAhhKBxBCwnNmDPSeehz3fHlwwqzYbo0bMR4apLDFGJGGVDnc9KuuXoMW+L7h6vn4MhuXfDY3SdF6LmIPLIt9YBVRXzcku/TT9B8I0ey4yx4yHTEFl7JNwrLRkmhKQusOXatiJZ6qdGNlLBIQTaHzvbT5KnzXd0OEo/vXDCb0iatv9Hc786Vm+QrzcYEDZoqehys0TvgHqGRMBlseOCXUdQcHOL9z5o+1aAtF3FthFyWOnDVyL7bwGa+ILUXS9JpsNVqDi7Dx2QgWfmGDQ4IgJCD0XqfsRJMSREBfxw08DiEAyCLDouIPb/oQa0w74lH4L5B0yDObmoO9FtyTDpLityd4q1ljX4lDj2/AGytUr5Rl8MYYiw8TzJrz1trejdf0atK5ZCa+lJWSfTAb9RWP4Cqja8oqUTJgbN9g0MREQiYDUHT6RtpmwaYhnwlDTQkQgoQTad2xF7V+f59dUmLJR9vgzUGYZE2oDW6z543/yv1jTDBiE0vmLIFMGHMyEW0MLdkfA6/PwYlxnhF1IuPMLdp3CXYenFbHmsVPIVMhg12IVfsGOXYM1NA6GQZmPfgP6QC5T+H9BQX50kh9XEuKSfADxWJ6cvnhQpTmJgLgEWmq+xZ6av8CRF8pDYarNw6hRD0ObVSTuYkmYzeFuRGX939FkD10lzc0YjeHme6BVdp/DzV1fB8uq5bB+uQ4+V4iLTK2GYcJkmK6eDXVhcRJ2Q0sSgd5DgHwIcc+aeIrLk2YjAlIg4KqrRc2ih8DZ7YBCgZLfLYRu8JCkmObjOD4qrmPPLn5941UzkX/rj5NiCy0aGwGWxsXOtfsj7MKi6tg12NA1WRZ11wJvoNiZkBWH2q5Hsa4CeX3Cq+vKgqKcAkycUwYEOv/vioBYx/4+FSvFCuGS7D4kxCX7BOKwPjl9cYBKUxKBOBDwelw4sPUPOJW7GwjcrlTYZCiXXYeyUf8ZhxXjPyWLgjvdtgEHG9+Ah/NfwVXItBiSdytKsqZ1+8PcfuQQf7XDtmMbf72isymyjDBedTWM02ZAYei5kEP8d0crEIH0J0A+hLhnTDzF5UmzEYFkE2CFEmoWL4Cruoo3Je9Ht/KV2pPZvO1tfL44T1Mjb0bBT/4HhssnJNMkWjuOBPg8dlxHIH9d53XYznx2Xa/Hujg7uhfihBvoj6A7V6ALRdcFRDwJVYoVvrvk9SQhLnns47YyOX1xQ0sTE4G4EGiq2oK9tf8Pzlx/EQPWss8UYORFj0BryI/LmvGY1OlpQWX9y2js+Da0D91wjDD/BDqVucuS7A2u7dvtvADnOBKqoMo6qYpLkD1zDjIvnwC5Wh0PU2lOIkAEzkOAfAhxHw3iKS5Pmo0IJJtA3dIlaNu4njdDP/ZSFN73oCQihhzHj6LmyUcBjwcytQaljz4BTUn65h9O9nOQKuu7OSeY4KOQKdFnQAk4nxecz8NfgfXyX3vBwcP/HmuTydiF105RLiTe8RF2YX8vg1wSn5lY9xvreBLiYiUowfHk9EnwUMgkItADAa/bgX1bn8WZvP3B6Dhluxzlqh+idLi0S9KzN3O17V9jf8Or8AQqSMllGpTn/hfKjDPAfjB3Ns7pgHXjerSuWg52FTW8sUTHpllzkTFydEKTHdPDSQSIQIgA+RDiPg3EU1yeNBsRSCYB64YvUP/a33kTVIVFKHv0Sch1Gck0qcvaLL9uw+uvSNY+yYDqZYYIEXyYL38+ga5TrOsU7oDQ7ZVoUMrArsV2XoVl12A7xTsln7sulfLYMaGTVc9l14rZ/3dYsQyVXCMIi5BzYRNJ3Y+gYg1hxy31wxL0ZFInItBLCTQc34R9jS/Bme0OEsg9U4yRlzwCtT5HclScnlbsb1iKetvWoG0m7RCMMN+LDHVh8O88lha++ELrutXgbGHl3hUKZI6/HNmz5kDTt7/k9kcGEYHeRoB8CHFPnHiKy5NmIwLJIuCsOo6axQvh87jBcteWLngCmrI+yTKn23WZmFL/yoto++pL/vv6seNReN/PKfpIUqeUeGOECj5CLOMFOwQi6cKi6ZhYx4pShL7ngU8EwU4WzFfXNdKOz2HH//KLd4nOY+fh3LB6GuAOpOEJZ6eS65ClzIdSrrogUqHnInU/goQ4EuKE/NtBfYhAShDwujpQue1p1OYfBgLBZMo2OSp0N6O4Irl5SMIB1rVvwb76V+Dm2vi/lstUGJRzE/qaZgej4Jw11bCsXIa2bzbx1yU6m1ynQ9aU6TDNmAVlTvfFG1LisMhIIpBmBKTu8KUabuKZaidG9hKBcwl4bTZUL3oInoZ6/pvmu3+KrCsnSRIV53Si5gmWw+4kb1/uTbfyLzup9V4CQgUfMQkxwY5Jdp1XYb1B8S4k1vkj7Jhgx8W4NIuwk3fNYxeW165TrGPiXfgtnWgX9XAutLhPX/A6LxMJs1UlFxTjhJ6L1P0IEuJIiIv2s0TjiIBkCdQf+QL7LK/CZQoJWHm1fTByzMNQ6YxJs9vlbcOBhtf466idLUszECMLfgq9ugTsh6+9cg8sK5ahY6+/ildnU+bm8dVPsyZNldR1jqTBpIWJgMQISN3hkxiuHs0hnj0iog5EQNIEWE7b2r/+EbadO3g72UtE8x3/LWmb/VVd54OzdwByOUp+uwC6IUMlbTMZFz8CQgWf+Flw4Zk5X0CwC+Squ5B4F6uNfIRd2PXXw/uP4bmnn8eX6zeiubkFRUVFmDNnDhYtWoT8/O7zdDe7TncbCXe2bSwyLkddfF6ThZ6L1P0IEuLCjljqhxXrB4jGE4HeRMDtaEfl9idRX3AckPl3rmpVYGjWHSgcPCPhKOptO7Cv/mW4vBZ+bRkUGJjzQ/TLvhYyrw9tW77mCzB0vontNFDTfwCf/y1z7KWQKQIlYhNuPS1IBIhATwTIh+iJUGTfJ56R8aLeREBqBFqW/RtNH7zLm6XpNwAl8xelRCGp9m+3o/Z//8DbrTCaUPbY01CasqWGl+xJAAGhgk8CTIlpCZaHzZ+vrrPQRNersKGoO1Z4ouc8dpvWf4Nbf3AP7B12DB4yEOUVg3Bg3yEcPXwcxSVFWL7hI5SWlXYpNMHi/GyeFsH7yFGXnjdnnNBzkbofQUJc2OMg9cMS/ORSRyJABIIEag+uwP72t+A2Bqoa+QBz3QAMHzsfKm1m3Em5vTYcbHwDp9v8eUdYM2j6YYT5p8jw5MC6bi0sa1bAawn74SSTQX/RJbwApy2vSHj+hrhDoQWIQBoSIB9C3EMlnuLypNmIQCIJdOyvxOnfPwH4fJDr9Shb9DRU+V2rwCfSnkjXavzgXViW/Zsfxvywkt88AplSGek01D/FCQgVfFJ8m0Hzz5fHrvMqLBPy2m3tGDt0AhrqGvGL+f+D3yx4kB/Pxj4+/1m8+OdXMGX6BLz36esxYdErs5Gp7D7Ht9BzkbofQUJc2CMi9cOK6WmmwUSgFxNw21uxZ8cTaCysDlJQW5QYlv3fMA+cEjcyjbZdqGx4CU5PM78Gi4Lrn30dyrwTYF29EtYv18HndAbXl6lUMFw5GaaZ10BddP6Q7LgZTBMTASIQNQHyIaJG1+1A4ikuT5qNCCSKACsyVb3wd/BaW/klix78Df9yMZWaz+vF6T88Bfv+St5s9mI076ZbUmkLZKsIBIQKPiIsxU/hbK1C++mt4NwdkKsykFk8HhpjX7GmF2Wet99+G7feeiuGDBmC3Xu/g0/GBQtNOF12XDz8Upw8UY11W5dj2MghUeexy1AYYVDldWuz0HORuh9BQlzY8Ur9sET59NAkRKAXEzi9/xMccLwHjyGQ3JQDCuvLMXz876BQZ4hGxsPZcajxbdRY1wbnzFSXYbBrLjwrvoVtx1b+LXFnUxiyYJw+E8apM6DIyhLNDpqICBCBxBEgH0Jc1sRTXJ40GxFIBAGfx4NTv38CjkMH+OWyr73+/7N3HuBRFd3//+5ueiO9kdBFpAoIAhYUFbB3/alY0L9dXgQUCSWFFrBgV+zYK6DYARUbIoJIExURMJX0kLZJtvyfmZCYQMrd3bubu7vfeZ48r2925sw5n7lXT747MwdRl1/tiqlVn8NUXoastJSmEwvx90xHyEkjVZ+HBrVLQKng42gEdZX5KPh1OYzFDe9N8xYQ1Q+xw+6AX0i8o9OoMn7atGl47LHHcOutt+L5558/xuakSZPw5ptvIiMjA6mpqWi4x86EKnMZjOaGInVKGnfEKaHkQX2Y9HnQYjIUEmiDQF1VMXb8uhAlCXlNPfxLfDEg9g5E9zjFYW4l1buxq2A5jKbCI7Z06GocgdCPS1H3594W9n0TEhE+4XyEjjnNLe5NcRgODZCABxNgDqHu4pKnujxpjQRcQaDonTfkfbeiBfYfiMT7ZkOnP1LG3hUOqDxHzd4/kbNkPmA2QxcQiOT0RfCL54kFlTFr1pwrhLi6yjxkf5sKS13bIpXeLxRJYxdoQoy77bbb8MILL2DmzJlYunTpMWs3depUPPHEE7jsssuwcuXKps/rLbUoqctWvNa8I04xKs/oyKTPM9aRUZCAEgLZuz7An6aVMIcc2ZlmBhKL++OEETNh8A1QYqJFH5PFiL+L38G/5V80/T6gvgtiPzXAZ1fD0dTGFtivvzzmEDT4RLdOUG2GxAEk4MEEmEOou7jkqS5PWiMBZxOo3LoZ+U8uk9MYIiIbihyEdV6lerXiLVv3OYrefFWa80tKRtK8BdD7254nquUP7biOgCuEuOzv0lvdCXd0lGJnXNLp6a4Lvo2Z5syZg8XDNMokAAAgAElEQVSLF+Pqq6/GO++8c0yviy66CB9//DGGDx+OLVu2tPicVVNb4uLR1GY8mPR1+rtNB0jApQSMFYew87dFKE0oaJo3oNgPAxOnIDJ5RNPvig/8hNyctTBZquGjD0Ji0gREdR/V9HlpzZ/YXfAsquvzG35nBcJ/9UX4unroTUe66fUIGTm6oQBDj54ujZOTkQAJOJ8Acwh1GZOnujxpjQScSaAuPw/ZGbNhqakBDAZ0nZWKwOOOd+aULrMtLqE/9OwTqNz8k5wzZNQpiLv9HhbSctkKdN5EzhbixJ1wWV8/oDjA5HFLO/3OuC+//BITJ05ESEgI9u/fj+jo/+5xy8nJQZ8+fWA0GtG3b1/8+eefLWIzWepRWp8jK7i21fQ6AyJ8u8JH79tmH6XrovU8gkJcsyXW+mIpfkvZkQRIwCYC/+54B3utH8EcfGR3nAlIKh2ChOSJ2J31BKpjao6xF1QYiP7d/4dCv904WPZpU7lvn1IgZg0QeKQuhDjK0OWMcehyzrnwjWr90lGbnGVnEiABTRJgDqHuspCnujxpjQScRcBSW4vshfNQl/WvnCL6mhtk0SlPahajEVnz56A+N0eGFXP9zehy1nhPCpGxtEKgI8En/5cnUV+Zazc7U00pzLVliscbAsLhExChuP/RHX1DEhE/Yord48VAIUyfdNJJEP+NFv/79NNPo3///ti5cyduv/127NmzByaTCf369ZP/fHQTYtxhUyHqLcf+beWrD0SYT0y7Ipyw19G6NM6p9TyCQlyzp0Pri+XQW8PBJEAC7RIwHs7D9h2LUB5f9F8/8YWNoZ1houZDs6tPwn4BIr8G9PWAT2QUuow/D13Gngl9oHqFILiMJEAC2iTAHELddSFPdXnSGgk4g4D4o7zgpeWo+OFbaT5kxCjE3TXVI3eL1eXmSDHOajTKXX9Js9MR0Ps4Z2ClTY0Q6EjwyfomBbVl+zXibcdu+If3RPKZmR137KDHwYMHcf7552P37oaqwo0tLi4OU6ZMwdy5czF69Ghs3LixTUvizrhaSxWsVgt0Oj389cHw1fsr8q2jdWk0ovU8gkJcs+XW+mIpejLZiQRIwCECB7a9hr8Nn8MS9F9V044M+pQBMZ8AgQcA/x69ED7xfIScdDJ0Pj4dDeXnJEACHkKAOYS6C0me6vKkNRJwBoHyb79G4SsNlRN94xOQnLbIo798rNj8Ew4987iM1ycyEsnpS1jt3hkPlkZsdiT4eOOOuMalEbveVq9eLcW2mpoaDBgwANdddx1WrVolK6r+v//3/2RRB2e0jtaFQpwzqDvZJpM+JwOmeRJwEwK5ez7GLt83FXub+AoQGT0MEedegIDjT/DIb4IVw2BHEvBSAswh1F148lSXJ62RgNoEjAf2I2dhKqymeuj8/JGUuhD+SclqT6M5e4VvvYrytZ9LvwIHDELijBQW3tLcKqnjkFLBx97Z3PGOuI5ivfnmm/HKK6/gjTfekMKcM5rSddF6HsEdcc2eDq0vljMeZNokARI4lsDOHzOQF3fsvQZtsYrP6o3BZy4iShIgAS8mwBxC3cUnT3V50hoJqEnAXFWJrPTZMBU2FLuKu+1uhI45Tc0pNGvLajIhZ+kCGPc2XEQfceGliLr8as36S8fsJ6BU8LF/BsDdqqa2F2t+fr4s0uDn54esrCwEBgY6gqbNsUrXRet5BIU4CnFOeUFolATcmcC27x9AYcJBxSHE5HXH0NOWKu7PjiRAAp5HQOsJn7sRJ093WzH66y0ErBYL8p54BNW/bZUhh515NmJv/H/eEr6M01Ragqy0FJgPl8v/n3DvTASfOMyrGHhDsEoFH0dY1FXmI/vbebDUVbRpRu8XiqSxC+AXEu/IVKqN3bVrl6yOGhAQ0GQzOzsbl19+OTZv3owVK1bgxhtvVG2+ow0pXRet5xEU4ijEOe0loWEScFcCtu6ISzzUHwNPSXXXcOk3CZCACgS0nvCpEKJLTZCnS3FzMhJQTKD0k49Q/MHbsr9/z15Imp0Bna+v4vGe0rF6z27kPrhQlJGEPigYyRmZ8I2J9ZTwGIcN1TkdhSXEuIJfl8NY/McxpgKi+iF22B2aEeGEgzfddJO8H27YsGFISEhAQUEBfvjhB9TW1mLevHmYP3++o0jaHU8hzql4O8c4k77O4c5ZSUBrBIoP/IStpobLeJW04b73Iqr7KCVd2YcESMBDCTCHUHdhyVNdnrRGAmoQaCE+BQcjOd27xafSz9ag+L23JFr/7j3Qdc586P381EBNGxogoFTwUctVcWdcVe4vMNdXweAbjODEEfDv0l0t86rZ+fDDD7F8+XJs374dxcXFiIiIkFVS7733XpxxxhmqzdOWIaXrovU8gjvimq2w1hfL6U81JyABEmgi8MNPk1EdU9MhkaDCQJw6+pUO+7EDCZCAZxNgDqHu+pKnujxpjQQcJXDMccxpDyB4yFBHzbr1eKvVivwnl6Hq119kHGGnn4nYm29365jo/H8ElAo+ZOZaAkrXRet5BIU4CnGufXM4Gwm4CYGyvJ3YUrQYlkBrmx7ra3Q4KXo2whMGuUlUdJMESMBZBLSe8DkrbmfZJU9nkaVdErCdgCxQ8OACGP9igYKj6Zmrq5GdMRv1h/LlR7GTb0PY2HG2Q+YIzRFQKvhoznEPd0jpumg9j6AQRyHOw19VhkcC9hMQYtyuA8ta3RkndsIN7DGdIpz9eDmSBDyKgNYTPneDTZ7utmL015MJFL3zOsq++FSGGDhgEBJnpECn13tyyDbFVpt1ENkL5sFaVwedjy+S5s2Hf/eeNtlgZ+0RUCr4aM9zz/ZI6bpoPY+gEEchzrPfVEZHAioQKD64CXnZa1FvqYKvPhgJSeN5J5wKXGmCBDyJgNYTPndjTZ7utmL011MJVG7ZjPynlsnwDBGR6JaxBIawME8N1+64Dv/4HQpeeEaO94mJRXL6YhiCQ+y2x4GdT0Cp4NP5nnqXB0rXRet5BIU4CnHe9eYyWhIgARIgARJwAgGtJ3xOCNmpJsnTqXhpnAQUEajLz5PHLi01NYDBgK4paQjs01fRWG/sVPDqizj8zXoZetCQoUiYej93Drrxg6BU8HHjEN3SdaXrovU8gkIchTi3fAHpNAmQAAmQAAloiYA9CV9RURHCw8Ph4+OjpVA04Ys9PDXhOJ0gAQ8hYKmtlcct67L/lRFFX3sDwsef5yHROScMa309shenoXb/P3KCyMuuRuRFlzpnMlp1OgGlgo/THeEELQgoXRet5xEU4ijE8dUmARIgARIgARJwkEBrCd+WLVvw2Wef4YorrkD//v2bZli9ejXuvPNOFBYWIiQkBPPnz8fUqVMd9MCzhms9gfYs2oyGBFoSENVAC158FhU/fic/CBk5CnF3ToVOpyOqDgjUFxUiKy0FlqpKQKeT9+kFDRxMbm5IQKng44ahubXLStdF63kEhTgKcW79ItJ5EiABEiABEtACgdYSvhtvvBHvvvsucnJyEBUVJd3cv38/+vXrh/r6eiQkJODQoUMQf/R+9dVXOOOMM7QQiiZ80HoCrQlIdIIEnESgfMNXKFzxgrTuG5+I5LRF0AcGOmk2zzNbtXM78pYtAaxW6ENDkZyeCd+oaM8L1MMjUir4eDgGzYWndF20nkdQiKMQp7mXiw6RAAmQAAmQgLsRaC3hO/744xEZGYmffvqpKZx58+Zh0aJFePjhhzF9+nRs3boVo0aNwgUXXACxU46tgYDWE2iuEwl4KgHjgf3IWZgKq6keOj9/JKUthH/XZE8N12lxlXy0EiWr35f2/Xv1QVJKGnS+vk6bj4bVJ6BU8FF/Zlpsj4DSddF6HkEhjkIc33QSIAESIAESIAEHCbSW8In73yZMmCB3xTU2Ibrt3r0bxcXF8PPzk78eN24cDhw4gH/+abhXiI1CHJ8BEugMAuaqSnms0lRUKKePu+0ehI45tTNccfs5rRYL8h57ENU7fpOxdDlrPGKuv9nt4/KmAJQKPt7ERAuxKl0XCnFaWC2FPmh9sRSGwW4kQAIkQAIkQAIuJtBaDhEaGorx48dj5cqV0pvKykq5Q04Ib1988UWTh5MmTcKqVatQXV3tYq+1Ox1zMu2uDT3zTAJSOHr8YVRv/1UGGDbuHMTecItnBuuiqMyVFQ3CZnGRnJHCpovAqzSNUsFHpeloRiEBpeui9TyCO+KaLbjWF0vhs8luJEACJEACJEACLibQWg4xYMAAVFVVyd1uoomdcddccw0efPBB3HfffU0eXnTRRfjll1+Ql5dns9c1NTXIzMzEO++8g3///VcKfRMnTsSCBQvQtWtXRfbKyspkUYmPP/4YmzZtknfa+fv7ywIT1157Le666y74tnKk6qabbsKrr77a5hzPPvss7rjjDkU+HN2JOZld2DiIBOwmUPLJhyj54B053r9nbyTNTudRSrtp/jfQuH8fshelASZTw1Hf1IXwT+JRXxXQOt2EUsHH6Y5wghYElK6L1vMICnEU4vhqkwAJkAAJkAAJOEigtYRv1qxZUnS75JJLcOaZZ8p/FmLbnj17cNxxxzXNmJycLEUzIYLZ0oxGo7QrxonCD6eddpoU/TZv3oyYmBj5+169enVocu7cufLeOlER8cQTT0Tfvn1lRdcff/wRtbW1OPXUU/Hll18iKCioha1GIU4cv42Pjz9mHlGsQvhnT9N6Am1PTBxDAlolUL1nN3IfXNhQXCA4BMkZmfCNjtGqu27nV8viFwlHil+0/Pep2wXlBQ4rFXy8AIWmQlS6LlrPIyjEUYjT1ItFZ0iABEiABEjAHQm0lvAVFRVh5MiRTTviRFyiQIMo1NDYfv75Z4wePVrukBNCnS2tUUAT49euXYuQkBA5fNmyZZgxYwbGjh2LDRs2dGhS7KgTu+LuvvtudOvWran/3r17cfbZZ8uddikpKVi8eHGrQtw333yjesVXrSfQHUJlBxJwEwKm0hJ5fNJ8uBzQ6ZAwbSaCBw91E+/dw01RGbvgpeWo+OFb6XDw8JGIv2ea/PKDTbsElAo+2o3AMz1Tui5azyMoxFGI88w3lFGRAAmQAAmQgAsJtJXwiXvhPvjgA7nDbPjw4fJ+uObto48+kmLZ5MmTMXjwYMUe19XVITY2FuXl5bLC6NChLf9wHjJkCHbs2IEtW7bIee1tb7/9tjye2qNHD+zfv59CnL0gOY4ENEjAajIhZ+kCGPf+Kb2LuOgyRF12lQY9dX+XLLW1yF6YirqsgzKYqKsnIeLcC9w/MA+OQKng48EINBma0nWhEKfJ5WvdKa0vlhuhpKskQAIkQAIk4FUEXJ1DiF1oQtTr3bs3GpPS5sDFHXGpqalIS0tDenq63Wvx+++/Q9x1Jyq8imOqzVvj0VTuiLMbLweSQKcSKHr7dZR9+an0IXDAICTOSIFOr+9Unzx58rpD+chOnw1LTTWg16PrA/MQePwJnhyyW8emVPBx6yDtcH7r1q1Yt26dvAZD/Ih7ZUUTOz/baytWrMAzzzwDkVeInEJUkRc7+8eMGWOTF0rXxdV5mU1BAOCOuGbEtL5Yti4u+5MACZAACZAACbiGgK05hNjJJo5+JiUltXq/WkdeP/bYY5g2bRquvPJKvPfee8d0//TTT3HBBRfg0ksvlRVZ7W2ffPIJLrzwQnlk9eDBhp0cja1RiJsyZYpMwM1mM3r27Cn79+vXz94p5ThbeTo0GQeTgBcSqPzlZ+Q//aiM3CcyEsnpS2AIC/NCEq4NuWrbFlmdVjRDl3B5H59PeIRrneBsiggoFXwUGfOgTuLeW7Gb/+jWnhB377334vHHH0dgYKCsJi/uuP3qq69k7iBODQibSpvSddF6HmGXEKdGha5G0EJNFcmkUFPF/SRhYWHyCMWdd94pk8ej2xlnnIFvv204X99a+/zzz2W1MHua1hfLnpg4hgRIgARIgARIwPkEWsshxL1topqpEKqaHx198sknMXPmTIjjpeKOIPH5o482/EGstIm75sQYIcaJO+GObtu3b5eFF4YNGwbx7bW97ZxzzsH69eulj0888UQLM21VTRUxiTxOJN0+Pj52Tc2czC5sHEQCigjU5eciK30OrMYawGBA15Q0BPbpq2gsOzlOoPiDt1H6SYOQEdC3H7rOnAudnf+udNwbWmiLgFLBRy2CtcaDqKzYDIu5GnpDEEJCR8I/oLta5lWzs3TpUlkRfsSIEfJHXF0hdsy3JcSJHELkElFRUfjpp5+ailWJfxbajigEJa6+CA8PV+Sj0nXReh5hsxCnVoUuQbnx21yRsImLhkXVsKysLLlAYiFnz54tq3g1b41C3OWXX950KXHzz8XlxIMGDVK0iEd30vpi2RUUB5EACZAACZAACTidQGs5xFVXXQXxBaGolNpYSGHnzp1SIDMYDDKBFRVUxe44sWvt4osvVuznbbfdhhdeeAFz5szBwoULjxknElVRmVX8/PXXX4rtNu+4fPlyKaiJ5Hj37t1ITExsYUcIbQEBAfKIrNjZl5+fL+MVR01KS0shvgFXIjAKPuKneRNcJk2aJEVEISaykQAJqENA3lW2YB7qsv+VBqOvuxHh55yrjnFaUUTAajYj9+HFqNmzW/YPn3g+ov/vekVj2cl1BJQKPo56VFeXj4Lc5TDW/HGMqYDAfohNvAN+fsdWJnd0XrXGizygPSHuvPPOk7mByAdEXtC8TZ06VX7JJ4pYCR1HSVO6LlrXdmwW4tSq0CUuLRbCm8VikWeMRWWvxvbdd9/JLYvim2IBulevXk2fNQpxQjUV6quaTeuLpWastEUCJEACJEACJKAegdZyiD59+iAhIQHff/9900Qi0RRfRL7++uuyCILIZ/r374/TTz8dX375pWKHnC3ECZ9FxdT6+nqsXLmy1VMKbTkrRDshnokc759//pH5XntN3GGXkZHRahcKcYofCXYkgQ4JyOqdLz6Lih+/k31DRo5C3J1TWb2zQ3LqdzAdLkdW6iyYy0ql8fi770XIiFHqT0SLdhNQKvjYPQGAuro8ZB9IhcVc0aYZvSEUST0WaFaMa0+IEycpIyIipFAnNlyJL+2aN5FriPxHaZV3MVbpumhd27FJiFOzQlfjnSMTJkzAF198ccyDJ74VXrNmDd59912Ib5QbG4U4R151jiUBEiABEiABEnAGgdYSvtDQUHlPm6g82tjEbjiRjIovJPVHLkUXV2qIy4v//bdhh4qS5syjqbt27ZKJsdjVJr6pFsdSbW3i7jpx78srr7wCcYS1vcYdcbbSZX8SsI9A+Yb1KFzxohzsm5CI5NRF0AcG2meMoxwmULP3T+QsmQ+YzdAFBCI5bRH8ElruPHZ4Ehqwm4BSwcfuCQBkH0hvdSfc0TbFzrikHvYXXnLEx47GtifE/fbbb/JqjpiYGBQUFBxjShxxFScGhFhXUlLS0VTyc6Xr4lFCnJoVusQuOLHrrSMhTpwpPuuss5oWhUKcoueTnUiABEiABEiABFxIoLWETxzpPPPMM7F69WrpiUgyY2NjpTj34YcfNnl3/fXXS9FKfHOstDmrWIPYoXfKKafIo6Jip5qoumpPE9eLZGZmYvHixUhJSbHZhNYTaJsD4gAS6GQCxgP/IHthKmAyQefnj6S0hfDv2v5u1U522SumL1v3OYrefFXG6tc1CUmpC6H3D/CK2LUepFLBx944xJ1wWfsfUDw8uedSTd4Z154QJzZWiQ1WQowT/11vrQkRTtQKOHz4MMQXmB01peui9TzCph1xaiZ94j4UcbRUqKBtHU0VFbrEt7KivG1jaxTixBHZ4uJi+W1y3759ZaUN0d+RpvXFciQ2jiUBEiABEiABEnAegdZyCLH7TQha2dnZ8PX1xYsvvojbb79d7jK7++67m5wR96fs2LFD9lPa1PxytHFO4etpp52Gffv2QdzbIvI+e5u4W07cMSfukfvf//5nsxnmZDYj4wASaJOAubISWekpMBUVyj5xt9+D0NGnkpgGCIjjwoeWP4nKnzdKb0JGnSLXR9yhzta5BDoSfPJznkR9Xa7dTprqS2E2lykeb/AJh4+P/RV2ff0SEd/V9h3uHTnYnhD31ltv4brrrpNf8P3www+tmhLHVXNycuTP0XfRtjago3VpHKP1PMImIU7tYxDiYmJxP4o48jpmzBh5ZlgkoRs3bpSL9dprr6Fnz54t+LdVNVUkuPPmzZM/9jatL5a9cXEcCZAACZAACZCAcwm0lkOIHWGimMLJJ58s8xxxTLO6uloKXV27dpUOiT/C4uPjcfzxx0Pckau0Nb8uZNu2bbIARPM2ZMgQKe5t2bJFVqPvqIljqOKOFlFMYvLkyXjppZfs/kNQ3AUjikSII7ji/pdTT7X9D37mZB2tGD8nAWUErBYL8h5/CNXbt8kBXcaNR8wNNysbzF4uIWAxGpE1fw7qc3PkfNGTJiP87AkumZuTtE2gI8Ena38Kao373Qahf0BPJPfMVN1fCnH2IbVJiHPGxcDiG11xB1xRUVFTBGFhYbjvvvvwwAMPtNgNJzqkpqbKHXAioRUXIIskTxznEBXDxJEO8e2t+Ba3o8b7SDoixM9JgARIgARIgASUEmhNOBK7/sX1Gps3b5ZmxC7+hx56CNOmTWsy+9VXX+Gcc86RXyS2VbCgLR8aC2iJnGjt2rUIDg6WXZctWyarjx19+fFTTz0F8XPppZfKY6ONTYiDojCDqFovcjLxDbao6tpe++OPP/DLL7/I/v7+/k1dxd13Il8UR2+FGChEQnt2dlCIU/rksR8JtE+g5OPVKFn5ruzk36s3klLSofP1JTaNEajLzZFinNVoBAwGuU4BfY7TmJfe5U5HQhx3xDU8Dzyaat970alC3COPPIKZM2fKY6XiHhJRHVVU1xJim0jgzj//fIiiDkqaSEDFfXPiPpbc3FwEdnDxKCt0KaHKPiRAAiRAAiRAAkoItCUcicqhYqebEKjErjWxU6x527BhA7Zv344LL7ywRZV4JXMajUaIkwI///yz/HJSHCs9ePCg/P/iYuRNmza1sNmY+9x4441YsWJF0xRCGBRfZArx7eqrr5bHaFtrzccIv8X9d+Jul5NOOknOJ/IvUeW0oqJCnnIQIqP48tSeRiHOHmocQwItCVT/vhO5Dy0WW2+hDw5BckYmfKNjiEmjBCo3b0L+Mw1XAvhERiI5fQkMYWEa9dbz3epIiHOUgDfcEcdiDW0/JTYJcWoeTW1M4ER5e/GNamPlMOGq2WyWSZ1YuM8++wznnnuuoud8xIgR8giG2GUnEtP2GnfEKULKTiRAAiRAAiRAAgoIdJZwJE4DiN1tYhebOCUQGRkJUYV1wYIFUgxr3toS4kRV01dfbbgsvL0mjtE2NiG6id19Quw7cOCAvLdX7IwTwpsQFcXpBCHS2ds6i6e9/nIcCWiNgKm0BFmps2CuOAzodEiY9gCCB7c8wq41n+kPUPj2ayj/8jOJInDAICTOSIHuSIVt8nEtAWcLcSIaT6+aKnIUkQuIKyvEFWSN13I0rqS4vkJUaT96B397K610XbSeR9gkxKlZrOGWW27Byy+/LBNFcbTi6CZ+L3bGzZo1q8XxifYWRdw39/bbb8tk9JprrrH5TdX6YtkcEAeQAAmQAAmQAAm4hICSHKKgoEBeRiyaSEZFBVW21gko4Ul2JEACrROwmkzIWboAxr1/yg4RF1+OqEuvJC43IHDM2l14KaIuv9oNPPc8F5UKPo5EXleXj+wD82AxV7RpRm8IRVKPBfDzi3dkKqeNbe9oqphUFKT6/PPP8eijj+Lee+9t4Yf40k4UsHr44YfllRpKmtJ10XoeYZMQp2aFLnGMVBwnFeCnTDm2eof4vVgYUV1MVN1S0sTOuS+++AIfffQRLrroIiVDWvTR+mLZHBAHkAAJkAAJkAAJuIRAeznEM888I6uHNiaPjQ6JY6oi1xEVRtlaEmBOxieCBOwn0GJX1cDBSJw+i7uq7Mfp8pFyN2NaCsyHy+XcCffej+ATOy6643JHPXxCpYKPoxiEGFeQuxzGmj+OMRUQ2A+xiXdoVoQTDnckxK1fv17ehRsVFSXvom28okP8s7jiQlwptn//fnnFmJKmdF20nkfYJMSpWaFL3E8iqqLecMMNrR6HuP766/HGG2/I3XBiV1xHTdy9IiqsiouRxdGMo49jdDRefK71xVISA/uQAAmQAAmQAAm4nkBrOYS4H04UM1i9erWsjiqSzO7du8viBeIuN1GpVPyzKJ7w/vvv21XUwPWRumZG5mSu4cxZPI9A5S+bkP90s3vGMpbAEMp7xtxtpav37Ebugwsb7vcLCkZy+mL4xsa5Wxhu7a9SwUetIMWdcVUVv8BsroLBEIzg0BHwD+iulnnV7Hz66afyVGNjEwWpRI4jKsQ3NlGAStz339jETjjxhWRQUJAU5YSutG7dOjlOFN4UNQOUNqXrovU8wiYhTsBRq0KXSEovu+wyeTGwKMxwwQUXNLEXO9rEZ6L9/vvvOP744+U/b9y4EeJYh7h7pHk1L3E3yaRJk/Djjz/KnXBivD1N64tlT0wcQwIkQAIkQAIk4HwCreUQYkf/XXfdJfMYcZ9a81xHeCSS2fvvvx9//vknxK45cQqArYEAczI+CSRgO4G6/FxkpYvKmzWsvGk7Ps2NKP1sDYrfe0v65d+9B7rOmQ+9n5/m/PRUh5QKPp4af1txicJNkydPbjfsV155BeL+2eZNjBOV2/fs2QM/Pz+MGjVKVowXld9taUrXRet5hM1CnFoVuoT6KSpziW+ARRPFGcSONrEtURRcEG3RokWYPXt207o0Lnp8fDxEkQfxzbL4RllU6BJ+DRgwAF9//bXdd65ofbFseUDZlwRIgARIgARIwHUEWsshxLfDQmQTP3Fxre9kyM/Pl0Jdv379ZLVTtgYCzMn4JJCAbQQstUZkL5iHuuwsOTD6upsQfs5E24ywt6YIiL+X859chqpff5F+hf7w/yQAACAASURBVJ52BuJuuUNTPnqyM0oFH09moMXYlK6L1vMIm4U4sRhqVOgSdsS/XIRaKip17dixA5WVlVJcGzlypLw3TlT9at6Eevrkk0/KRFUcPxVHOoKDg3HCCSfgyiuvlHesiDPG9jatL5a9cXEcCZAACZAACZCAcwm0lkOEhobKIxirVq1qd3JxCkAc0aioaPuyZud6rz3rzMm0tyb0SLsExN9UBS88g4qN30snQ04eg7g7pvC4u3aXTLFn5upqZGfMRv2hfDkmdvJtCBs7TvF4drSfgFLBx/4ZONIeAkrXRet5hF1CnD3A3GGM1hfLHRjSRxIgARIgARLwRgJtCXHjx4/HypUr20Vy+eWXywJWFOL+w8SczBvfIsZsL4Hyb9aj8NUX5XDfhEQkpy2GPiDAXnMcpzECtVkH5W5Ha10ddD6+6Dp3PgJ69NSYl57njlLBx/Mi13ZEStdF63kEhbhmz5nWF0vbrwS9IwESIAESIAHvJdBaDiGu3di3bx/27t2L6OjoVuEUFRXJCmK9e/duuprDeylSiOPak4CtBIz79yF7URpgMkHn74/k1EXw65pkqxn21ziBwz9+J3c9iuYTHYPk9EwYQkI07rV7u6dU8HHvKN3Pe6XronVth0IchTj3e/voMQmQAAmQAAlojEBrCZ+4TmPq1KkYNGgQli1bhrPOOquF19988w2mT58ur+cQ1cTuuecejUXVee5oPYHuPDKcmQT+I2CurERW2iyYiovkL8Vx1NBRpxCRhxIoeO0lHP56nYwuaPBQJNx7P3R6vYdG2/lhKRV8Ot9T7/JA6bpoPY+gEEchzrveXEZLAiRAAiRAAk4g0FrCZzabZTX3zz//XN7VFBMTg+7du8vZRbGpwsJCeV/ueeedhzVr1kDPP6iaVkbrCbQTHiGaJAGbCFgtFuQ99hCqd2yT47qcNR4x199skw12di8C1vp6ZC9OR+3+fdLxyMuuQuRFl7lXEG7krVLBx41C8ghXla6L1vMICnEU4jzihWQQJEACJEACJNCZBNpK+CwWCx599FE88cQTstBU89atWzdZnGratGkU4Y5aPK0n0J35rHFuEhAEStasRsmqdyUM/169kZSSDp2vr1fAqag9iEOVm2GyVMNHH4S4kJEI9W/4ksPTW31xEbLSUmCprAB0OiTOSEHQwMGeHnanxKdU8OkU57x4UqXrovU8gkIchTgvfo0ZOgmQAAmQAAmoQ0BJwieEuNzcXDlhYmIikpOT1ZncA60o4emBYTMkElBEoPr3nch9aDFgtUIfEorkjEz4RrV+D6Uig27SqbouH7sKlqPM+McxHocH9MPA2DsQ5BfvJtHY72bVzu3IW7bE69bffmL2jVQq+NhnnaPsJaB0XbSeR1CIoxBn7zvAcSRAAiRAAiRAAkcIOJLwvfzyy8jOzkZqaip5qsCTEEnAkwmYSkuQlToL5orDckdUwvRZCB40xJNDlrFV1eVhc3Yq6i0Vbcbqqw/FyUkLvEKMK/loJUpWvy9ZeNuOSFc97EoFH1f5w3kaCChdF0fyMlewphBHIc4VzxnnIAESIAESIAGPJuBIwjd69Ghs3rwZ4k45tgYCjvAkQxLwVAJWkwk5S+bD+PdfMsSIiy9H1KVXemq4LeLanJ3e6k64o4MXO+NGJqV7PJOGOwIfRPWO32SsXcaNR8wNvCNQzYVXKvioOSdtdUxA6bpoPY+gEEchruOnnT1IgARIgARIgATaJeBIwkch7li0jvDko0oCnkqg8K1XUb72cxle4MDBSJw+yyuqZoo74X7KekDxso5OXuoVd8YdUzX3trsROuY0xZzYsX0CSgUfcnQtAaXrovU8gkIchTjXvjmcjQRIgARIgAQ8kIAjCR+FOApxHvhKMCSVCVRu3oT8Zx6TVn0io+S9cIbQMJVn0aa5v4vfxz+lKxU71zvyCogfb2jGA/8ge2EqYDJB5+eHpHkL4Z/czRtCd3qMSgUfpzvCCVoQULoujuRlrkBOIY5CnCueM85BAiRAAiRAAh5NwJGEj0IchTiPfjkYnMME6vJykZUxG1ajETAYkDQ7HQG9j3PYrrsY+KPwVfxb3rATUEnr1uVc9Iu5UUlXj+hTvuErFK54QcbiGxeP5PTF0AcGeURsnRmEUsGnM330xrmVrosjeZkruFKIoxDniueMc5AACZAACZCARxNwJOGjEEchzqNfDgbnEAFLrRHZ8+eiLidb2omedBPCz57okE13GmyyGLEt90GUGn9X7LY37YgTUKxWKwpefg4V32+QjIKHj0T8PdOg0+kUM2PHYwkoFXzIzrUElK6LI3mZKyKiEEchzhXPGecgARIgARIgAY8m4EjCRyGOQpxHvxwMzm4CUmB5/mlU/PSDtBFy8hjE3THFKwQWi9WM3MMbsK/kA9SaS21i6C13xDWHYqmrQ/aCeajLOih/HXX1dYg490KbuLFzSwJKBR9v47Z161asW7dOFpkSPzk5ORKB+PeVK5rSdXEkL3NFHBTiKMS54jnjHCRAAiRAAiTg0QQcSfgoxFGI8+iXg8HZTaD863UofO0lOd43sSuSUxdBHxBgtz13GCj+mC+s3oq9RW+jqr7hD3zRdPCBFaYOQ9DBgJOTFiIsoGeHfT2tQ31BPrLSZsNSUw3o9eg6cy4C+/X3tDBdFo9SwcdlDmlkoksuuQQfffTRMd5QiLNtgSjEUYiz7YlhbxIgARIgARIggWMINApxer3ebjpms9nusZ420BFh09NYMB7vJGDcvw/Zi9IaLuH390dy2mL4JXb1aBhlxr34q+hNlBn/aIrToAtEz4iLEB08DFtzFqLeUtEhA4MuACcmzEBU0KAO+3pah6ptW5D3+MMyLENYFyTPXwKf8AhPC9Ml8VCIax3z0qVLUVVVhREjRsifHj16oLa2ljvibHwqKcRRiLPxkWF3EiABEiABEiCBowk0Ckf23skjxlGI+48qhTi+Y95MwFxZgay0FJiKiySGuDv+h9BRYzwWSVVdHv4ufgeHqn5uilHsbEvucg56RV4GP0NDddjqunzsKljeQqhrHBAe0A+BPjHIq/xe/kqMHxR3D+JDR3sst7YCK/7gbZR+0rBjKaDv8eg6cx50Pj5ex8HRgF0txFXUHsShys0wWarhow9CXMhIhPp3dzQMp48PCAigEGcHZQpxFOLseGw4hARIgARIgARIoDkBCkfqPg/kqS5PWnMfAlaLBXmPPYjqHb9Jp7ucNQEx1092nwBs8LTWVI5/Slchu3w9rPhvR3BcyCgcF/l/CPKLb9WaECwKqn5BvbkKvoZgxAaPkIKFOBp3oGwN9ha/fWScDv2ib0S3cO8pbiECF89Q7sOLUfP7LskhfML5iL7mehtWhl0FAVcJcR0JzANj72jzXdDCSlGIs28VKMRRiLPvyeEoEiABEiABEiCBJgIUjtR9GMhTXZ605j4EStasQsmq96TD/r36IGl2usftZhKVUP8t+wz7S9fAbDU2LU5EwAk4LvpahAcc59CC5Rz+BrsLnheSlLTTK+JS9I68yiuKXDSCMx0ul7sqzaUl8lfxd9+LkBGjHOLqbYNdIcSJ3aCbs1PbPXLtqw/FyUkLNCvGUYiz782gEEchzr4nh6NIgARIgARIgAQoxDnpGaAQ5ySwNKtpAtW7d8qdTLBaoQ8JRXJGJnyjojXtsy3OtVUJNdgvCX2jrkF00DDVxLKCqi3Ykf84LNZ66WLXsHE4IeYW6HUGW1x26741f/+FnMwMwGyGLiCg4Z7BhES3jsmVzrtCiNucnd7qUeuj4xRHr0cmpbsyfMVzUYhTjKpFRwpxFOLse3I4igRIgARIgARIgEKck54BCnFOAkuzmiVgKinGv2mzYKmoAHQ6JM6YhaCBQzTrry2OtVUJ1d8Qgd6RVyIxbKxTBLLSmj+wLe8hmCxV0l1xhHVQ3BQY9H62uO/WfcvWfYGiN1fIGPwSk5CUutDjK++qtWDOFuLEEeufsh5Q7O7o5KWavDOOQpziJaQQ1xYqJn32PUQcRQIkQAIkQALeToA5hLpPAHmqy5PWtE3AajIhZ0kGjH/vlY5GXnKF/PGE1l4l1G7h58JHH+DUMCtqs/Br7mLUmkvlPGJn0dCE++Xdct7QhAh66LknUblpoww3ZNQYxN0+RbWdh57MsCMhbkf+k6iqz7UbQa2pFHXmMsXj/Qzh8PexvwJusG8iBsdPUTyf0o4U4pSSatmPO+Ka8WDSZ99DxFEkQAIkQAIk4O0EmEOo+wSQp7o8aU3bBArffBXl6z6XTopdcAnTH4BOr9e20x14p7QSqiuCrKkvxNbcTFQfEU1C/LphWOIsBPhEumL6Tp/DYjQie/5c1OVmS1+iJ92E8LO9q4CFPYvQkRD3U1YKKmr322O6U8aE+vfE6ORM1eemEGcfUgpxFOLse3I4igRIgARIgARIoIkAhSN1HwbyVJcnrWmXQMXmn3Domcelgz6RUfJeOENomHYd7sAzeyuhOjvgOvNh/Jq7FIdr98mpAnyiMTxxNoL9vOPOtLrcHGTNnwOr0QgYDOiakobAPn2djd2t7XckxHFHXMPyUoiz7zGnEEchzr4nh6NIgARIgARIgAQoxDnpGaAQ5ySwNKspAkeLI0lzMhDQq4+mfFTqTHuVUPtGX4cuAZ0fl/Bxe/6jKK7eLsMS1SjFzrguAb2VhunW/So3b0L+M4/JGAwRkeiWsQSGMPcVfZ29GB0JcY7Ozzvi7COodF20nkdQiKMQZ98bwFEkQAIkQAIkQAIU4pz0DGg9gXZS2DTrRQQstUeOC+Y0HhecjPCzJ7gdAVdWQlUDjsVqwu6C5cir+KFBkNL5Y0jCdEQHeUZhjI4YFb79Gsq//Ex2C+w/EIn3zXb7Y9AdxWzv50oFH3vti3Gsmmo7PaXrovU8gkIchTjbn36OIAESIAESIAESaEFA6wmfuy0XebrbitFfWwjIC/SffxqVPzWIQe54gX67lVCjrkRiqHMqodrCua2+VqsFfxW/iYNln8ouOhgwMO5OJISeqoZ5TduQhUEeXADjX39KPyMuuARRV/yfpn3uLOeUCj6O+Fddl4+fs+eh3lLRphmxc/PkpAUI8ot3ZCqnjeXRVPvQUoijEGffk8NRJEACJEACJEACTQQoHKn7MJCnujxpTVsEyr9ei8LXXpZO+SZ2RXLqIugDnFs9VE0C7VVC7R5+Hgx6fzWnc5qtA6UfS0GusR0ffQOE/57eTKUlyEpLgflwuQw1Yer9CB463NPDtjk+Vwhxwikhxu0qWI4y4x/H+Ciq/A6MvUNTItynn36KBQsWNPm6efNmCGH+5JNPbvrdvHnzcP7559vMXMkApeui9TyCQlyz1db6Yil5MNmHBEiABEiABEjA9QSYQ6jLnDzV5Ulr2iFg/GcfshenASYTdP7+SE5bDL/ErtpxsB1PtFQJVS1guYe/k0dVrbBIkz3DL0afqP+DTqdTawpN2qn543fkPLgQsFigDwySRUJ8Y+M06WtnOaVU8FHLP3FnXEHVL6g3V8HXEIzY4BEI9e+ulnnV7KxYsQKTJ09u194rr7yCm266SbU5mxtSui5azyMoxFGIc8oLQqMkQAIkQAIk4E0EtJ7wudtakKe7rRj9VULAXFkhdyKZiotk97g7/4fQk8coGdqpfRoqoa5EdvlXsMLc5EtcyCgcF/l/mtqtYw+owqptsoiDxVonhyeGnoH+sbdCrzPYY85txpR+9jGK32vYEejXrQeS5s6H3s/Pbfx3tqNKBR9n+0H7LQkoXRet5xEU4ijE8d0mARIgARIgARJwkIDWEz4Hw3P5cPJ0OXJO6GQCVosFeY89iOodv8mZupw9ETGTnLNjRK1QRJVRcY+aOMJpthqbzEYEnACtVEJVK1Zx3HZb7lLUWyqlyeigYRgSP9Vtjtnaw0EcJ8x/ahmqtv4ih4eedgbibrnDHlMeOUap4OORwWs4KKXrovU8gkIchTgNv2Z0jQRIgARIgATcg4DWEz73oPifl+TpbitGfzsiUPLRSpSsfl928+99HJJS0qDz8eloWKd87m6VUNWCVFmXg19zF8NoKpYmwwOOx9CE++FrCFFrCs3ZMVdXIztjNuoP5UvfYibfhi5jx2nOz85wSKng0xm+efOcStdF63kEhTgKcd78HjN2EiABEiABElCFgNYTPlWCdKER8nQhbE7ldALVu3Yg95FMwGqFPjQUyemZ8I2Kdvq8tk7gzpVQbY21rf7G+iJszVuCqrps2SXYLwnDE1MQ4BOl1hSas1Ob9S+yF8yFta4OOh9fdJ2bgYAevTTnp6sdUir4uNovb59P6bpoPY+gEEchztvfZcZPAiRAAiRAAg4T0HrC53CALjZAni4GzumcRqC+uAhZ6SmwVFQAOh0SZ6QgaOBgp81nr2FPqYRqb/zNx9WbK7Et70GUGf+SvxYi3LDE2Qjxc4+iGvYwqNj4PQ49/7Qc6hMdI8ViQ4jn7gRUwkip4KPEFvuoR0Dpumg9j6AQRyFOvbeClkiABEiABEjASwloPeFzt2UhT3dbMfrbGgGryYTszAzU7tsrP4689EpEXny5pmB5YiVUNQCbLbXYnv8Yiqq3SXO++hAMTXwA4QHHqWFekzYKXnsJh79eJ30LGjwUCffeD51er0lfXeGUUsHHFb5wjv8IKF0XrecRFOIoxPG9JgESIAESIAEScJCA1hM+B8Nz+XDydDlyTugEAoVvrkD5ui8ahI1BQ5Aw7QHNCBueXglVjeW0WE34veAF5FZ8K83pdf4YEn8vYoKHqmFeczas9fXIzkxH7T/7pG9aFI5dCU2p4ONKnzgXoHRdtJ5HUIijEMf3mQRIgARIgARIwEECWk/4HAzP5cPJ0+XIOaHKBCp+3ohDzz4hrfpERSM5Qxz1C1V5FtvNeVMlVNvpHDtC3Ju3t/htHChbIz/UwYABsbcjMex0NcxrzoY8Sp2WAktl41HqWQgaOERzfrrCIaWCjyt84Rz/EVC6LlrPIyjEUYjje00CJEACJEACJOAgAa0nfA6G5/Lh5Oly5JxQRQJ1uTnImj8HVqMRMBiQNCcDAb36qDiD7aYaKqF+g30lK1FrLm0yIIoR9I26BtFBw6DT6Ww37CUjDpZ9ij+LXm+Ktm/UJPSIuMAjo6/etR25jyxpKC4SEipFZC0WF3E2fKWCj7P9oP2WBJSui9bzCApxFOL4bpMACZAACZAACThIQOsJn4PhuXw4ebocOSdUiYDFaET2/Lmoy22ouhlz/c3octZ4lazbbua/Sqhvoao+t8mAvyECvaOuRGLoWOh1BtsNe+GIvIofsOvQs7DCLKPvHn4B+kZdC53O8+5RK/loJUpWvy/j9O/VG0kp6dD5+nrVqisVfLwKigaCVbouWs8jKMRRiNPA60QXSIAESIAESMC9CWg94XM3uuTpbitGfwUBIXodeu4pVG76UQIJGXUK4m6/p9N2mjVUQn0DZcY/mxbIoAtEz4iL0D38PBj0/lw4GwkUVW3H9vxlMFtr5ciE0NMxIPY26HU+NlrSdnerxYK8xx5C9Y6GYhVdxo1HzA03a9tplb1TKvioPC3NdUBA6bpoPY+gEEchji87CZAACZAACZCAgwS0nvA5GJ7Lh5Ony5FzQhUIlH+1FoWvvywt+SUmISltIfT+ASpYts0EK6HaxsvW3uXGv/Fr7lLUWyrk0OigoRgcPxU+etevta2+29LfXFmJrPQUmIoK5bC42+5G6JjTbDHh1n2VCj5uHaQbOq90XbSeR1CIoxDnhq8fXSYBEiABEiABbRHQesKnLVode0OeHTNiD20RMP7zN7IXpQFmM3QBAUhOXQS/xK4udZKVUF2Hu6ouF1tzF8NoKpKTdvE/DkMTZ8LP0PkFOdSkYDzwD3IWpsFqqofOzw9J8xbCP7mbmlNo1pZSwUezAXioY0rXRet5BIU4CnEe+ooyLBIgARIgARJwHQGtJ3yuI6HOTOSpDkdacQ0Bc2WFrDRpKm4QZeLumorQkaNdMzkAVkJ1GeoWExlNJfg1NxOVdVny98G+iRiWOBuBvtGd45CTZi3/9msUvvK8tO4bF4+ktMUwBAU5aTbtmFUq+GjHY+/wROm6aD2PoBBHIc473lhGSQIkQAIkQAJOJKD1hM+JoTvFNHk6BSuNOoGAvEvr0aWo3rldWu9yzkTEXHeTE2Y61mRjJdS/Sz5AnbmsqQMroboEv5yk3lyJbXkPNd3D52+IxPDEFIT4J7vOCSfPJO4+LHj5OVR8v0HOFDx8BOLvmd5pdx86Odwm80oFH1f5o7V5CgsLsXTpUnz88cf4999/ERgYiB49euCss87CQw895DR3la6L1vMICnEU4pz2ktAwCZAACZAACXgLAa0nfO62DuTpbivmvf62qC7Z+zgkpaRB5+Pci/tZCVVbz5vZUocdhx5HYdVW6ZiPPhhDE2YiIvB4bTnqgDeWujpkL0xF3b8HpJWoq65DxHkXOmBR+0OVCj7aj0R9D7du3YoJEyaguLgYAwYMwMCBA3H48GH8/vvvyM7OhslkUn/SIxaVrovW8wgKcRTinPaS0DAJkAAJkAAJeAsBrSd87rYO5OluK+ad/lbv2oHcRzJFuVToQ0ORnJ4J3yjnHktkJVRtPmtid+KewheRc/gb6aBe54vB8fciNni4Nh22w6v6gkPISp8NS3UVoNMhceZcBJ0wwA5L7jFEqeDjHtGo56XYCde/f39UV1fj7bffxkUXXdTC+ObNmzFy5Ej1JjzKktJ10XoeQSGOQpzTXhIaJgESIAESIAFvIaD1hM/d1oE83W3FvM/f+uIiWVHSUlHRIErMSEHQwMFOA8FKqE5Dq5phsVPx75L3sL90tbSpgx79Y29D17AzVJujsw1VbduKvMcbjh0awrogOSMTPhGRne2WU+ZXKvioNXlh7UHsq9yMOks1/PRB6BMyEtH+3dUyr5qdu+66C88++yyefvppiH92dVO6LlrPIyjEUYhz9bvD+UiABEiABEjA4whoPeFzN+Dk6W4r5l3+Wk0mZGdmoHbfXhl45KVXIvLiy50CgZVQnYLVqUb/LfsCfxS9CsAq5zku6hr0CL/IY+5UK/7gHZR+8qGMLaDv8eg6c57Tj2M7dcHaMK5U8HHUt7K6fKwvWI5c4x/HmEoM6IezY+9AuF+8o9OoMr6mpgZxcXGwWCwQO+PEvXCubkrXRet5BIU4CnGufnc4HwmQAAmQAAl4HIHOSvhEUpyZmYl33nlHXpYcGRmJiRMnYsGCBejatasizmVlZfjss8/khcubNm1CTk4O/P395dGTa6+9Vn7j7evr26ots9mMJ554Ai+//DJEchwSEoIzzzwTGRkZOOGEExTN31qnzuJpt8Mc6FUECt9cgfJ1X8iYgwYNQcK0B6DT61VlwEqoquJ0ubG8io3YdehpWGGWc3frci6Oj74eOp26z4nLAxPyosWC3IcXo+b3XXL68AnnI/qa6zvDFafOqVTwccSJsro8vJedCqOlok0zAfpQXJW0QBNi3Pfff4/TTz8dp556KsQ/f/7551i3bh2MRiP69u2Lq666ComJiY4g6XCs0nXReh5BIY5CXIcPOzuQAAmQAAmQAAm0T6AzEj6R+ArRS4hnCQkJOO2003DgwAGI+1liYmLk73v16tXh0s2dOxeLFi2SuzVOPPFEmUyLb7p//PFH1NbWyoT7yy+/RFBQUAtb4hvxK664AqtXr0Z4eLislFZUVITvvvtOfkv+zTff2H1PTGfw7BAUO5AAgIqfN+LQs09IFj5R0fJoniEkVDU2rISqGspON1RcvRO/5T0Cs9UofYkPOQUD4+6EXufcYh6uCNx8+DCy0mfBVFLSENtd9yJk5ChXTO2yOZQKPo449EF2eqs74Y62KXbGXZGU7shUqox97rnncMcdd+Cyyy6D+CLuo48+amFX/Lf/pZdewjXXXKPKfK0ZUbouWs8jKMQ1W12tL5bTnmYaJgESIAESIAEScIhAZ+QQjQLa6NGjsXbtWrkbTbRly5ZhxowZGDt2LDZs2NBhXGJHndgVd/fdd6Nbt25N/ffu3Yuzzz5b7rRLSUnB4sWLW9h68cUXceutt+K4446T34yL4yqirVy5Ugp0ffr0wZ49e+BjRwXJzuDZISh28HoCdbk5yJo/B1ajETAYkDQnAwG9+qjCRVZCrdqKvcVvoao+t8mmvyECvaOuRGLoWOh1BlXmohHXESg37sOveUtRbz4sJ40KHIwhCdPhow9wnRNOmsn4915kZ6YDZjN0AQFITl0Ev0RlO7Gd5JKqZpUKPvZOKu6EezvrAcXDr01e2ul3xi1ZskTmA+K/6waDQeYbV155pSzc8NRTT+Hhhx+WO+jFF4Liiz1nNKXrovU8gkJcs6dD64vljAeZNkmABEiABEiABBwn4Oocoq6uDrGxsSgvL4eYe+jQoS2CGDJkCHbs2IEtW7Zg+HD7q/aJimjieGqPHj2wf//+FnOIo6tCaBM74i655JIWn1188cVYs2YNPvjgA1x+ue13Z7map+NPAC14OgGL0Yjs+XNRl5stQ425/mZ0OWu8KmGzEqoqGDVrRBTa2Jq7GEZTofQxzL83hiU+AD9DmGZ9VupY2fovUPTGCtndLzEJSakLoQ9wf5FRxNOR4PNl/pMoaSaaK2XW2K/KVIpqc5niYUGGcAT7RCjuf3THSN9ETIifYvd4MVB8ITdnzhxpY+nSpZg5c2YLe+Jo6vvvvy/zhjfffNOhudoa3NG6NI7Teh5BIa7ZCmt9sZzyJNMoCZAACZAACZCAwwRcnUOIY5/jxo1D7969m/5YaB6EuCMuNTUVaWlpSE+3/zjL77//jgEDBsDPz08eU21sQpQTx17FMRQhBh59h9zrr7+OG264ATfeeCNWrGj4I82W5mqetvjGvt5HQOxWO/TcU6jc9KMMPmTUKYi7/R6HL9+vqsvF3uJ3UFC1uQmqDgYkdzkHvSIvsnSLWgAAIABJREFU8wihxvueltYjrjWVYmtuJirr/pUdgnwTMDwxBYG+sW6NqOHdeBKVmzYeeTfGIO72KQ6/G1qA0pHg83ZWCgprW35BpQW/2/Ihxr8nrknOdMhFcSfs1KlTpY2CggJ5DUbzJu6MO++88+QdtdnZDV9aqN06WpfG+bSeR1CIa/ZkaH2x1H6IaY8ESIAESIAESEAdAq7OIR577DFMmzZNHgl57733jgni008/xQUXXIBLL70Uq1atsjvITz75BBdeeKE8snrw4MEmOx9++KG0PWLECHkE5ei2e/duDBw4UO7UE2xsba7maat/7O9dBMq/WovC11+WQctdP2kLofe3f9dPrakM/5SuQnb5V02X+Qvb8SGj0SfqagT5aqNConetsvOjrTdX47e8h1Bq3CMnE8eOhyXOQqh/d+dP7sQZLLVHdovmNAgv0dfdhPBzJjpxRteY7kjw8cYdceJOOLEDXtwZW1VVdcxCiF3yYre8+HJO7Nx3RutoXRrn1HoeQSGu2dOh9cVyxoNMmyRAAiRAAiRAAo4TcHUOMX36dDz66KNSjBN3tBzdtm/fLu9nGTZsGLZu3Wp3gOeccw7Wr1+PKVOmyOqoja3xW/G2hD6xS04UcBBVXIuLi22e39U8bXaQA7yGgPGfv5G9KE2Ve7BYCdVrHps2AzVb6rDz0FNNuyB99EEYmnA/IgLtrzKtBap1ebnIypjddH9i15Q0BPbpqwXX7PZBqeBj7wTueEecuDO2e/fucsejqNouKqw3b6LIkyjwFBERgZIjhTzs5dPWOKXrovU8gkIchTi13w3aIwESIAESIAGvI+DqhO+2227DCy+8IO9qWbhw4TG8RaIqiiiIn7/++suu9Vi+fDnuvPNOKaiJHW6JiYlNdhrvibnuuuvwxhtvHGPfZDLJb8SVfCuel5cH8dO8iW/VJ02aJEVEISaykUBnEDBXViArLQWm4iI5fdxdUxE6crTNrrRfCfVaRAcN9YijfDaD8dIBVqsFewpfRvbh9ZKAXueLwXH/Q2zICLcmUvnLJuQ//ZiMwRARKSsK+4R1cduYlAo+jgToblVTRaziSz7xZZ+opj5+fMt7MhtzA3F1xldffeUImjbHKl0XV+dltgZLIa4ZMa0vlq2Ly/4kQAIkQAIkQAKuIeDqHMLZQpyogioqptbX18sqqGLnW/OmphAn7rDLyMhodaEoxLnm+eUsxxKwWizIe3Qpqndulx92OWciYq67ySZU7VVC7RN1layEqtPpbbLJzp5BQDwb+0o+wD+lK48EpEP/mFuR1GWcWwdY9PbrKPvyUxlD4AkDkHj/HOj07vmMKxV8HFmwsrp8vJc9D0ZLRZtmAvShuCppAcL9tHFk/a233oL4Em7QoEFSjEtISJC+//bbbzjrrLPkTjhxZYa4OsMZTem6uDovszVWCnHNiGl9sWxdXPYnARIgARIgARJwDQFX5xDOPJq6a9cunH766SgtLZXHUcWx1KObmkdTuSPONc8oZ7GNQMlHK1Gy+n05KKDPceg6Kw06Hx/FRlgJVTEqr+6YVb4WewpfAWCVHPpEXo2eEZe47Q5Jq8mEnAcXwPjXnzKeiAsuQdQV/+eWa6xU8HE0OCHGrS9YjlzjH8eYSgzoh7Nj79CMCNfo4E033YRXX31V7pgfM2aMPKa6ceNGWdTp1ltvxfPPP+8oljbHK10XV+dltgZMIY5CnK3PDPuTAAmQAAmQAAkcRcDVCZ+zijWIaqinnHKKPCoqdqqJqqutNRZr4CvgyQSqd21H7iNLAKsV+tBQdMtYAp/IKEUhsxKqIkzs1IxAfuUm7Mx/ClaY5G+Tu0xAv+gb3Xa3pKmsFFmps2A+XC7jSZh6H4KHnuR2a65U8FErsKLag9hX9QtqzVXwNwSjd/AIRGu0kIfY0fniiy/iueeeg7hKQtwZN3jwYNx+++2yWrozm9J1cXVeZmvMFOIoxNn6zLA/CZAACZAACZBAJwtx33zzDcQdLL1790ZjUtrcpQULFiA1NVUKaUJQU9KE+Hbaaadh3759mDp1KoTY11YTgl2vXr0QGBgIUZhB3AXXvL3++uu44YYbZEK+YsUKJdO36KP1BNrmgDjAbQjUFxfJe+EslRWATofEGSkIGji4Q/9ZCbVDROzQDoGS6t3YlvcwzNYa2SsuZBQGxd0t749zx1bz5x7kLF0AWCzQBwYhOWMxfGO1cbRSKU+lgo9Se+ynDgGl66L1PIJCXLPnQeuLpc6jSyskQAIkQAIkQAJqE3B1DlFXV4fY2Fgpgm3btk1enty8DRkyBDt27MCWLVswfPjwDsMVx1DHjh2LnTt3YvLkyXjppZc6PBrVv39/+U346tWrcckll7SY4+KLL8aaNWvwwQcf4PLLL+9w/qM7uJqnzQ5ygEcSEMfqsheno/afv2V8kZdeiciL239+WQnVIx+FTgnqcO1+/Jq7BHXmhp1kkYEDcWLCDPjoAzvFH0cnLf38YxS/+6Y045fcHUnzFkDv5+eoWZeNVyr4uMwhTiQJKF0XrecRFOKaPdBaXyy+eyRAAiRAAiRAAtok0Bk5xNy5c7Fo0SJ5P8vatWsRHBws4SxbtgwzZsyQwtqGDRuagD311FMQP6LwQmZmZtPvq6urZWGGn376CVdddRXERcwGg6FD0OJYirgLRlRm/eGHH6QwKNqqVauk+NanTx8p1PnYcK9W46SdwbPDgNnB4wkUvrEC5eu/kHEGDT4RCffObPOieVZC9fjHoVMCrK7Px9bcTNTUH5Lzh/r3xLCEWfD3cb/qo+L4Yv5Tj6Jq6+aGWE4di9hb7ujwS55OAd/KpEoFH6346y1+KF0XrecRFOIoxHnLO8s4SYAESIAESMBpBDoj4TMajTjjjDPw888/y6pl4ljpwYMH5f+PiYnBpk2b5PHRxtZYnfTo46LTpk2Tx1CF+Hb11Vcfc8y0cfzRR0wtFguuuOIKuSMuIiJCVksrKirCt99+i4CAAIjjsyeffLJdzDuDp12OcpDHEKjYtBGHlj8h4/GJikZyRiYMIaHHxMdKqB6z5JoNRBxz/jVvCSpqD0gfg3zjMSwxBUG+cZr1uS3HzNXVyM6YjfpD+bJLzE23ossZZ7lFHEoFH7cIxoOcVLouWs8jKMRRiPOg15KhkAAJkAAJkEDnEOishE9UKhO728QutqysLERGRmLixIkQd8QlJSW1gNGWENdY/awjckKAOLqZzWY8/vjjePnll+XdcmJX3plnnomMjAyIo6v2ts7iaa+/HOfeBOpyc5CVMRvW2lrAxwdJszMQ0Kv3MUGxEqp7r7M7eW+yVOO3vEdQUrNbuu1nCMewxFkI8+/hTmFIX2uzs5A9fy6sdUferzkZCOh57PultcCUCj5a89vT/VG6LlrPIyjEUYjz9HeV8ZEACZAACZCA0wloPeFzOgCVJyBPlYHSXJsELEYjsubPQX1ujuwTc8PN6DJufIv+rITKB6gzCFis9dh56Gkcqtwkpxd3xZ0Yfx8igwZ0hjsOzVmx8Qccev6phjjkjtMlMISEOGTT2YOVCj7O9oP2WxJQui5azyMoxFGI47tNAiRAAiRAAiTgIAGtJ3wOhufy4eTpcuReOaHY5XnouSdRuWmjjD9k9KmIu+3upjusWAnVKx8LTQVttVrwR9EKZJWvlX7p4IPB8VMQF2Lfsf/ODK7wtZdR/nVDHB3dwdiZfjbOrVTw0YKv3uSD0nXReh5hlxDXeAzinXfewb///tviGETXrl1teg7WrVsn7yXZvHkzysrKEBYWJqt73XnnnfIy4daamvM3t6/1xbIJLDuTAAmQAAmQAAm4jABzCHVRk6e6PGmtdQJl679E0RuvyA/9uiYhKXUh9P4BYCVUPjFaIiAE4/2lq/F3yXtH3NLhhJibkdzlHC252aEv1vp6ZGeKqsT7ZF8lVYk7NOrEDkoFHye6QNOtEFC6LlrPI2wW4sTFwOLuD3EBcOPFwAcOHJBCWmsXA7f39AgBTlwQrNPpMHr0aCQnJ8v7TUTVLvEvnNmzZ8tqYM2bmvMf7ZvWF4tvIgmQAAmQAAmQgDYJMIdQd13IU12etHYsAeO+vchenA6YzdAFBCA5bTF84uOQe/gb/F3yAerMZU2Dgv2S0DfqWkQHDXWbio9cc88jkF3+FX4vfBFAw32dvSOvQK+Iy93qmawvLkJWWgoslRWAToeE6bMQPGiIJhdLqeCjSec92Cml66L1PMJmIW7u3LlSHBPC2dq1axFy5Gz3smXLMGPGDIwdOxYbNmzocOkLCwul8CYqboldcWJcY/vuu+8wfvx41NXVQYBuXvFLrflbc1Dri9UhVHYgARIgARIgARLoFALMIdTFTp7q8qS1lgTMFYelGGAqKZYfxN01FTX9fbG3+C1U1ec2dfY3RKBP1FVIDB0LnU5PjCTQ6QQOVW7GzkNPQtwfJ1pS2Dk4IWayWz2f1bt2IPeRTMBqhT44RFYo9o2O6XS2RzugVPDRnOMe7pDSddF6HmGTECeEsdjYWJSXl0MENnTo0BbLPGTIEOzYsQNbtmyRx0vba5988gkuvPBCTJgwAV988cUxXS+++GKsWbMG7777Lq666ir5uZrzU4jz8DeU4ZEACZAACZCACwloPeFzIQpVpiJPVTDSSCsErBYL8pYtRfWu7fJTn0tHoWB4KcqMfzb1NugC0TPiYnQPPxcGvT85koCmCJTU7MFveQ9BVFYVLTZ4JAbF3QOD3k9TfrbnTMmaVShZ1XDU1r9nbyTNTofO11dT/isVfDTltBc4o3RdtJ5H2CTEffPNNxg3bhx69+4td6od3RYsWIDU1FSkpaUhPT293cdA7IITu946EuLWr1+Ps846S9pSc/7WnNP6YnnBe8UQSYAESIAESMAtCTCHUHfZyFNdnrT2H4GSDz+A+KmLBMovDEFFcmXThzoY5L1bvSIvg58hjNhIQLMEKmoPYmtuZtMR6ojA/rKiqq8hSLM+N3dMCuKPPYTqHdvkr8PGnYPYG27RlO9KBR9NOe0FzihdF63nETYJcY13ul155ZV4773GyyL/W+1PP/0UF1xwgSyysGrVqnYfA7GrrkePHqiqqmrzaGq3bt2wa9cu+Pk1qPtqzk8hzgveUoZIAiRAAiRAAi4ioPWEz0UYVJuGPFVDSUPNCFTt3I6s5zJRdipweBiAZqdN40NGo0/U1QjyjSczEnALAtX1Bfg1dzGq6/Olv6F+PTAscRb8fcLdwn9zZSWy0lNgKiqU/sbeehfCTjldM74rFXw047CXOKJ0XbSeR9gkxE2fPh2PPvqoLLAg7oQ7um3fvh0nnngihg0bhq1bt3b4KAix7tprr5VHTseMGYOkpCRkZ2dj48aNOOWUU/Daa6+hZ8+eTXbUnv9oB7W+WB0CZQcSIAESIAESIIFOIcAcQl3s5KkuT1oDaopysPvzFJQOq4O12WnTiIAT0Dd6EroE9CYmEnA7AnXmw/g1dwkO1/4jfQ/0icXwxNkI8nMPQdl4YD9yFqbCaqqHzs8PSfMWwD+5uybWQangowlnvcgJpeui9TzCJiHutttuwwsvvIA5c+Zg4cKFxyy3gHLcccfJn7/++kvR4yCOm4o74IqKipr6h4WF4b777sMDDzzQtBtOfKjm/Hl5eRA/zduePXswadIkKSIKMZGNBEiABEiABEiABJQQ0HrCpyQGLfUhTy2thnv7YrGakVO2Hn9lvwZzoLkpGFZCde91pff/ETBZarA9bxmKa3bKX/oawjA8YRbCAnq5BabD336Ngleeb/A9Lh5JaYthCOr8I7ZKBR+3gOxBTipdF63nEZ0qxD3yyCOYOXMmLrnkEnmnnKiO+s8//8h75j788EOcf/75EEUdGpuaQpyYLyMjo9VHkkKcB72pDIUESIAESIAEXEBA6wmfCxCoOgV5qorTK41ZrVYUVm09phKqT40v+iZPRtcuZ7hVpUmvXEQGrZiAxWrCrkPPIL9yoxxj0AXgxIQZiAoapNhGZ3Y89NJyVHy/QboQPGwE4qdMh06n60yXmu7E79OnT6f6obXJhVYi7vvfvHmz/MnJyZEuin/nttaysrLw8ccfy74///wz/vzzT9lXbMg644wzbA7PK4U4NY+GbtiwAWeeeabcefbLL79Ar//vkgaz2YyTTjoJv/32Gz777DOce+65coHUnJ874mx+5jmABEiABEiABEigDQIUjtR9NMhTXZ7eZq3MuBd/Fb3RohKqrhaI2haEgRc/BL/QKG9Dwni9gIDVasGfRa/h3/IvZLSi+IiophofOlrz0Vvq6pCzKBW1Bw9IX6Ouug4R513YqX4rFXw61clOmFxsovroo4+OmbktIa7xnv+jB1CIa4tYK4uqZrGEW265BS+//DJEpdW5c+ceM1tjBdZZs2YhMzNTfq7m/K09s0z6OuFN5pQkQAIkQAIk4AEEmEOou4jkqS5Pb7FWVZeLvcXvoKBq838hm4GwrUDETwZ0nz4fAT15F5y3PA/eGKf40/5A2RrsLX77SPg69Iu+Ed3CJ2oeR33BIWSlz4alugrQ6ZA4cy6CThjQaX5TiGsd/dKlS2XBzREjRsgfUYCztra2zR1xa9askbvfGvvfc889WLt2LXfEWW0Q4gTAcePGoXfv3k1bNZsvT6N4lpaWJo+attcmTJggF+CJJ57AlClTjukqfj916lTcfvvtWL58ufxczfkpxHXav9M4MQmQAAmQAAl4HAEKR+ouKXmqy9PTrdWayvBP6Spkl6+HFZamcEP/CUT45zXwLQVibrgFXcad4+koGB8JSAI5h7/B7gJx71rDccFeEZeid+RVnX7cs6PlqfptK/Iee0h2M4R1QXJGJnwiIjsa5pTPXS3EZdcexG+Vm1FjqUagPggnhoxEkr82Cle0BzggIKBdIe7osRMnTsSXX35JIc4WIU5UN42NjUV5eTm2bdsmK6Q2b0OGDMGOHTuwZcsWDB8+vN0X4sYbb5RVUW+44Qa8+uqrx/S9/vrr8cYbb8jdcGJXnGhqzt+ac0z6nPLvMBolARIgARIgAY8nwBxC3SUmT3V5eqo1k8WIg2Wf4kDpxzBbjU1hikqoUd/6wLKu4fL60NGnIva2uzUvQnjqOjGuziFQULUFO/Ifh8VaLx3oGjYOJ8TcAr3O0DkOKZy1eOW7KP14tewdcNzx6PrAPOh8fBSOVq+bq4S4grp8vFGwHH8b/zjG+T4B/TAp9g7EargKLoU4+545m4o1iCnEMdJFixZhzJgxckdbcHCwnHnZsmWYMWMGxo4dC3H/W2N76qmnIH4uvfTSpiOm4rPVq1fjsssug8FgkIUZLrjggqYx4syx+Ey033//Hccff3zTZ7bObwsWJn220GJfEiABEiABEiCBRgLMIdR9FshTXZ6eZk1WQj38DfaVfIA6c1lTeI2VUH1+OoTiNxq+6PfrmoSk1IXQ+wd4GgbGQwIdEiit+QPb8h6CyVIl+8YGj8CguCkw6P06HNtZHawWC3IfXoya33dJF7pMOA8x19zgcndcIcQV1OXhoexUVFkq2owvWB+K+5MWaFaMoxBn36NpsxBnNBpldQtR8SIhIQGnnXYaDh48KP9/TEwMNm3aJKufNrbG6qRiB9yKFSuafi9OxF599dV4//335e9EcYaePXti//79ckedaELwmz17dovIbJ3fFixM+myhxb4kQAIkQAIkQAKNBJhDqPsskKe6PD3FWluVUP0NEegTdRUSQ8eidt8+ZGemA2YzdAGBSE5bBL+ERE9BwDhIwGYCFbVZ+DV3MWrNpXJseEA/DE24H76Ghg01Wmzmw4eRlT4LppIS6V78XfciZOQol7rqCiFuWXZ6qzvhjg5U7IybntT+1V8uhdNsMgpx9pG3WYgT09TU1MjdbW+99RZEOdrIyEiIs77ijrikpKQWnrQlxIlO4j+mr7zyijyaKo60VlZWIjw8HCNHjpT3xgmbrTVb5rcFC5M+W2ixLwmQAAmQAAmQQCMB5hDqPgvkqS5PT7BWVvMX/ip+s0UlVB99IHqEX4zu4efCoPeHueIwstJSYCopbvjj/e57ETLCtX+8ewJrxuB5BGrqC7E1NxPV9bkyuBC/bhiWOAsBPp1z/5oSwsa/9zYT1QOQnLoIfoldlQxVpY+zhThxJ9zirAcU+zo7eakm74yjEKd4CVt0tEuIs28q7Y9i0qf9NaKHJEACJEACJKBFAswh1F0V8lSXpztba60Sqg4GJHcZj16Rl8LPECbDk8fZli1Bza4d8v93GX8uYq690Z1Dp+8koCqBOvNhbMt9EOW1f0u7AT7RGJ44G8F+2t0xWrb+SxS98Yr01zexqxTj9AGuOWbekRD3Sv6TyD8ibNqzUOWmUhxudrS+IxthhnB08YnoqFubn8f7JmJy/LFFMu02eGQghTj7CFKIa8aNSZ99DxFHkQAJkAAJkIC3E2AOoe4TQJ7q8nRHa21VQo0PGY0+UVcjyDe+RVjFq99H6UcrGwSGPn3RdVZqp1zw7o6s6bP3EBAFTnbkP4ai6t8axC19qNwZ1yWgtyYhiBN0h557CpWbfpT+hZw8BnF3THFJ4ZWOhLjMrBRk1e7XJLfWnEr274mU5EzV/aUQZx9SCnEU4ux7cjiKBEiABEiABEigiQCFI3UfBvJUl6c7WWuzEmpgf/SNuq5VwaBqx2/Ie3SpuPcGhtAwJM9fAp8I7R65c6f1oK+eR8BiNWF3wXLkVfwggzPo/DEkYTqig4ZoMlhLrRHZ8+eiLidb+hd93U0IP6f1K6zUDKAjIY474hpoU4iz76mjEEchzr4nh6NIgARIgARIgAQoxDnpGaAQ5ySwGjbbfiXU6/4/e28CHmV19v9/n2f2zEwm+0bCviNrQBRQcAfX4ta3tm+xv7YW+3/VVlsVRAiioLV1+VVb3tZfq+37Vtu6VWtBWgoqmxB2kC2BQFayziQzmX3mf50zWWZClpnJM5NnkvtcV64k5Jz73OdznglnvrnPfSMjaUa3UTDuhnqUr34SPpsVEATk/XQlkiZPlfFKyTUiMPAE/H4fz7l43vwJd4Zd974s+0HkGhcMvHPdeOCqrkL52qfgd9gBhYJHvOrGTYipr30Jcf2dnHLEbeNFQCNt4e6L3M8RJMSREBfps0/9iQARIAJEgAgQgS4E5H7gS7QNI56JtmPR+xuohFqMMw1vwxaUbym4EqogiN1O4He7eTJ359lS/vO0O7+OtNuXRu8MjSQCQ4xAWdPHXJBrbxMyvo0RKTfLkoJ135eoef1l7psiJTUQ+Zpsipmv4Qo+/XGAqqaSENef52fQjKVD36DZSloIESACRIAIEIG4EqAzhLS4iae0POVqLZxKqL35XvfH38GydQvvkjR9JnIf+SkEsXvRTq4MyC8iMNAEqpo/51dV/fBxV0al3IGx6f8Rlzxska69/p0/wrw5EMWnmzQFeT9ZCUGhiNRMWP3jIcTVumrwYsXTsPlaevRJLxrx0/x1yFKH5sUMaxFx6ERXU6ODTBFxQdzo0BfdQ0SjiAARIAJEgAgMdQJ0hpD2CSCe0vKUm7VwK6H25nfL7h08iTtryoxMFBRtgMJgkNtSyR8ikBAE6mwHcbjmZfj8Lu5vnnERJmd9H6IQG5ErWih+jweVP3sWjtMnuYnUW+9A+t3fiNZcr+PiIcQxB5gY9z+1G1HiCKwpuI3VTsS3spbLSoT75JNPsG7dug439+7dCxbZPHfu3I5/e/rpp3HLLbfw76urq7F0aWek8smTJ2GxWDBp0iQkJweqXrO+bEw4Ldx9kfs5goS4oN2W+2aF82BSHyJABIgAESACRCD+BOgMIS1z4iktT7lYi7QSak9+s6TtPF+Uywkolch/ai20o+RZ9VEu7MkPItAXAbPjDA5WvQC3z8q7ZiTNwvScR6AQNX0NjevPPeYmlK9ZAa/FzOfNfeQn0M+cLbkP4Qo+Uk3McsYdtu1Dq9eGJIUe0/VzkK8ZIZV5yey8+eab+M53vtOrvd///ve4//77eZ+ysjKMGjWq1/7Lli0DsxtOC3df5H6OICEuaLflvlnhPJjUhwgQASJABIgAEYg/ATpDSMuceErLc6CtRVMJtSeffXY7yp95Cu7qKt4lc9n3YLrm+oFeIs1PBAYFAaurEgeq1sPhaeDrSdFOwMzcn0KlkFe0qf3UCVS+sA7w+SDqkpBftB7qbGmvboYr+AyKjU+gRYS7L3I/R5AQR0JcAr3syFUiQASIABEgAvIkIPcDnzyp9ewV8Uy0Heve385KqH+Fy2vp6KRX52N8es+VUHtaPbv+dPHX/xfWvbt5F+O8q5D1/R/KMpfV4NhBea7CefE8rKf3wudohahNgmHC5dBkyS9ySJ70+vbK4a7H/urnYXNV8M7s9VqYtwJaZXrfg+PYo2nT39Hw5//hM6oLhiN/1TqIGumi98IVfOK4ZJoKQLj7IvdzBAlxJMTRC5oIEAEiQASIABHoJwG5H/j6uby4DyeecUcu6YTtlVBPN7yN1pBKqGkYm34P8owL0VMl1N4cMf9zM+r/N3B9SZ1fgPynn5X0jbekEMiY5ARcjTWo/cdGOMovzaWlLZiIrJuXQ50mbVSU5ItIEINurxUHq38Gs+M095iJcLPyVsKgHiabFbDfMzWvvQzb/r3cJ+P8q5H1vQclE+bDFXxkA2SIOBLuvsj9HEFCHAlxQ+QlS8skAkSACBABIhA7AnI/8MVu5bGxTDxjwzUeVvtbCbUnHx0lZ1CxoQjweiFodSgoeg7qnLx4LInmkAEBV2M1Kv6wGj57z9UlRZ0R+d9eR2KcRPvl9TlxuOYV1Lce5BZVogEz855AinacRDP034zP3oryopVwX6zhxjLv/z5Mi67rv+EIIq8kmYyMhE2AhLiwUSVORzr0Jc5ekadEgAgQASJABOREgM4Q0u4G8ZSWZzysSVEJtSc/vc3NKC96Ep7GRt4l5//7EQxzrojHsmgOmRCo+J+ibiOj/gE+AAAgAElEQVThurrHIuPyv1UkE68T3w2f34Ovan+LqpbP+GJEQYPpOT9Cpn6mbBbnrChHxTOrJC/eEq7gIxsQQ8SRcPdF7ucIiogLemDlvllD5LVFyyQCRIAIEAEikHAE6Awh7ZYRT2l5xtIaq4Ra2vgeKpu3wg9fx1Q5hisxNv3rSFL176qg3+dD1S82wH78KLdtuulmZH7j27FcEtmWGQGWE678d0+E7VXBd1+gnHFh0+q7I7sCeqbhbZSZP+KdBSgwJesHyEu+uu/BcerRsmsHLv7mNT6bMj0DBWs3QGEw9mv2cAWffk1CgyMmEO6+yP0cQUIcCXERP/w0gAgQASJABIgAEQglIPcDX6LtF/GU/44FKqH+HWVNf4fX7+hwOFU3mRdiMGnHSLKIhg/+iqa/vcdtacdNwLAnnoagVEpim4wkBoGGL/6Kph2BZyCclrbgbqRddXc4XalPBATOmz/Bqfo/dowYn/4tjEy9NQILse1a98ffwbJ1C58kaep05P74CQiiGPWk4Qo+UU9AA6MiEO6+yP0cQUJc0PbLfbOielJpEBEgAkSACBABIhBzAnSGkBYx8ZSWp5TWpK6E2ptvtiOHUP3yC4DfD0WyiUe5KFPTpFwO2UoAAjV/+yWsX+0M21PT7CXIvGFZ2P2pY/gEqlt24NjFX8MPLx80IuVWjE+/L6riK+HPGl5Pv9uNig1r4TxbwgekLb0HaXfcFd7gbnqFK/hEPQENjIpAuPsi93MECXEkxEX1AqBBRIAIEAEiQASIQCcBuR/4Em2viKf8dqz3Sqj3Is94taRvxt31dShfswI+mxUQBOT99CkkTb5MfmDIo5gQYFeSbWeKYd63CY7yExHNockZjZw7H4XKlBHROOocHoF622EcrnkJXr+TD8g1Xo0pWQ9AFAY+UtXdUI/yohXwtbTw3xu5jz4J/dTp4S2sS6/S0lJ4vV6MHz8+qvE0SHoC/Jr0mTNQKpUYPXp0rxPI/RxBQhwJcdK/QsgiESACRIAIEIEhRkDuB75E2w7iKa8di1Ul1J5WySNb1hfBea6Ud0m76+tIu22pvKCQNzEh4LVb0XxkGyz7P4XHUh/9HKICydMWIvXKr0GVkhW9HRrZLQGLowQHql6A2xeoYpuRNBPTch6BUtQOOLHW40dR9fP1PJJW1Bt4JK0qIzNivyoqKtDS0sIFH41GE/F4GiA9AZvNhgsXLsBkMiEvr/eq2XI/R5AQR0Kc9K8QskgEiAARIAJEYIgRkPuBL9G2g3jKY8diWQm1txXW/eF3sPy7LdfT9FnIfeQn/cr1JA+a5EVvBFwNlTDv24yWY5/D7w5EWvEmiDBMvALs567a831CFFSaS8Ybp17NBTl1Wv8Kh/Q5+RDrwH4/7K9aD4cnIJiaNOMwM+9xqBX9K5IgBcbGjz5A4/t/5qY0o0Yjf+VaCCpVRKabm5tRWVkJrVbLRR8S4yLCJ2lnFplotVpx8eJFHqU4cuRI6HS6XueQ+zmChLig7ZP7Zkn6NJMxIkAEiAARIAJEQDICdIaQDCU3RDyl5RmptZ4roc5rq4SaHanJsPu37N6Bi//dVv0wIzNQ/VBvCHs8dUwcAn6/D61nD8OybzNazx0OcVzUGWGacS1Ms26EMjkdrsYaVPzhafjsgQis7hobk//ttXDVVaBx5/twXSzr7CYIME5ZgNR5S6FO7z2SJnEIDrynDk8jDlRtgNVVzp3Rq/IwK28ldKqBvRbMrjZXv/oiWg8f5H4lX3M9spZ9LyJg7BpkTU0NzGYzH6dQKCD2o/hDRJNT5w4CbB88Hg//XhAE5OTkICUlpU9Ccj9HkBAXtIVy36w+nzbqQASIABEgAkSACAwIATpDSIudeErLM1xr8aqE2pM/zspyVKxdBb/LCSiVyF/1DLQje88DFO7aqJ98CPhcDrQc/Rzm4s1wN1aFOKbOLAArtsBEM1GlDvkZE+Nq/7ERjvKTlyxGWzARWTcv74h6Y2/eW0sOoHHHe3DWnA3qL8Aw+Uqkzb8T6ox8+UBJYE/cXisOVr8Is+MUX4VGkYbCvBUwaAoGdFVem5XnmfTU13E/sr7/QyTPvzoin9hzxK6nsug4p9MJ9j21+BJg4hvLCafX62E0GsOOTJT7OYKEOBLi4vtKotmIABEgAkSACAxCAnI/8CUacuIZ3x2LZyXUnlbms9tRvvYpuGsCwkzm/d+DadH18QVBs8WUgNtcy3O/NR/eBp+zNUQc04+bxQU43YgpPOqlt+asPQ/bqX3wOmxQaPXQT5gDTdaIbodwQe7soYAgVxWophloAgwT5yJ1/tIex8YUxiAz7vW5cOTiq6iz7ecrU4p6zMx9HKm6CQO6UkfZOVQ+uxp+jxuCWo38p9dBU9D9szKgjtLkkhOQ+zmChLigLZf7Zkn+dJJBIkAEiAARIAJEQBICdIaQBGOHEeIpLc+erMW7Empvflz89auw7t3DuxjnX42s7z3YpyATH0o0S38IsGeMVT1l1U9ZFVSWQL+9CWodkqcvQkrhTVClxjZ/G/PDXnaUC3KOikDkVnvTj5/DI+Q0OaP6s9QhP5YJ+ifq3kBl8zbOQhRUmJbzI2TpCweUTfPn21D7u//mPqiyspG/Zj0Uev2A+kSTx56A3M8RJMQFPQNy36zYP640AxEgAkSACBABIhANATpDREOt5zHEU1qe3Vkz20/hdMP/wuw43fFjpajDyJQ7MCJlCRRi/KoEmv+5CfX/+xb3Q50/nEetiFSlMPYPQQxn8HlcsH61iwtwXYssMNHNNHsxkqcuhKjpPeG61C5yQe7CV2ja8R7/HNySxhZyQU6bN0bqaYeMPca3pPEvONf0AV+zABGTsx7AsORFA8qACXFMkGNNP3M2ch5+jIT+Ad2R2E8u93MECXEkxMX+VUAzEAEiQASIABEY5ATkfuBLNPzEM3Y7NlCVUHtakb3kNCo3rAW8XghaHQqKnoM6h5Lpx+4JiK1lT0sjLAf+ieZDW+FtbQ6ZTDdyKlLmLEHSmBkQBDG2joRh3X7hBC/qwCLlQgS50dOROv8u6PLHh2GFunRH4IJ5M07WM3E9EAE5Lv0bGJly+4CJXz6XC5XPrYbzfKCAR/q99yH15ttp8wYxAbmfI0iIIyFuEL/8aGlEgAgQASJABOJDQO4HvvhQkG4W4ikdy3ZLA1kJtafVeJubUV70JDyNjbxLzn89CsPsy6VfPFmMOQFHVQmPfrOe3AP4vB3zCUo1jJddxSPgNJkDm7y/Jwj2itNo2vk+zyUX3JhwmLbgTugKJsWc32CcoLplF45dfB1+BJ6H4aYlmJDxnwMmwrprL6K8aCV8rTZWfhN5j69C0qQpgxE9rSkBqq+TEEdCHL1QiQARIAJEgAgQgX4SIOGonwC7DCee0vHsrIT6Mbx+Z4fhVN1kjE//JkzagbmG5/f5UPWLDbAfD0Qjpdx0CzK+8Z/SLZwsxZyA3+uB9dReWIo3wVF5JmQ+ZXI6TLNuRPKM66DQGWLuixQTOKpKeYRca0mg4EB70w2fjNQFd4F97quQhBR+DCYbDa1Hcaj6F/D6HXxZOYb5uCz7QYiCckCWaTt0ANWv/IzPrUg2oWDtBihT0wbEF5o0tgTkfo4gIS5o/+W+WbF9VMk6ESACRIAIEAEiEC0BOkNES677ccSz/zx9fg9Pml7a+C5cXkuHQb06nwtwGUnsemDv1Sn770XPFhre/wuaPnqfd9COn4Bhjz8NQTkwb85juc7BaNvb2gLLoa2wHNgCb0sgmrG9afMnIGX2El7JVBAVCbl8R805HiFnO73vkrWlMUFu5NQBfe0kGlSLoxQHql+A2xu4qpyum4bpuY9CKWoHZCkN7/0ZTR8Hcthpx47HsCdX0++eAdmJ2E4q93MECXFB+y/3zYrto0rWiQARIAJEgAgQgWgJ0BkiWnLdjyOe0fOUSyXU3lZgO3IQ1S+9wLtQVEr0ex3vkc66clj2bULL8S/g97g7pxcVME66EqY5S6DNHZgIy1iwcNaeR9POD2A9+WVHrjM2jyZvHL+ymjR6YMXsWKw5VjZtrmocqNoAu6eWT5GsGYNZeU9ArUiO1ZQ92u0ajWu6cQky71sWdz9owtgSkPs5goQ4EuJi+wog60SACBABIkAEhgABuR/4Em0LiGd0OyanSqg9rcBdX4fyNSvgs1kpT1N02xzXUX6/D60lB2Eu3gR72bGQuRVJJiTPvB6mWddDaUiNq1/xnIwJkE27PoD1q92hglzOaLAIuaSxsyhCLowNcXqacKDqebS4zvPeSapcFOatgE6VFcZoabt0zU+Z/cNHYLz8SmknIWsDSkDu5wgS4kiIG9AXCE1OBIgAESACRGAwEJD7gS/RGBPPyHZMbpVQe/Le73ajYv0aOM+d5V3S7v4PpN36tcgWS73jQsDnbEXzke2wFH8Kt/liyJya7JEwzV4C4+R5EJSquPgjh0lcDZVo2vUhWo7vAPyBaqCsqbNHIm3+ndCPnz1ghQjkwCccH9zeVhyqfhFNjhO8u0aRill5T8KoGRHOcEn7OErPoGJ9UVvFZi0KVj8Hdd4wSecgYwNHQO7nCBLigp4NuW/WwD3GNDMRIAJEgAgQASLQGwE6Q0j7fBDP8HjKsRJqb57X/uH/ofnf/+RdkqbPQu4jP4EgiuEtlnrFhYCrsQaW/ZvRfOQz+F32zjkFAfrxc5DCrp/mTxzSEWCMUdPuD9Fy9HPA7+tgpM4cjtT5S2GYMJee616eVq/PhaMXX0OtbS/vpRSTMDP3p0jVxb86rWXrFtT98XfcD1XeMC7GidqByV0Xlxf4EJpE7ucIEuJIiBtCL0daKhEgAkSACBCB2BCQ+4EvNquOnVXi2TtbuVZC7c3rll07cPE3rwXeeGdmoaBoPRT6xKimGbsnXR6WWV5Bdu2UXT9l11CBzmgvUatH8vRrYSq8ESpTpjwclokXbnMtF+SYaAmft8MrVfowpDFBbtI8EuR62Ct25flE3e9Q0fwv3kMUVJiW/TCyDHPiurvs2b/4m9dh3b2Dz2u4/EpkP/jwkBaa47EBTsd5WFv2wudthahIgsF4OTRaaaMi5X6OICGOhLh4vNZoDiJABIgAESACg5qA3A98iQafeHa/Y3KvhNrTc+asLEfF2lXwu5z8KuOwVc9AO3JUoj2Wg85fn9uJlmNfwFK8Ga76ipD1qdLzkDJ7MYyXXQ1RTRFCvW2+21IP856PYDn8b8Dr6RTk0nKROm8pjFPmJ2wF2Vg+9EwEY1Wdzza91zaNgMmZ30e+6dpYTnuJbZ/TgYpnVsFVGXgNZHxzGVJuWBJXH4bKZC5XDWqrNsJhP3nJkrW6icjKWw61OkcSHHI/R5AQR0KcJA86GSECRIAIEAEiMJQJyP3Al2h7QzxDdywRKqH29Iz57HaUr30K7poq3iXz/u/DtOi6RHskB5W/7uZ6WA78E80Ht8LnsIasjVUCZdVPk0ZNpXxnEe66p6URTXs+QvOhrSFVZVUp2Uid9zUYL7sKgkIZodXB373csgUn6n7fEYk5Nu3rGJX6tbhGpblqqlBe9BT8DjugUGDYk6uhGzdh8MOP4wpdrmpUlK2Gz9vS46yiwoj8keskEePkfo4gIY6EuDi+/GgqIkAEiAARIAKDk8BAHfjsdjs2bNiAd955BxcuXEBaWhoWL16MdevWYdiw8JNOf/bZZ9i+fTv27t3LP+rr6zFixAiUlZX1uGH3338/3nrrrR5//utf/xrLly+PasMHimdUzsZ4UE+VUNkb1eGmxVCImhh7EL15fu3rV6/Cum8PN2KcfzWyvvdgXN9gR+/94BrJ9sJReRqW4k2wntwbkttMUGmQPHUhTLMXQ52eN7gWPgCr8VibYP7y71zs9HtcHR4oTZlIvfIOJE9bRIJcl32pse7B0ZrX4EcgorDAdBMmZiyLqxhsLd6Lmtde4vMrUlJRsHYDlKaUAXiCBueUFWVF3UbCdV0ti4zLH1nUbwhyP0eQEEdCXL8fcjJABIgAESACRGCoExiIA5/D4cA111yDPXv2IDc3F1dddRUXzpiQlpmZyf999OjRYW3NjBkzcPjw4ZC+4QpxN910E3JyLr1KsmzZMu5fNG0geEbjZyzHBCqhvo1a276OaQQoUGC6EaPTlkKtSI7l9JLYNm/5B+r/9AduS10wHPmr1kHUyFc4lGTRMjPi93pgPbEb5n2b4KwJVKttb0wYYuIbE4YUWr3MPE98dzw2C8x7/w7L/i3wu52d3JPTkXrFHTBOXwRRqU78hUq0gsbW4zhY/XN4/YEiIdmGKzA1+//j+ePi1erf+R+YN/+dT6ebNAV5P1kJQaGI1/SDdh6WE6783BNhr69g1Av9zhkn93MECXFBj4PcNyvsJ5c6EgEiQASIABEgAnElMBBniFWrVuG5557DlVdeiS1btsBgCCS+f+mll/DYY49h4cKFPMotnPb444/DZDJhzpw5yM/Px5QpU8KOiNu2bRsWLVoUzjRh9xkInmE7F+OOiVYJtScc9jOnUPn8M4DXC1GnQ/6a9VDn5MaYHplvJ8BEoOaD/+JRWV6bOQSMbvhkfv1UP7aQignE4ZHxtjbDvO8fMBd/GlKJVmFIReoVtyN5xnUQVSTIsa1odp7Dgarn4fJa+M6k6S7DjNzHoBR1cdgpwO/1ovJnz8Jx6gSfL+WWO5BxzzfiMvdgnqSh7q9oqm/PBdj3StMy7kZa5t19d+ylh9zPESTEBW2e3DerX08iDSYCRIAIEAEiQARiRiDeZwiXy4WsrCxYLBawuWfOnBmytunTp+PIkSMoLi5GYWFhROuuqanhEXbhRsSREBcR3h47J2Il1J4W42m2oHzNCnibGnmXnP96FIbZl0sDiqz0SsBZc45XP235aldI4QBBoYJhynykzF4CTba01QlpS8Ij4LVbeWQiux7sc7Z2DFLoTUi54naYmCBHhTHQ6q7B/qoNsLsvckZGzSjMyn0SGqUpPND97OUxNwV+f1kCAnbOwz+BYdbsflodesM9Hguc9hI4HGfRYv4MHk992BBMqUuQmbMs7P7ddYz3uSxSZ0mICyIm982KdHOpPxEgAkSACBABIhAfAvE+QzDx69prr8WYMWNQUlJyySJZjrjVq1djzZo1KCqKLNcKCXHxeWbaZ+mpEqpBXYBx6fchI2lGQuVU8/t8qPr5eti/OsaXmLL4FmT8x3/GF+oQm83v88J2upgLcI7y0GqELOrKNOsGmGZeD0WS/K8zD4Wt8zpsvFIti5LzOWydglxSMlLm3grTrBuHvCDHIoMPVD+PFmcgT2mSKgez8lYgSZUdl0fEfuoEKl9YB/h8EHVJyC9aD3W2NNU847KAOE/i8zngtJ+Fw1EKp70UDkcJPO7whbeu7lJEXJw3cKCni/cheqDXS/MTASJABIgAESAC0hCI9xnilVdewY9//GPcc889+Mtf/nLJIj755BPceuutWLp0Kd5///2IFhmpEPfQQw+BJYL3er0YNWoUbrvtNkycODGiObt2jjfPfjkb5WDGjOV/O9PwDlrdgYqirGkUaRibfi/yjFfHNVF5lMu4ZFjDe39G08cf8H/Xjp+AYY8/DUFJlSKl4htsh0VYNR/ZBsv+T+GxhL7p1eSOQcqcJTBMvIIKA8QCvgQ2WVScef8WmPd+Ap+9s5KkqDMi5fKbkVJ4E0RNkgQzJaYJj68Vh6p/gUb7cb4AtSIFs/KeRLJmZFwW1LT5EzS888fA3JTjsoO53++B01EOp6MEDnspnI5SuJwVHVVvu9scQdDA7+/Mk9jXBlKOuL4IDbKfD4VD3yDbMloOESACRIAIEAFZEIj3GeLRRx/Fyy+/zMU4lhOua2OFF1gBhlmzZmH//v0RMYpUiOtqXBAEPPjgg3j11VehjFKAiTfPiABJ0DmRK6H2tnzb4YOofvkF3kWRbApUHUxNk4AYmQgm4GqohHnfZrQc+zykCABEBQwT5nIBTjtsHEFLEAI+l4Pn8jN/+TFYPrn2Jmr1/Coxy+c3VItp+PxuHL34Oi5aA5WXWa64GTk/QVrSlJjvLvtjSc3rL8NWvJfPNRSrPjMGbndNIMrNXsJFN6ejDH6/u2f+ghIazQhodWOh0Y2BVjsGKnUuKs8/Q1VTg6jR1dQgGIP90Bfz31Y0AREgAkSACBCBIUog3meIBx54AL/97W/x1FNP4dlnn72EOruuOm7cOP5x+vTpiHYlXCGOCW1arZZfkWUFHti4TZs2gRWRaGpqwo9+9CMuFvbVqqurwT6C24kTJ/Ctb32Li4hMTBwsbTBUQu1pL9x1tSgvWgGfzQYIAvIeX4WkSbF/szxYno2+1uH3+9B69jAs+zaj9VxohWMWQWWaeR2/0qg0kvDZF0u5/tzndvICG017Pg4psCFqdDAVLkbKnJuhSDLK1f2Y+cWe/ZP1b6LcsoXPIUCJaTkPIdswN2Zzthv22VtRvvYpuGsC/0dl3v89mBZdH/N5B2oCj7uJi20s0o1dL2XXTX2+zuvTl/olQKXOg5YLbgHhTaMZDkG8tNKty1WDirKn4fN2Rn92tScqjMgfuQ5qdf+vAcf7XBbpnpEQF0RM7psV6eZSfyJABIgAESACRCA+BOJ9hpCDENcT2ePHj3PxzOfz4ezZsygoKOh1E1gOu7Vr13bbZ7AIcYFKqO+isvnf8MPXsdYcwzyMTf963PIexerV4He7UbF+DZznzvIp0u/+BlJvvSNW0w0puyxaqvnoZ7AUfwp3Y+cVZgZBnTkcKXMWwzB5AVXdHERPhc/tQvPhf6Npz0fwtgQKnrAmqLUwFd6E1MtvGXL5/lhk1rmmD1DS2J6KQcCkzP+DAtMNMd95Z0U5Kp5ZBb/LCSiVyH9qLbSjxsR83lhP4PO2wuE411ZQgeV2K4HH0/m8dTe/UpkGjW4sj3ILfB4FURH+9WkmxtVWbew2Mk6rm4isvOWSiHDM93ifyyLdLxLigojJfbMi3VzqTwSIABEgAkSACMSHQLzPEHK4mtobWZa77t1338Xvf/973H///b1uwmCOiOu5EuoUjE+/DyZt4r+ZY5tb+9YbaN72L77PSTMKkfvwYxBEMT4vvkE6i9tcy3O/NR/eFlJhk8UD6cfNgmnOzdANn5xQhTwG6VbFbFl+jxvNR7ajafff4GnuzAEoqDS8AAcr7KDUp8RsfjkarrBsxVd1b3TkIxuTdjdGp94V89dBy+4duPjfr3EkyvQMfu1eYUic6MRAXrfzHYUUWMSb28WEfX+P2yyKemh0o3mkG4t402jHQKlKleSxYL7YWvbB67VBodBDb5wDjVbaas7xPpdFCoaEOBLiIn1mqD8RIAJEgAgQASLQhUC8D3xyKNbQ20OwcuVKbNiwAevXr8eKFSsifl7izTNiB/sYMNgqofa23JZdX+Dib14PvEHNzEJB0Xoo9AapkQ4Jeyzqx1F+AuZ9m2A7Uwz4O98ks+uJxmmLeAJ/VWr/r20NCaCDZJF+rwfNRz9H0+4P4THXdqxKUKqRPPM6pM69bUhdSb5o3YujF38Jlj+OtfzkGzAp8zsxL25T98ffw7L1Uz5n0mXTkfvoE7L8gwO7yut2VXdWMGW53ZznAb+nx1eEIKig0Y7kYlt7bjeVKifmAmcsX6JyP0eQEEdCXCyff7JNBIgAESACRGBIEIj3gW/btm08N9uYMWPA8sF1bevWrcPq1auxZs0asKufkbRwc8T1ZpMVa9i4cSMv2PDwww9HMj3vG2+eETvYw4DBWgm1Jz6BK1tPwe9yQVCqkP/0M9CMGCUVziFjx+dxwfrVLi7AuWrPh6ybiW6m2YuRPHUhmBhHbegSYIJcy/EdaNr1IdxNNR0gBIUKyTOuReoVt0OZnD4kADXaT+BQ9YtglVVZy9JfjqnZ/wWFqI7Z+v0eDyrWF8F5NvB/btrX7uYfA9087saOQgrtVUx9PnsvbglQa/LbRDcW6TYWGm0BBGFwVbeW+zmChLigR1TumzXQL3KanwgQASJABIgAEeieQLzPEC6XC1lZWbBYLDh48CCvkBrcpk+fjiNHjqC4uBiFhYURbVt/hTin08mLRJSXl+OLL77AggULIpqfdY43z4gd7GbAYK2E2hObS5KYf+cBmBZeKwXKIWPD09LIq2U2H9oaUi2TAdCNnMqrnyaNmRHzSJ8hA3yQLNTv83LhtnHnB6F5AxVKJE9bhNQr74DKlDlIVtvzMlqc57G/agNcXjPvlKqbzCuqqiLIWRYpJHdDfaAoTUsLL0qT++MnoJ8W+v9vpDYj6c+ucjodZwPCm/0sL6jg9TT1akKpyugopMBzu/G8boNf1Jf7OYKEOBLiInntU18iQASIABEgAkSgGwIDceBj1Umfe+45zJs3D1u2bIFer+eevfTSS3jsscewcOFCbN++vcPb1157Dexj6dKl/NpoTy0cIe7kyZPYt28f7r33Xmg0mg5TdXV1YIUkPvzwQzAxkImEgiBE/MwMBM+InWwbMJgrofbEhEX+1bz+CmzFX/IuxgULkfXd5VHtdbTcE3mco6qER79ZT+4BfN6OpbCrhsapVyNl9mKoM/ITeYnkexwI+H0+/gw17XwfrvqKzhlFBY+g5IJcanYcPBm4KVrdtThQtR6t7kCEoFE9ArPynoRGKU0us+5W1nr8KKp+vp5fHRf1Bp4vTpUhvfDp87ngcp7nFUxZIQWH42xbXreeeYsKQ1AhhdG8iqlSObTyCLbTkfs5goS4oOdY7ps1cL/iaGYiQASIABEgAkSgNwIDcYZwOBxYtGgRvvzyS+Tm5uKqq67C+fPn+feZmZnYs2cPRo8e3eF2e3XSZcuW4c033wxZzhtvvAH2wZrb7eYRaWq1GjNnzuzo96tf/YpXQ2WNCXzXXHMNUlNTMXv2bD5fVVUVWJXTlpYW5OfnY+vWrRg/fnxUD85A8IzU0cFeCbU3HuZP/4H6t//Au6gLhiN/1TqIQYJspCyHQn92rdB6ai8X4KfOKYgAACAASURBVJxVZ0KWzK4TsmqYydOvhUJH+fWGwvMg5RpZTjDbqb1oZIJc7YVO04II42ULkDpvKdRpuVJOKStbLm8zDlQ9j2ZnoGqzTpmFwryVSFLHLpdi48cfoPG9P/P5NKNGY9iKIojq6K/FBvK6VfFIt/brpaygAdAp1HeFLghqHt3GxLZAQYXRUKqy6Q8ibaDkfo4gIS7oiZb7ZsnqNx45QwSIABEgAkSACHQQGKgzhN1u59Ftf/rTn/hV0LS0NCxevBgsRxwTw4Jbb0Jc+89621KWl44Jf6wx0e3FF1/kYl9ZWRkaGhp4ZBwT3m677TY88sgjXKSLtg0Uz3D8HSqVUHtiYT9zCpXPPwN4vRB1OuQXbYA6O3ZveMPZEzn38bY2w3JoK7+C6m1pDHFVmz8BKbOXQD9hDgRRIedlkG8JQIALcmf28wg5Z825To8FAYbJ85HGBLmMYQmwkshd9PjsOFz9EhrsR/lglSIZhblPIlnb+ceoyK32PIJFI1a/+nO0Hj7AOyUvuh5Z938vrClYRLHH09BRwdRpL4XDcQ7+PvO6FQQKKfCCCmN4nrfBltctLIBhdpLzOYItgYS4oI2U+2aF+cxRNyJABIgAESACRCDOBOgMIS1wOfIcSpVQe9pNT7MF5aufhNccyEmU89CjMBReLu3mDxJrztoLsBRvRsvxL+D3BKo78iYqYJw8D6bZS6DNjY1IMEgQ0jKiJMCEntbSg2jc8R6c1aVBVgQYJl2B1Pl3QpNZEKV1+Q5jv6OPXfwVaqy7uJMKQYsZuY8hPWlqTJz22qwoL1oJT12gkm3W9x5E8oKFl8zl9VrbRLfSjmumXq+lV5+UqiwutrFCCizSjed1E7UxWcdgNSrHc0QwaxLigmjIfbMG64uE1kUEiAARIAJEINEJ0BlC2h2UE8/OSqhvo9Vd3bFQjSINY9PvRZ7x6iGRTJ9FgLC8SPavjnEGKYtvRcZ/fEvajU9wa4xRa+kBfv3Ufv54yGoUSSYkz7oeppk3QGkYmjmbEnx7E859LsidO4KmHe/CURl6HVo/4XKkzb8LmuwRCbeu3hxmUYGn6v+AC5bNvJsABa+mmmO8MibrdJ4/h4p1q7nYLqhUyFu1Bsj0wuloF91K4W7LX9eTA6LC2Ha1dEzbNdMxUCiTY+LvUDIqp3NEd9xJiCMhbii9HmmtRIAIEAEiQARiQkDuB76YLDqGRuXCs/dKqEugEKPPCRRDfDEx3fDen9H08Qfctnb8RAx74mkICrpOyXj4nK1oPrwd5v2b4TEHomPamyZ7JI9+Y1FwglIVk70ho0SgNwJMkLOfP8Yj5BzlJ0O66sfN5hFygyk6k623zPwRzjS83bZWARMzlmF4ymJJHxQm+rmcFTAf+QQtJZ8BLA1fFot67XkaQdDw6DYe7aYbywsrKFWZlNdN0p0JGJPLOaKnpZEQF0RG7psVg+eTTBIBIkAEiAARIAISEKAzhAQQ43Qma3Gex0XrXnh8rVCKScg2XA6jJjQqZChWQu1tB22HDqD6lZ/xLopkEwqeeR7KlOhzAEr7tAycNVdjNSzFn6L56Hb4XY5ORwQB+vFzkDJnCbT5E+lN9sBtEc3chYD9wldckOsasZk0ZibSmCA3bNygYVbZvA3Ha38DwM/XNDp1Kcak3RvV65HndXPXweFgFUxZTjf2+Sz8fmcvvERoNMMDUW5t10zVmmEQBPoDRjweMrmfy0iIi9OhLx4PG81BBIgAESACRIAIDAwBuR/4BoZK9LPGgmerqwbHajfC7AiNCGFepmgn4rKs5VCIWpQ2vovK5n/DD1/HAnIM8zA2/etIUmVHv6gEHemuq0V50Qr4bDZAEJD3+CokTZqSoKvpv9s8uqjsGMz7/oHW0kMdb/KZZVGr55VPTYU3QmXK7P9kZIEIxIiAvfwkr7JqP3ckZIakUdORuuBO6PInxGjm+JqttRXjSM2r8PkDeRqHJV+LSZnfhdiHGOb1NHcR3Urh9Tb36rzQooS/3ANUA8kTr0fGtf8JUdTEd8E0WweBWJwjpMRLQlwQTblvlpQbT7aIABEgAkSACBAB6QjQGUI6lsyS1DxtrmrsrVgNt6+lR0dFQc0DJ3xwdfRJ1U3B+PT7YNKOkXaBCWLN53Khcn0RnGVnucfp93wDqbfckSDeS+umz+1Ey7EveAEGV31FiHFV+jCkzF4M42VXQVRTQnVpyZO1WBJgueOYIMeKOwQ33cjLeIScbvjkWE4fF9tN9lM4WP0zeHw2Pl+Wfg6mZj/UkVrA53PC6TgXKKTgKOGfPe7QK+ZdHVUoTG1XS0d3fPY12VG+ZgV8rfRHi7hsbB+TSH2OkHpNJMQFEZX7Zkm9+WSPCBABIkAEiAARkIYAnSGk4dhuRWqeeyuKuo2E68lrg7oA49LvQ0bSjKiuMUlLY+Cs1b75Bpq3/4s7oJ9ZiJyHfzLkeLib62HZvwXNh/4Nn8MashnsOh8T4HSjpg05LgP3VNLMsSDgqC5F0873YTuzP8S8tmAS0hbcBd2IKQn9jFud5dhftQFObyNfn1GZgxHqcfA4zvM8b+xPMD01QdRCq2WC2xie043ldlMq07vlcck1/rUboExNi8WWkU0S4gbPMyD1oW/wkKGVEAEiQASIABEgAr0RoDOEtM+HlDxZTrjd5U+E7eCYtHt4LiFB6CXjdtjWErdj887PUfvbX/EFKDOzUFC0AQq9PnEXFIHn7Pqpo/I0LPs2wXpqL+DvfJMuqDRInrYIpsKboE7Pi8AqdSUC8ifgrDmHxl0fwMae+6CmzZ8QiJBLINE5kNetlke4ORwlsLaeRJnnLNxCYGFqP5DjAZQhK1VAox0BjbYtr5tuDNRqltct/P8PGt7/C5o+ep9b1Y4dj2FProagDJ1F/k9C4nso5TkiFjQoIi6Iqtw3KxYPANkkAkSACBABIkAE+k+AzhD9ZxhsQUqeJQ1/xdmm98J2cEza3WAfQ7k5yy+gYt0q+F0uXukz/+lnoBkxatAj8Xs9aPlqF79+6qwJXMdtb0pTJkyzF3MRTqEdGoLkoN9wWmCPBJy1F9C06wNYT+wJyYOoyR3DI+RYNKggtClaMuHo8ViCCimUwOE4C583NB2BF0CNAnC26WpKKDBWMwPJ+qm8oIJaMwJiP6th+30+VP1iA+zHj3IyphuWIPOby2RCaei4IeU5IhbUSIgjIS4WzxXZJAJEgAgQASIwpAjI/cCXaJshJc+TdW/hgmVT2AiGm5ZgYubQfdPks7eifO1TcNdUc2ZZ33kAyQuvDZtfInb02MxoPrgVlgP/hNdmDlkCy5FlmrME+rGFEMTwo2ISkQP5TAS6EnDVV/IIOetXOwF/oPooa5qcUUidfxf04woHRJDz+Ry8amlnFdMSeNz1vW6gQpnKr5YqtSNQ5jiEJmcp768SjZiV96SkuUC9Lc08X5ynsYHPkf3gwzDOnUcPWBwJSHmOiIXbJMQFUZX7ZsXiASCbRIAIEAEiQASIQP8J0Bmi/wyDLUjJkyLiwt8bdpWr5vVXYCv+kg8yXrUI2d9dHr6BBOvJruGZizfxKDh4PR3eCwoVDFPmI2X2EmiyRyTYqshdIiA9AVdDFZp2f4iWYztCrmqrs0bwK6v6CXMiur4ZiYd+vwdOR3lHIQWno7Qtr1unMNjVnijqoOF53cZy8U3L8rqpOnO1+fweHK/diOqWHXyoQtBgeu6jyEiaHolrvfZ1lJ5BxfoiwOuFoNGgYM16qPOGSWafDPVOQMpzRCxYkxBHQlwsniuySQSIABEgAkRgSBGQ+4Ev0TZDSp6R5oi7suAFGDVDU3wxf/oP1L/9B/64qAtGIP/pdRDV6kR7fHr11+/zwna6mAtwjvKTIX0VhlSYZt0I08zroEhKHlTrpsUQASkIuJtq0LT7b2g++jngYxc9A02dkY/U+XfCMPGKfkWOsj8GuN01gSumbVVMnY4y+P3unt0XlNBoRvCrpRrtWP5Zpc7tUxj0+3043fC/OG/+hNsWoMBl2Q8i17hAClTchmXrFtT98Xf8a1VuHgpWPwdRp5PMPhnqmYCU54hYcCYhLoiq3DcrFg8A2SQCRIAIEAEiQAT6T4DOEP1nGGxBap5flq+ExRma86s7j02a0ZhbsF7axSSINfuZU6h8/hkevcHeKOYXbYA6OydBvO/bTa/diubD22DZ/yk8zaFX2DR5Y3n0m2HiXAgKSqreN03qMdQJuM21aNrzEX9NBQtyqvQ8pM27E4bJV0IQFX1i8njMcNpL2goqlHIBzuez9TJOgEqd1ya6BSLdNJrhEERVn3P11KGs6WMuyLW3CRnfxoiUm6O2FzyQCYu1v3kdLbsDkXeGy69A9oOPDMh1XkkWJJERp+U8rFV74XO3QlQlwZB3OTQmaf8AJvU5QqKld5ghIS6IqNw3S+rNJ3tEgAgQASJABIiANAToDCENx3YrUvM8e24lznrOwtdLbnHRD4xWjsboUUNPiPM0W1C++kl4zU18C3IeegyGwjnSbuoAWWM5rvj102NfwO92dnohKmCYMBcpc5ZAO2zcAHlH0xKBxCbgbq6HefdHsBz+d8j1blVqDlLnLYVxyvwOcdvnbYXDcS4gvDkCopvHE8ih1lNTKtM6rpdqeMTbaCgUSZJDq2r+nF9V9SNQIXlUyh0Ym/4fkghmPqcDFeuehquinNvOuO/bSLlRGqFPchAxNuiy1qD2wEY4GkKjkdm02vSJyJq1HGqDNH8AkvocITWaiIU4u92ODRs24J133sGFCxeQlpaGxYsXY926dRg2LLw7z2+++Sa+853v9LmWt956C9/+9rc7+t1///1g/9ZT+/Wvf43ly6PPYyH3zeoTGHUgAkSACBABIkAEBoQAnSGkxS4lT6fjPMrPPQF2salOATi6ybev9QGZXoDFVBSMegEarbR/mZeWjrTWeIW/n6+H/atj3HDKktuQ8fVvSjtJnK2xK2etZw/Dsm8zWs8dDpld1Bn51VN2BVVp7MwZFWcXaToiMKgIeFoa0bTnYzQf+hf8Hje75wmYADHPAPWYPHh1VrhdrABMb3nd9NDoRkOrHQsmumm1o0PyusUaWJ3tIA7XvAyf38WnyjMuwuSs70MU+o7s68s3V00Vyouegt9hBxQKDHtyNXTjJvQ1bFD93GWtRsVnq+FzhVayDV6kqDYif+E6ScQ4Kc8RsdiIiIQ4h8OBa665Bnv27EFubi6uuuoqlJWVYe/evcjMzOT/Pnr06D793LFjB954441u+1ksFnz44Yf8Z6WlpSH22oW4m266CTk5lyqly5Yt4/5F2+S+WdGui8YRASJABIgAESACsSVAZwhp+UrJs6Hur2iqf6/DQRYT1SoCLLsRe3uV5AM0Qe6z3EK6pElQKFOgUJigZJ+VJv6hVKRAELWSRElISyx6aw3v/RlNH3/ADWgnTMKwx1dBUPT/jWf0HkU/0udyoPnoZ7AUb4a7MVD1tb2pM4cjZc5iGCYvgKgaXHnvoidGI4lA/wkw4ZuJbCzKzd78FVobD8GrMAd+wfbQBEEFtXZkRyEFFummUuf0mdet/972bsHsOIODVS/A7bPyjhlJszA95xEoxOD/JaLzwlq8FzWvvcQHK1JSUbB2A5SmlOiMJeCois+Luo2E67oUFhmXf3VRv1co5Tmi3850YyAiIW7VqlV47rnncOWVV2LLli0wGAzc5EsvvYTHHnsMCxcuxPbt2/vlJ4tq++EPf4j58+eDCXbBrV2I27ZtGxYtWtSvebobLPfNknzBZJAIEAEiQASIABGQhACdISTB2GFESp51NW/B0rRJMgcFQd0mzKVAqQgIdEy0C/6ai3bKFIiiVrJ5Y2HIdugAql/5WeCNYbIJBc88D2VKaiymiqlNlq+K5X5j+ap8ztaguQToxxXCNGcJdMMnDyoBNaZAyTgR6IWAx90Ih70ErHppoKACy+tm73kEC4JjQVBNgGhPQvKI65A6dSkUKumvmEqxcVZXJQ5UrYej7dpsinYCZub+FCpFQPvoT6v/8//CvOljbkI3cTLyfvpUwv7hIxIOLCdc+b+fCHtIwbUv9DtnnJTniLAdj6Bj2EKcy+VCVlYWWMQaW9TMmTNDppk+fTqOHDmC4uJiFBYWRuBCaFcmwO3atQsbN27ED37wg5AfkhAXNVYaSASIABEgAkSACMSQgNwPfDFcekxMS8mza0RcXw4zoc3fdjWpr759/VwQNIFIOibWKYIi64K+5z8bANHOXVeL8qIV8NlsgCjySDj2xjBRGkuCbr/wFY9+s50pBvydV95EjQ7GadcgpfAmqFKzE2VJ5CcRkB0Br9cGp+NsQHizn4XDUQKvJ5BLsqemVGYErpbqxkIl5MJx7CSai7fC5+wU61iF4tS5tyF55nUQVf2PNpManMNdj/3Vz8PmquCm9ep8FOatgFaZ3q+p/F4vKn/2LBynTnA7KTffjox77+uXzUQY3HDir2g62RmZ3pfPaRPvRtqku/vq1uvPpTxH9MuRHgaHLcSxKLRrr70WY8aMQUlJySXmWI641atXY82aNSgqii6U8Ny5c/wqqlqtRk1NDVJTQ/8iR0JcLB4BskkEiAARIAJEgAj0l4DcD3z9XV+8x0vJsz1HXLhrYDni1Jph8Hos8Hgs8HrM8Hq7fm3mP2cfvVf4C3dWoFO0axPsgq/F8si7lICgpzT1O9LO53Khcn0RnGWBSrLp996H1JtvD9/ZAezp87hgPb4T5uLNcNWeD/GEJYg3zV6M5KkLwcQ4akSACIRPwOdzweU8H4hy4wUVzsLtqurVgKgw8OulgZxugc8sGrhrY1WLmWjOCqf4HJ1VURVJJqRccStMM2+AqJZXBLHba8XB6p/B7DjNl8NEuFl5K2FQh5cXvydwHrMZ5WuehNdi5l0GU3Gcrmt2t9bDWrEbTWc+6jU3XNdxpjFLkDltWfgPbzc9pTxH9MuRHgaHLcS98sor+PGPf4x77rkHf/nLXy4x98knn+DWW2/F0qVL8f7770fla7uY15ONdiHuoYceAvsrmNfrxahRo3Dbbbdh4sSJUc0ZPEjum9XvBZIBIkAEiAARIAJEICYE6AwhLVapeVaUFcFhv7RKW1evtbqJyB8Z2R+U/T43PN6AKBcq2gW+7/yZhKKdqA29Cts1l11bfruAaHdptEntm2+gefu/+PL1M2cj5+HHZH9tkyWDtxz4JywH/wWfPTTZt27kVF79NGnMjAHPMSXtK4GsEYHYEAjkdavikW7t10vZHy0C2TO7byyvG8vl1l5QQctEN1V2RL87vI5Wfo3cvPcT+ByBPGyssSIqqXNv5UVU5CSie31OHK55BfWtB7mfKtGAmXlPIEXbv0rL9lMnUPnCOsDng6jTIb9oA9TZ0lQLjc0TE75Vr7MF1qov0VK+E46GQORfpI0i4oKIPfroo3j55Ze5GMdywnVthw8fxowZMzBr1izs378/Uta8/4QJE3D69Gku5DExrmvrqWqqIAh48MEH8eqrr0KpVEY1Nxsk9aEvakdoIBEgAkSACBABIpBQBOgMIe12Sc3T5apBRdnT8Hl7qdamMCJ/5Dqo1bF7M8QiTrze5g7RjkfccREvEGEXEO0CX/t8wbnOoucriLog0c4EX10r7PuOATZAoTIh677/gtqQ3XY9Vn5FDBxVJTDv2wTryT2Ar1MkEJRqGKdejZTZi6HOyI8eEI0kAoOcAAtg8Xga4LSX8qulgc/n4O8trxsEqDUF/HopE9/YZ7UmH4IQ/XvtYMzsmqrlwBY0ffn3EGFd1BqQcvnNMBUuhkIrjxxyPr8HX9X+FlUtn/EliIIG03N+hEx9aKquSB+jps2foOGdP/Jh6oLhyF+1DqJGftd0w1mXz+OArXo/Wip2ovXiYcAfKugKSi38Hkc4pngfyhEXhOqBBx7Ab3/7Wzz11FN49tlnL4HIrquOGzeOfzAxLdLGKq/OnTsXaWlpqK6u5tdTuzYmtGm1Wn5FNj8/n19f3bRpE1gRiaamJvzoRz/iYmE4jc3BPoLbiRMn8K1vfYsLiUxQpEYEiAARIAJEgAgQgXAISC0chTPnYO4TC55MjKut2thtZByLhMvKWx5TES7S/QqIdp2RdsGiXeDKLBPx2kW7XhKlRzCxKOoCxSeCr8KGXIvtvDYrirET7fxeD6yn9nIBzll1JmQFyuR0mApvQvL0a6HQ9T95egR4qCsRSAgCXq+1TWxrK6ZgL+G/S3prSlUWWISbRjsWWt1oaLSj+n0FPhxYrNIxi3I1f/kxvLZOH0VNEi+ykjJ7iSxe50zMPNPwNsrMH/FlCVBgStYPkJd8dTjL7LYPs1nz+iuwFX/Jf26cfzWyvvdgRBGGUU8uwUC/z4PW2iM88s1WXQy/l9Uk72yCQg197mwY8+chKWs6Knc+R1VTg/n42RMQRou1EPfwww/jl7/8JZYvXw5WOTWSdvz4cS6c+Xw+nD17FgUFBX0OZ3ns1q5d220/EuL6xEcdiAARIAJEgAgQgSACsRCOhjLgWPJk169sLfvAkpArFHrojXOg0Y5IaNxctOuay64t2s7THm3nboKntQ5Qh3X075OHKCZ1iHYsJ1SgemygKEV7Lrv2arKCqOrTHuvgbW2G5dBWfgXV29IYMkabP4FfP9WPnwNBVIRljzoRgcFOgL32nY5znRVM7aVwu2t6XbaoMELLBbfO3G4KZfKAovK5nWg+tBVNez6G19pZDEJQ63jUa8qcm6FIMg6oj2zy8+ZPcKo+EMXG2vj0b2Fk6q1R++Wzt6J87Sq4awK5+DKXfQ+ma66P2l6sB7IrzY6GUzzyzVr55aV53wSRi27GgvlchBOVnXn/XNYaVHz2dK+54kS1EfkL10Ft6H9keizPEVJwDjtHXCyvpno8HgwbNgy1tbXYuXMn5s2bF/HaWO66d999F7///e/BrrD21Sgiri9C9HMiQASIABEgAkQgXAJyP/CFuw659COe0u5EIPLiZdiK9wJKQL9oLlLvuhXtIl2gMEWgKEV7rjt2TdbvC/8qUW8ei6K+Q6hTtkXYdVaTNcHb4kDr0WJYj+0DXJ5OUwoljJPm8QIM2tzR0kIha0QgwQgwEcTlrAgS3UrgdJb3kddNw6PbAqLbWF5QQanKlG3UFSvG0nx4G8y7/waWF7K9CSoNj4RNufwWKPWmAd256pYdOHbx1/C35dMbkXIrxqffF3V+SmdlOSrWroLf5QSUSuSvXAvt6DEDusbgydn/H67mC2gp3wFrxS547A2X+KZNnwBj/nwYhl0BhaZnUZeJcbUHNnYbGadNn4isWcslEeGYg3I/R4QtxMWyWMM//vEP3HLLLbxiamlpaVQP3cqVK7FhwwasX78eK1asiMqG3DcrqkXRICJABIgAESACRCDmBOgMIS1i4iktT/Onn6D+7bZcRMNHIn/VMxC7SQPTdVafz9FZPTY4l12QaNcu5vn9odeSol6BC4BLhFKTBnXyCKi0mVzE41F3XMRr+1ppkixfVdS+0kAiECMCPK+buw4OB6tgynK6sc9n0fvrTIRaM5yLbu3XTFkFaEFIvAhSv8eN5qOfoWn3h/BY6kMFuZnXI2XubVAaLq3OGqPtuMRsve0wDte8BG/b771c49WYkvUAxChz6LXs2YmLG3/J51GmZ6Bg7QYoDAMbAei2XURLxS5Yy3fC1VJxCQN18nAe+WbInwdVUmZE6J2W87BV7YPXbYNCpYc+bw40Jmkj0+V+jghbiNu2bRvPzTZmzBiwfHBdW3vF0zVr1oBd+4yk3XfffXj77bexevXqHq+L9mWPFWvYuHEjL9jArrlG0+S+WdGsicYQASJABIgAESACsSdAZwhpGRNP6Xjaz5xC5fPPAF4vRF0SCtauhyqr/9d+uhftzAjOX9fxdVs+u8D3Zvj9TG3rfwtE2gVdhVUErsh2XJXtqCabTKJd/3GThRgS8Hqau4hupbywS29NpcoJVDDlBRVYfreR3VZJjqHbMTfN8kW2HPsCjbs+gMdc2zGfoFQhecZ1SL3idiiNaTH3o7sJLI4SHKh6AW5foAhQRtIMTMv5EZRi53XMSByr+583YfnXZj4k6bLpyH30CQiiGImJfvf1OMywVu7med+cTZdqPsqkzEDkW8F8aJL7TgfWb4f6YUDu54iwhTiXy4WsrCxYLBYcPHiQV0gNbtOnT8eRI0dQXFyMwsLCsJFZrVZkZ2ejtbWVF3lgxR4ibU6nk48rLy/HF198gQULFkRqgveX+2ZFtSgaRASIABEgAkSACMScAJ0hpEVMPKXh6Wm2oHz1k/CaAzmXch7+CQyzZktjPEIrrsZqWIo/RfPR7fB7HQB7r8oKBGoB9fCR0BSMgKBXXlI9VjLRTmGAsk2oa89nF/J9R247Eu0i3FrqHiEBn8/J87o57KVwOkr4Z4+7U2TqzhyLBtWwSDcmuPErpqOhUA5sxFSEy+5Xd7/Pi5bjO9G08324m4Jy4CmUME2/FilX3A6VKaNfc0Qz2Oaqwv6q9XB4AlF7Js1YzMx7AmpF5Hvj93hQsWEtnKWBAjWpd9yF9KX3RONWRGN87lZYq/bxvG/22qMAQvOIKtTJMORfCUP+fGjTxsn2WnPXRcv9HBG2EMcWxqqTPvfcczyH25YtW6DX6/l6X3rpJTz22GNYuHAhtm/f3sHgtddeA/tYunQpvzbaXfvDH/6AZcuW4YorrsDu3bt7fGhOnjyJffv24d5774UmqKxvXV0dWCGJDz/8EEwMZCKhIAgRPXztneW+WVEtigYRASJABIgAESACMSdAZwhpERPP/vP0+3yoevE52E8c58ZSbr4NGfd+s/+GI7DArtfZy47y6qetpYdC3uCJWj2vfGoqvBEqU/fXmth4lqfO01YdtttcdkHVY/1+dwTe9dyVJbPnBSfarsK2F50ICHhtEXhtP0vEa3+SQCIjYRHw+708r5vDXtKW262Efw/4ehwviFoutLEot/bcbkplyHb/jAAAIABJREFUetTvccNyNEE6sd9r1hO70MgEuYZAgQPeRAWSpy1C6pV3QJWSFdfVODyNOFC1AVYXy9cH6FV5mJW3EjpV5MKgp7EBF9Y8CV9LCyAIyP3x49BPmyn5enxeF1ovHuKRb601B+D3hf7uFJRaGHIv55FvSZmXJWSBHLmfIyIS4hwOBxYtWoQvv/wSubm5uOqqq3D+/Hn+fWZmJvbs2cPzvLW39sqkTGh78803u32AbrzxRvzzn//E66+/jh/+8Ic9PmRM4LvmmmuQmpqK2bNn8/mqqqrAKpy2tLQgPz8fW7duxfjx46N+UOW+WVEvjAYSASJABIgAESACMSVAZwhp8RLP/vNsePcdNP39Q25IO2EShj2+CoIiPrmiWAVEdp3MUrwZrvrQ3EKq9GG8CqLxsqsgqqO7wtUdnYBoZw8pOtFRjMLLilEErsW2F6SQRrQTILJIu27y1wVEvGDRjkXaxYd//58eshANgUBet1oe4eZwlPDcbizyrfeoTgWv2twpuo2BWs3yusX3SmI06x3IMVyQO/Ulj5Bz1QUEMN5EBf/dkjbva1ClSn8Fv6c1u71WHKx+EWbHKd5Fo0hDYd4KGDSRX99s/eooql5cD/j9EPUGFBSthyqz/+IiK/ZhrzvOxTdb9V6wSLiQJiqhz57J874lZc+EqGQhy4nb5H6OiEiIY9tgt9t5dNuf/vQnfhU0LS0NixcvBssRx8Sw4NaXEMcqlxYUFEAURbCv09PTe9xpJrq9+OKLXOwrKytDQ0MDj4xjwtttt92GRx55hIt0/Wly36z+rI3GEgEiQASIABEgArEjQGcIadkSz/7xtB3aj+pXXuRGFKYUFKx9HsqU2Cc2d1vqYTnwKZoP/Rs+hy1kEUljZnIBTjdq2oBH9jDBxOezhwhzoaKdua16rIVH48EfVMk16q0RoFAY26rHBgpP8Ki7NhGv42seicdEOxJiokYdp4FM3O0spFACh+MsfN5AvrCemkqd23m9VDcGas0IiKI6Th4PvmmYuGQ7XYzGHe/BVXu+c4GCCOOUBUid9zWo0/PisnCvz4UjF19FnW0/n08p6jEz93Gk6iZEPH/jxx+g8b0/83GakaMxbGVRWAV2uk7Eftc5m0r5tVNrxW54neYuXQToMifzvG/6vMuhUBsi9lWuA+R+johYiJMraCn8kvtmSbFGskEEiAARIAJEgAhIT4DOENIyJZ7R83TX1aJ8zQr4Wm2AKGLYE09DN2FS9Ab7GMne6DkqTsFcvAm2U/sAf+eVO0Gl4dfFTIU3xe3NsNQLDYh2rQHRzsOEubbIOh5hFxDq2Of2a7OAVwIXmGiX3CbamaBsi67rPrcdiXYSAO/TBKsgzKqWdlYxLYHH3VnNszsDCkVKoJBCR2630VAoBo/Q0Se0OHZgr9PWkv1o3PE+nDVnO2cWBBgmzUPa/KVQZ4QGDcXCPZ/fixN1b6CyeRs3LwoqXsAhSx9+Dn02jkX8Vf/fX6D1UEDUS150HbLu/37YLrtaKnnkm7ViJ1j1065NkzI6UPF02JVQ6gam2EXYi4myo9zPESTEBW2s3DcrymeQhhEBIkAEiAARIAIxJkBnCGkBE8/oePpcLlQ+twbO8+e4gfR7v4nUm2+Lzlgfo/weN1pO7ObXT0Pe+LJIkJQspBTeBOO0a6DQJsVkfjkaDYh2tg5RLnANtk3AaxPr2v+NRVNJL9oFX4UN/ZqLeAojRdqF8eD4/R44HeUdhRScjtK2vG6hSeyDTQmiLpDXjRdSCOR2UyjTBjz6M4zlDqouXJArPYTGne/BWRVc9ZMJcnOROu9OaLKGx3TNzIeSxr/gXNMHfB4BIiZnPYBhyYsimtdrs6K8aCU8dYFCHlnfXY7kq3q24bE3oKViN6zlO+C0lF0yl0qfA2PBAhjy50FtjE+UYEQLlriz3M8RJMSRECfxI0/miAARIAJEgAgMPQJyP/Al2o4Qz+h2rPbN36J5+1Y+WD9rDnIeelRyIcBjM6P5wL9gOfhPeG1MTOpsuhFTYJq9BPqxsyCIdLWyt128RLRrKzoRyGXXmc+u/XtpRDuxI9KO57XrKEjRJtrx7wNfs9x3cr0e63Sch7VlL3zeVoiKJBiMl/M8a9E0tg9ud03gimlbFVOnowy95hAUlNBoRgQKKWjH8s/syqlceUXDJdHHtBeKYVdWWcRucNOPn4O0+XdCkzMqpsu8YN6Mk/VvdRSpGZf+DYxMuT2i38nO82WoePZp+N1uCCoV8p9+Fprhnc+612WFtXIPv3rqqD95acVTbSqM+fN4xVNNyqiI5o4pnDgYl/s5goQ4EuLi8DKgKYgAESACRIAIDG4Ccj/wJRr9WPJ0Ws7DWhVIVC2qkmDIuxwaU3Rv4uXEtXnn56j97a+4S6qsbOSvWQ+FXi+Zi46ac7Ds24SWE7sAb2fONEGhgmHKfKTMWQJNVuJzlAyYhIZYHiyf18YLTXSfyy742iwTR3uuyBm+W2KHUBfIXxf4UCraBLz2CrKK+Il2LlcNaqs2wmFngkNo0+omIitvOdTq3hP0M35Oe0lbQYVSLsCxKMbemkqdF7hiyiPdxkKjGQ5BVIWPknoOGAEuyF34Co073oXjwokQP5LGFnJBTps3Jmb+1bTswtGLr8PfdmV9uGkJJmT8Z0SibfMX21H7/zZ2/G7Pe2oNHC0nAxVPLx4C/KHX4UWVHoa8ubziqS5jUkRzxQzEABiO5TlCiuWQEEdCnBTPEdkgAkSACBABIjCkCcj9wJdomxMLni5rDWoPbISjoZs38ekTkTVrOdSG+FXZk3JPnOUXULFuFfwuV7dRE9HO5fd5eSJ0lv/NUR7KTWFIhWnWjTDNvA6KpORop6BxEhMIiHbWPnLZWTqqx0oj2imgUCZfmsuuazXZDtFOiHjVLlc1KspW91oMQVQYkT9yXYcYxyLmHI5zAeHNERDdPJ6GXudWKtPacroFcrtptCyv29C5Xh3xxiTQAPuFE2jc+T7sZUdDBbnRM5C24C5oh42LyWoaWo/iUPUv4PU7uP0cw3xclv0gREEZ9nwXf7cRLSe3AyzN3TAREEPFdiYM63MLYchfAH32dLA/kAz1FotzhJRMSYgjIU7K54lsEQEiQASIABEYkgTkfuBLtE2RmqfLWo2Kz1bD5+q5oqGoNiJ/4bqEE+O8ra2oWLsS7os1fJv7yiMUzrPgtVvRfHgbLPs/hac5NCG9Jm8sUmYvgWHiXAiK8N9IhjMv9YkvgQ7Rru1abKDgROe12PYCFPyqrJdF2vWcIy18z5lo11YxNqgIBY+6a4u2a782K4r6jqt0FWVF3UbCdZ1XpcqGNmkCj3hzu6p69VkUk4IKKbDcbqOhVA3OxPXh78/g72mvOIWmne+j9ezhkMXqRk7lgpyuYKLkEJodZ7G/+nm4vc3cdrpuGqbnPgqlqO1xLvb6dDSc7qh46nNbQ/sKIpKypvJrp4bcORBVOsn9TmSDUp8jpGZBQhwJcVI/U2SPCBABIkAEiMCQIyD3A1+ibYjUPCs+L+o2Eq4rF236RORfXZQwuNi1q5rXXoZt/17uc/LV1yDr//wgav9d9ZU8+q3l2Bfwu52ddkQFF96YABerqJGonaaBcSHARAGvt6Wb/HXmtiuzQdVkudggjWjHBDpWCMHtquzXOgVBBbV2ZEchBXbNVKXOGbLX9voFc5AMdlSV8Ai51pIDoYLciClInX8ndMMnS5pTzeaqxoGqDbB7AsUXkjVjMCvvCagVnRHF7He6q/lCW8XTXfDYu6nMy4I6K4HspT+GcdrcQbIb0i9D6nOE1B6SEEdCnNTPFNkjAkSACBABIjDkCMj9wJdoGyIlT5YTrvzfT4SNoODaFxImZ1zT5k/Q8M4f+drUw0cif9UzENXqsNfKOjKBpbX0MBfg7OeOhIwVdUaYZl4P06wboDRSpFBEYIdw54Bo19wh2nl49digCrId37NIO6lEu1Dgak1+WyGF0fyzRjscQgRXAYfw9g25pbP8lyxCznZ6X8jatQUTkTb/LuhGXiaZIOf0NOFA1fNocZ3ncyWpclGYtwJKFwKRb+U74WqpuGQP1MZ8GAoWQNFiQt1r/81/rjAmo+CZ56FMpd/N3T20Up4jYvGiICGOhLhYPFdkkwgQASJABIjAkCIg9wNfom2GlDwbTvwVTSffCxtB2sS7kTbp7rD7D1RH++mTqHz+GcDng6hLQsHaDbxIQ7jN53Kg+ch2fv3U3VgdMkydOZwXXzBMng9RFZmwF+781I8IMAJ+v7cj0i64EIXXYw5ck/Va4HSc43nvwm3JKTcgK/e74XanfkSAE3BePI/GXe/DdvLLECIsCjh1/l1IGj1dEkHO7W3FoeoX0eQIFI9QehXIPuuFJpBCrqMpdRm84IKRVTw1De/494b3/4Kmj97n32vHjsOwJ9dAUFKagK6PsZTniFi8REiIC6Iq982KxQNANokAESACRIAIEIH+E6AzRP8ZBluQkmfdkbdgKd0UtoMKdTKMIxYiKfMysKuqolIT9th4dfRYzChfswJecxOfMveRn0A/c3ZY07vNtTAXb0bLkW3wOe1BYwToxxXCNGeJ5FeywnKMOhGBHgg01P0VTfURiOkZdyMtU/5iOm24PAk468rRtPMDWE/sDrlirckdw6usJo2dFbUgx6p1W6uL0Vz+Bcq0R9FqCjAQvUB2GaD3GGEcdiUX4LRp47q9Ou33+VD10vOwHwtEMJtuWIzMb94vT5gD6JWU54hYLIOEuCCqct+sWDwAZJMIEAEiQASIABHoPwE6Q/SfYbAFKXlGGhEXshJRCV3aeOgyL+Mf2tQxEESFtIuN0Jrf60XVz9fDfuI4H5ly8+3IuPe+Xq2wvEP2C1/BUrwZtjPFLAypo7+o0cE47RqkFN4EVWr4EXURuk3diUDUBJyO8yg/F8H18lEvQKMdEfV8NJAIMAKuhko07foQLcd3hPzO1GSP5Dnk9ONnh5Vj0O91w3bxEKwVO2Gr3g+/z80Bs9/C9XlAS3qAtwAFpmU/hGzjFX1ugLelmf8xxtMYqAKcvfxhGK+Y1+e4odRBynNELLiREBdEVe6bFYsHgGwSASJABIgAESAC/SdAZ4j+Mwy2ICXPSHPEseqpPVVXFZU6aDMmIylzCnRZU8Hy9giCIO3i+7DW8O7baPr733gv3cTJyPvpUxAU3YuDPo8L1uM7ef43V+2FEMuq1ByYZi9G8tSFYGIcNSIgZwLhVk3V6iYif2TiFFyRM3PyLUDA1VgdEOSOfQH4fR1Y2BV+FiGnn3j5JYIcy5Nor/+K53yzVn0JFgkX0gQF9NkzoM+fh4vacpyzfNj2YwGTM7+PfNO1feJ3nC1BxXNrAK8XgkaDgtXPQT0sv89xQ6WDlOeIWDAjIS6Iqtw3KxYPANkkAkSACBABIkAE+k+AzhD9ZxhsQWqekVRNHXbVGrhbqtBadxT2umP8zdQlb6LanFVoUqDLuoxfY2URc6qkDGlBdLFmO7gf1a++yP9VkZKKgqIN+P/buxPwqKq7f+DfWTLJZLKTDAlJBAVlC2tkB1Gsu1Yo2LetvqK2tv5t36pQrQuroLgVtdpKK1oUtK1VUKwbtgoKsoNsIigCJpCQheyZySQz83/OGbJMFjLLnZk7M9/7PHlamXvP8jn3wplfzj0/fUpKhzqbak6jatfHqNr9HzgsNW6fG88dKrOfxvcV+x1pA9peFk4BpQRstmIUHpsLh939fm5bvlaXiJw+i2AwZCpVLcuhQItAY8UpVGx5B9V7NwAOe8ufG9JzkDp+GkwDxsJWfcyV8fTEZtitrq0DWg8NjOkDkZAzAQnZY6AzJLR8VFC1DgdL/9byKmy/tP/BualTu/1FT9Un61D66suynJisXjIYpzXyFyvCQ+l5hNKPAgNxbUTVPlhKDz7LowAFKEABClBAGQHOIZRxbC5FaU9bbTEKN8ztcqWbqFeshMuZvAiGBPcv8U6HHQ2V36FeBOVK98Nafrjl1aL2vY4xZbYG5tIHQxebqBhMY8kpFCx4EI76OkCrRfbv58LYf6Bb+dYT38jVb7Vis/E2XxQ1MbFIzJuElAuvhPjSyIMC4SgggnElJ5fBavm6Q/PFSjhzrzsYhAvHgQ2zNjdWlaJi81pU7/0UsDcBMWKjNrFPgE5kXujQm9iUc2XwLTFnHPTGM++hdtLn4tot2Ff8PJxokp/mJl+BAekzz/oLE7HtQMlf/4SazRvlNQmjxqLnnXd1G8ALM3Kfmqv0PMKnRpzlIgbi2uCofbCUHnyWRwEKUIACFKCAMgKcQyjj2FxKIDxFMK5k1zJYyzv5Et9jAMwj7+gQhOusVw67DdbyQ67AXMk+NFQeddvQu/UaDWJT+siVcv4mfnDYbDjxyHw0HBd1AT3+50akXnWd/P9Oe5MMvIkAXMPJb92arE/qgeT8K5A0bAp0xtbVF8qOFkujQHAFxJ5xdTXbYbfXQaczwZQ4invCBXcIor62JstpVB35D6qP/Ad2R3UHD11MEpLOvRSJ50yEITHbY6/T9Qewu+gp2J2uRDo9E8ZiSM9fQ6sR0b7OD0dDAwoXzYWt0LX9QPpPb0bKFVd7XGeknhiIeYSSVgzEtdFU+2ApOfAsiwIUoAAFKEAB5QQ4h1DOUpQUSE+xZ1zdye2wN9ZBF2OCqdcoxCb7vrG73VYrX1+1lOyXwbnG2pOdY2j1MgtefMYQrxM/lPztr6je8Iks15Q/Cpm/mSVfOa368r+o2rkO9lr3V6DicvojZdRVMF0wKuTJJZS9M1gaBShAgdAIiL/ra09uk/u+ib/zXekW2hxiIZuIyYkfK6BPzkDq+KlyH06NTu9xo6sbjmLXycdgs1fJa9KMeRieNRt6bdevnNqKi1C48EE4LBZAp3OtmL5ggMd1RuKJgZxHKOHFQBwDcUrcRyyDAhSgAAUoENUCap/whdvghLNnk6Uc9SWu11hFYK7jPkGu0dDojTB6kPiheuMGlCx/QV4T0zMT5l/fiZr9n8pMfs4mV/Y9eej0SBw4HsmjrkJc5rnhNuRsLwUoQAHVCTiaGlBXvMuV8bR4t1iC7NZGbUw8TL1GIzFnAmLis1G17QNU7VoHZ2NDy3liZXLquOuRNPQSaPRdr2xrW3B9YzF2nlwCS+Mp+ceJsediZNb9iNWLd2A7P2p3bkPxc0td/xyIPUQXLoE+ueMeoqpDDlCD1D6PYCCuzcCrfbACdI+yWApQgAIUoAAF/BTgHMJPwHaXR4qn2L+nNfHDAVjKDpw98UPGYFfiB/MQmfihoeA4Ch+eA2djI5CsQ+ywPmg4dcRNS2dKRvKIy5A08gfQm6L3S5eydyBLowAFolVA7AsqkvXIjKdF2+FssrpRaLQxiM8cicTcCYjvORxancHtc3t9NSq3vYfKnR/BaWu9VpeYhtSxP5RbBWhj3K/pzLqhqRK7ih5DTcMx+XF8TCZG9noA8TE9uxyasn++hsoP3pWfd5dVO9LHV+3zCAbiGIiL9GeQ/aMABShAAQoEXEDtE76AAyhcQaR6uhI/HD2T+GHfWRM/6OPNsB+thrPc6noDSueOHJt5HpIvvBKJA8d5vMpC4WFicRSgAAUiQkD80sR6+rBc+VZbuAV2W/t93zTyFyRi5VtCr1EQK+G6O+yWWlRu/wBVOz6Ao6G+5XSdKQUpY69D8vBLoTXEnbWYJkc9viz6A05bDsjzDLoUjOx1P5Ji+3R6ndNux4knFsN66KD8POXq65D+4xu7a2pEfq72eQQDcQzEReSDx05RgAIUoAAFgimg9glfMC2UqCtaPN0SP5TuR0PFd50nfhCBOPGmUz0QlzYAqSOnI/6cPGbGU+JmYxkUoEDUCjRUF6C2YCNqCr9AU31pB4fY1PPlyreE7LHQx/m24thurUPVjg9Ruf19OKx1rQG5+CSkjLkWySMvP2tAzuFsxL5Tf8Kp2i3yWrFX3PDM3yEtfnCn49ZUWYmCBQ/AXunaOzTz/2YhIX901I2x2ucRDMQxEBd1DyU7TAEKUIACFFBaQO0TPqX7G+jyotFTrMio+2YrTu95B7aao4BYcBHbhXRL4oc8GDOGIC61L5MyBPqmZPkUoEBECDTWlaC2cDNqCjfBVu3KNNr2iEnMRmLuRCTmjEeMqevXQL3FEKviKneuk6+timQ7zYfWmIiU0VcjJf8KaGM7X2nndDrwddkKFFStk5dpoMfQzP9Dz4QxnTbDcvhrnHjsYcDhgNZoRM78R2HIzPK2yWF9vtrnEQzEMRAX1g8YG08BClCAAhRQg4DaJ3xqMPKmDdHk6WhsQM3+z+WKCVtZoTuTEzCNGg1Nsh6W8oPdJH4Y2LK/nCExh6vlvLnheC4FKBDRAvaGatSe2IKagk2wnj7Uoa96Yw8k5IyXAThD0jkB/fvTYbOiatfHqNz6LsR+ci0BuTgTUkZdLbcc0MWZOrRR/LLmaMUafHv6jTOfaTAw4zbkJl/W6dhVfvQeyv6+Un5myDkHOXMXQRvb1W93Im/41T6PYCCOgbjIe+rYIwpQgAIUoECQBdQ+4Qsyh9/VRYNnY1UZqnZ9hOovP3F7XUni1QKoADJ/MRsJI0fJP5KJH2pPor5kHyyl3SV+SIYxI+9MYC4PMfEZfo8JC6AABSgQTgKORgvqinbIlW/1JXsBp8Ot+dqYBCTkjJX7vsX16A+NRhvU7omAXPWX/0XFlndhr6tsDcjFGpF84VUyKKczJnRoU2HVf/FV6fKWbQz6ps3AeanTOwQPxb8ZxX96BnU7tsoyEsdPgvn2OwMaZAwqYDeVqX0ewUAcA3Fqel7YFgpQgAIUoEBYCoRqwmexWLBkyRL84x//wPfff4+0tDRceeWVWLRoEbKzsz223LBhA9avX49t27bJn7KyMvTu3RvHjrmytXV12O12/PGPf8TLL7+Mb7/9FgkJCbjkkkuwcOFCDBw40OP6258YKk+fG+zhhXJD8MJDqNzxAeoObXf7YqiJiYO20Qj7dxVAo9hk+4dI//HPuixZvKokEz/IwNx+WMsPwelo7PT8GFOmKzBnzoMxfTB0sYketpinUYACFAgfAaejCfWnvpQr3+qKd8Jpt7k1XqOLhSnrQlfGU/NQaLT6kHfO0WhD9Z5PULH5HdhrXfu6iUNjiJOvq6aMvga6+CS3dp6q3YZ9p56D2D9OHDlJl2Fgxq0dgokOiwUFCx9CY/FJeV7GzT9H8pTOV9CFHELhBqh9HsFAXJsBV/tgKXxvsjgKUIACFKAABRQSCMUcwmq1yqDXli1bkJWVhUmTJsnAmQikZWRkyD8/77zzPOrh8OHDsWfPHrdzuwvEORwOzJgxA2vWrEFKSgouvfRSGcD77LPPYDQa8emnn2L0aN82iA6Fp0dQPp7kbGpEzcHNMnteQ/FRt1L0KWb5Zavxu3JUffi+/Mw4YBB63fsQNLp2qVLPUn9z4gcRlKs/W+IHaBCb3FtmABTBOWOP/tDqz565z8du8zIKUIACARcQv5SwlB10ZTw9sRWOxtaECLJyjQ7xPYfJlW+mrHzV/n3naLKhZu96GZBrqi5vcdPExMqEDiljroHe1Jow4rTlIL4sehIis6o4zKbRGNLzN9BpDW7mthOFKHj4ITgbGgC9HjkPLkDcef0CPi6hrkDt8wgG4hiIC/UzwvopQAEKUIACYS8QignfnDlz8Mgjj2DcuHFYt26dXI0mjqVLl2L27NmYPHmyXOXmyXHfffchOTkZo0aNQk5ODgYPHtztirjly5fj9ttvx/nnn4/PP/8cPXu6NrV+6623ZICuX79+OHjwIPR671cchMLTEydvz2mqrUTV7o9Rvfs/sNdVuV1u7D1Yvn5k6jcS9Xt2oejZp+TnupRU5C5YAn2Kbxn6miux2+pgKfsKltJ9qC/ZL19r7fTQ6BDX4wLXa6wZeWcSP3g/Zt7a8HwKUIACvgqI1cUNVcfOZDzdDLv1dIei4noMdGU87TUmrFYBO+1NqN63ARVfvI2mqtZMrhq9AUkjfoDUsddBn5Aq+1vTcBw7Ty6Bze56tTXVOEhmVI3RuSd9qNmyCaeWPSfP0fdIR+6CR6FLdF9l5+tYqPU6tc8jGIhrc+eofbDUepOzXRSgAAUoQIFoFwj2HMJms8FsNqOqqgqi7hEjRrgNwbBhw7B3717s2LED+fn5Xg1PcXGxXGHX3Yq4QYMGyUCbWBE3depUtzquv/56rF27Fm+++SamT5/uVf3i5GB7et3Abi6wFn0nV7+JVXCwN7WcrdHFIDFvotyMO9bcW/55Y8kpFMx/AA5LPaDVIvv3c2Hs7/trvV01rclyWq6Us5S4Vsx19sVVXKvRG2FMZ+IHpe8JlkcBCvgvYKstQm3hF/LV085+uWBI7iNXviXkjENMfLr/FYawBBGQqzmwERWb1qCx8pTbvyNJw6cgdewPoU/qgfrGEuw6+SjqG4vlOYmG3hjZ637E6l3BuuajdNUKVP3nQ/mfxryh6DXrfmi0wd0XL5icap9HMBDX5m5Q+2AF88ZlXRSgAAUoQAEKeC4Q7DmEeO1zypQp6Nu3r9ybrf0h9oibN28e5s+fjwULFnjeEQCeBOKOHj0qX3sVr6CKYGBMTIxbHStXrsTNN9+MmTNnYsWKFV7VL04OtqfXDezkAqfDjrrD21G5/QO5D1zbQ5eQiuT8y5E8/FK3vX4cNhtOPDIPDcdde/H1+J8bkXrVdUo056xltCR+OBOYs5QdgKPR9XpT+0MXKxI/DEZ8xhAYzUz8EPDBYQUUoICbQJO1ArWFm2XShYaKIx109CazDL6JH0NSTsTpiX9bag5sQsUXa9B4uqi1fzo9koZejNRx18OZEItdJx9DdcN38nOj3oz8Xg8i3pDZcr6zqQknHlsI67ffyD9LvX46eky7IeK8mjuk9nkEA3Ftbj21D1bEPiXsGAUoQAEKUCDMBYI9h3jmmWdwzz334IYbbsAbb7zRQe+9997Dtddei2kFu3fiAAAgAElEQVTTpmH16tVe6XoSiHv77bdl2eJVVrEnXfvjwIEDyMvLkyv1hI23R7A9vW1f2/Ptllq50XbVznVoqi5zKyq2Vz+Z+S6h/2hodB1f9yx5+a+o/uwTeY0pfxQyfzMrJBntvEv80BPGjCFnEj8Mgi42sl9v8ufe4LUUoIBvAuLV+rqT22TwTWSJBpxuBYlfEIhVbyL4FpvaLyR/b/rWM9+vcjocqD24WQbkbGWFrQVpdUgaMhmJY6/AV5ZVKLfsk5/F6JKQn3U/kuJa94ptOl0uV2Dba6rlOVn3/B6mYe4r6n1vobquVPs8goG4NveL2gdLXbc2W0MBClCAAhSgQLNAsOcQs2bNwtNPPy2DcWJPuPaHSLwgEjCMHDkSO3fu9GqgPAnEiUypd911V5eBPrFKTiRwEFlcy8tbN532tCHB9vS0XW3PE1+EKnd8iJp9n8HZ1CYzn1aHhAFjkHLhVYjLPr/Loqs/X4+Sl5a5vjD1zETO/Eehi3ff18eXdilxjdeJH8T+ciIja48Bqt0IXQkXlkEBCgROQPy9U1+8y5Xx9NRuwNH6Wr+oVas3wtRrjNz3zZg+CBqt58lsAtfq4JcsfnFSd2gbTm9cDVvp960N0GhhypuAU0PrUGJz/QJMp4nD8KzZ6BE/pOW8+q/24eSTjwJOJ7Qmk9yTNCbDHPyOBLhGtc8jGIhrcwOofbACfK+yeApQgAIUoAAFfBQI9hzil7/8JV588UU89NBDWLx4cYdWi9dVRRIF8XP48GGveuVJIO7RRx+Vdd94441YtWpVh/Kbmprk66riR+xnd7ajqKgI4qftIfaeu+mmm2QQUQQT1XKIL0D1R/agcvv7sBxzrTpoPrTGRCSP+AGSR14GfWLaWZvc8P1xFC6aA2djIzQxMciZuxix57j2jFPj0Zr4YT/qS/adPfFD2gWu1XJM/KDGoWSbKKAqAfHapcj0LFa+1Z7cDmeTxa19Gm0M4jNHyJVv4n+1OveMoKrqTJAbIwNy3+xExca30HDKtb2BOJwaoOaSLJT1cP27qoFOZlPNTBzXcs7pf7+N02/+Q/53bJ/zkP3gAmgNkWUb7HmZt8PPQFwbMbUPlreDy/MpQAEKUIACFAiOQLDnEJEUiBN72C1cuLDTgVJLIM7RYJFZ7Kp2fuS+Rw8Ag/kcufotYdAEaGO6/yJjr69H4cIH0XjKtbG2+ed3IGnSxcG5URWqxfPED3Fy5UpzRlZDUm5UvEKmEDOLoUBECsiMpxXfypVvtSc2w97gnlFahI5EIF+sfDNljYLOYIpIB6U6JTzrv92F05tWo6HItYeeeJG3qj9wemhzLRoMSJ+Jc1KudH3ucKDoj39A/ZeuFfNJF18K8y23K9UkVZQT7HmZt51mII6BOG/vGZ5PAQpQgAIUoEA7gWBP+CLp1dRgr4hrOHUctYe3wWGthzYuXu7f1pzBtP2N3VhxCpU7P0LN3k8hgnGthwamCy6U2U+N5wzyOLgkvjAVP7cUdbu2u778XDQF5tt+GdbPkyvxQxHqS/fJjKyWsq/gaKzrtE+tiR/Eq6xDEBOfEdZ9Z+MpQAHPBWzVhXLlmwjANdWXdLhQ7PWWmDNe7v2mj3PP+Ol5LdF7pgzIfbcHp8UKuZOuhAw1fYBSkTj9THLU81KnoW/aj+W/Wfa6OhQseABNpa6xCMdfCp1ttIM9L/P2zmMgjoE4b+8Znk8BClCAAhSgQIgDcUzW4P0taDtdjJL3l8Fa8HWHi+NyB8B89R0wpGVCfJmxfP8VqrZ/IF/7abtJuDbWiKRhU5CcfwViUrzfU6fig3dR/s/XZP2xvfsg+6GHI+51oObEDyIoV1+6H9byr+F0NHY6YDEmkfghD/HmIXLlHBM/eH9f8woKqFmgsb4MtYVfyACcrep4h6bGJPRCYu5EJOSMhyGhNcOnmvuk9rbJf8OO7cfpTW/Jf+/qsoCScYDzzJZ6mfpRyOt9N7QanczYXbh4bus2CXMWyX+bIuFgIC6MRlHtgxVGlGwqBShAAQpQIKoEgj2H+PTTTzFlyhT07dsXYj+49seiRYswb948zJ8/H+LVT28OT/aIO3r0KM477zwYjUaIxAxiL7i2x8qVK3HzzTdj5syZWLFihTfVy3OV9rSdLkLhq/PgsNR02RatMQEpo6+RWelsJW02wBbJFFIz5eo3kZlOBON8OSyHDuLE44sAhwPaeLFB9qOIMff0paiwukYmfjh9WK6WE6vmGiq+65ABsblDhuQ+rtdYmfghrMaYjaVAWwF7QzVqT26VK99EIL79oTemISFngtz3zZDc2+MVxVT2XqD++AFUbFqNitoDKJ4IOM7snpBYmYyhWXfDlD0QbRMH6TPMMnmDzhT+rwMrPY/wXv/sV3BFXBsftQ+W0oPP8ihAAQpQgAIUUEYg2HMIkQDBbDbLINju3btlhtS2x7Bhw7B3717s2LED+fnivRTPD08CcaK0QYMGQSRVWLNmDaZOnepWwfXXX4+1a9fizTffxPTp0z2v/MyZSnsWrlrQ6Uq47hpmPHeo3P8tvu8waDRn3u3p7qJOPm+qrETB/Pthr6qUn2bddS9MI7wbFx+qVeUlbokfSvejseZE5+3U6BDHxA+qHEM2igLtBRxNVtQV7ZDBt/qSvYDT7naKNiYBCdlj5b5vcT36+/X3KfW9F7AUHMTJHa/jWN9vYD+TnDuuFDj3RB7Sx96Amo83oHrDJ7Lg+OH5yPrtbGi0vv+b530Llb9C6XmE0i1kIK6NqNoHS+nBZ3kUoAAFKEABCigjEIo5xJw5c/DII49g/PjxWLduHUxnfoO9dOlSzJ49G5MnT8b69etbOvj8889D/EybNg1LlizpsuOeBuKWL1+O22+/XWZm3bhxowwMimP16tUy+NavXz8ZqNPr9V4jK+kp9oQrePn3nrdBHyNXvokAnCE92/PrujjTabfjxBOLYT10UJ6Rcs31SL/hp36XGykFtCR+KBUZWffDbj3dadc0+jgYeww8k5F1CJj4IVLuAPYjXAWcjibUn9ojXzutK9oJp73BrSsanQGmrAtdGU97DoNG6/2/BeFqo9Z2VxZsx5dVz8MW5xorQyWQ+TmQaB6Epm8r0HjUlWk1bcZPkHat+y/Y1Nqnrtql5DwiEH1nIK6NqtoHKxA3AMukAAUoQAEKUMB/gVDMIaxWKy6++GJs3boVWVlZmDRpEo4fPy7/OyMjA1u2bJGvjzYfzdlJO3tdVATVxI84Ghsb5auhBoMBI0aMaLn+z3/+M0aOHNny3w6HAzNmzJAr4lJTU3HppZeirKwMGzZsQFxcHMTrs2PGjPEJV0nP8s//hYqNb3ncjpSxP0T6JT/z+PzuTix743VUvr9WnmYcOBi9fvcgNLozm/V0d3GUfd6c+MEig3L7PE/8kJGHGJP3e/ZFGS+7SwG/BcQekNbyQ2cynm6Bo7HWvUyNDvHmoWcynl4IrT7O7zpZgLICNns1dh5fhBpHgSxYXwdkfgYYxFA2aIESB2ABet37EOIHDVG28iCWpuQ8IhDNZiCujaraBysQNwDLpAAFKEABClDAf4FQzSEsFotc3fb666+joKAAaWlpuPLKKyH2iMvJyXHr2NkCcc2fnU1CBNZE4K/tYbfb8eyzz+Lll1/GkSNH5Kq8Sy65BAsXLpSvrvp6KOlZ+vErqNrxgcdNSb7wKmRcNtPj8892Yt3uHSh69il5ii4lFbkLl0CfnKJI2dFQiDeJH/QmM+IzhriSP2QMZuKHaLhB2MegCIgAua3q2Jng2xcQq1jbH3E9BsjgW0KvMXz2gjIq/lXS5LBib/EzKKv/UhakbXCtjIurOFNuPaC1xCP3gScQ0yPdv8pCdLWS84hAdIGBuDaqah+sQNwALJMCFKAABShAAf8FOIfw37BtCUp6ersiLm3iDKRNmuF3hxpLilEw/0E4LOIbjRbZv58LY/+BfpcbzQW4J37Yj4aKI90kfhgMo8jI2mMAV+ZE843DvvskYKstRm3hJhmAa6w92aEMkWhBvHYqMp7GxIdnsMYnmAi5yOFswoGSZSiq2egKxtm1MG9yIP5Uawc1zjj0nPEbmM7PD7ukGkrOIwIx5AzEMRAXiPuKZVKAAhSgAAWiSkDtE75wGwwlPb3dIy73548j1tzbLzKHzYbCxfNg+/6YLKfH/9yE1Kuu9atMXtxRwJX44SDkq6yl+7pJ/HA+4kVQLiMPcal9uV8VbygKdCLQZK1EbeEXct83V6Db/dDHm10r33LGIzYpl4ZhLiBWHR8ufw3HK9+TPdFAi5zCvojZ/I1bz2Izz0PaxB8hvl/4BOSUnEcEYpgZiGMgLhD3FcukAAUoQAEKRJWA2id84TYYSnt6mjU1LncAcm5a4DdXyct/QfVnn8pyTPmjkfmbe8JuNYHfCCEoQLwyZyk9IINyIjjX2St08sumW+KHPBiSzuH4hGC8WKU6BOyN9ag7uU2ufBPPDeB0a5guNhkJ2eNkAC42tR+fFXUMm6KtOFbxrgzINR/n6a6CZsUncMY2iOhcy2Ew90baxOkwXXCh6jPfKj2PUBRc/DvkFC9985ACah8sDhMFKEABClCAAuoU4BxC2XFR2tN2uhiFr86Fw1LTZUO1xkTk3LwIhrRMvzpT/fl6lLy0TJYR0zMTOfMfhS4+3q8yebH3Am6JH0r3ywCdo7Gu04JEoMGYMfjM/nJM/OC9Nq8INwHxmnd98W658k38r9PR6NYFjd6IhF6j5KunYhWpRssEM+E2xt6292T1Z/JVVScc8tJcXAT9UxuhSXYAyXK5XGtALiMXqRN+hIT+Y6DRar2tKijnKz2PULrRDMS1EVX7YCk9+CyPAhSgAAUoQAFlBDiHUMaxuZRAeIpgXMn7y2At+LpDY8VKOPPVd/gdhGs4fgyFi+fC2dgIjcGAnLmLEJvr32uuyspGb2ktiR9kRtb9sJZ/3SH40KzjSvyQB2PGECZ+iN5bJuJ67nTYYSk7IFe+iRVwjiaLex+1eph6jpAr3+IzR0KrM0ScATt0doHSut3YU/w0HE6bPDGj9nwkPPsNNHpAmx0Ph7EBcNhbConpkY208dOQMGic6oK1gZhHKHn/MBDHQJyS9xPLogAFKEABCkSlgNonfOE2KIH0bCg5jrpD22G31kEXZ4Kp/yi/94QTvva6OhQufBCNJa6drs2/+H9Imjg53Oijpr2uxA/fwFKyD/Wl3SV+6H0mMJcHY/pAJn6Imrsk/DsqVoY2VHwrV77VFm6GvaGqXac0cjWoWPlm6jUaOoMp/DvNHvglUGn9BrtPPo5GR60sJ6ksDWnLT0PbBMTnj4D+ggxU7/kEsDe1BuRSM5E6fhoS8yaqJiAXyHmEX8BnLmYgjoE4Je4jlkEBClCAAhSIagG1T/jCbXDCzVN82S1+binqdm13fXGZPAXmW38ZbuxR3V6xT5a17Cu5Wk7sk2WrKezcQ6NDXNr5rsCceQgTP0T1XaPeztuqT6CmcKMMwDXVlXRoaGxqX1fG0+yx0BvT1NsRtiwkArW2E9h18lFYm8pl/cZTsTCvbIDOCqT/9H+RMH4cKra8i+ov/wNnU+trzfoUM9LGT0Vi3kXQ6PQhaXtzpWqfRzAQx0BcSB8QVk4BClCAAhSIBAG1T/jCzTjcPCvefxflb7g2uo7tfS6yH1oIrYGvdYXbfde2va2JH0Rgbp8XiR9yVb+JeTiPC9vetUBjfZlc9SaCb7YqV8bmtkdMQhYScyfKjKeGhCxSUuCsAtbGMuwsegx1NtcvJQxlGmS+5oS+Tovs38+Fsf9ANNVWonLru6ja9TGcTa7XWcWhT05H6ripSBoyGRp9TId6RDbz2sPb4LDWQxsXj4T+oxVZmd62IrXPIxiIazNaah8s/l1BAQpQgAIUoIA6BTiHUHZcwsnTcuggTjy+CHA4oI03IXfhEsRkmJUFYWkhFfAq8YMhCUaz2F8uT66aizHxXgjp4EV45faGGtSe3Cr3fbOWH+zQW11cKhJzxiMhdyJik/sw42mE3w9Kd6/RXovdRU+g0npYFq2rArJeB4z2VOQuWAJ9Sor886a6KlRuew9VOz+Cs7GhNSCXmIaUcdcjadgl0OoNCMZerc2Vq30ewUAcA3FKP68sjwIUoAAFKBB1Amqf8IXbgISLZ1NlBQrmPwB7VaUkzrr7XpiG54cbN9vrpYAr8cMxuVJO7C9nLfMk8YMrMKeLTfKyNp5OAXcBR5MVdUU7XRlPT+0BnK2b54sztTEmJGSPQYLIeJo+kCs0eQP5JWB3NGBP8TMoq98ty9HWA5n/AFISByL7vjnQ6Foz6trrq1G5/QNU7vgQTltrMhBdQiqShk5G1e7/wGFx7T3X2aFU9nJRttrnEQzEMRDn14PJiylAAQpQgAIUUP+EL9zGSO0TaOHptNtx4onFsB5yrUJJvfZ69Jjx03CjZnsVEGhJ/CAzsu5DQ8URcYd0WrIhmYkfFCCPuiKcjibUl+x1ZTwt2gGnvXXVkcDQ6AwwZea7Mp6ah0Gj6/g6YNShscOKCTicTfiq5EWcrNkgy9TYgJ5vAb36X4f0H9/YoR67pRaVOz5A1fYP4Gio96odIot5zk0LvLqms5PVPo9gII6BOL9vchZAAQpQgAIUiHYBtU/4wm18wsGz7I3XUPn+u5LWOHAwev3uQbeVAeFmzvYqJ+Bd4od+iM8YIl9ljUvrB402tBucK6fAkvwVECsvreWHXBlPT2yFw1bjXqRGi3jzUFfG06wLoY0x+lslr6dAlwLiFf1vyv+OY5VrXefYgYx/A+dfPAsJ+aM7vc5urUfVzg9RsWUtnDarx7q5P3/c7z3j1D6PYCCOgTiPHwieSAEKUIACFKBA5wJqn/CF27ip3bN21w4U//EpyapLSZX7wumTXXvl8KBAewH3xA/70WRxZSJsf2h0sTCmD3LtL2fOgyGJiR+i7W4SwQ5b9feoKdiI2sIvOr1X4tL6y5VvIuMpX3WOtjsk9P09XvkeDpWtbGlIj/V6DLn2CRgye3XZuLJP/47KLe943Pi0iTOQNmmGx+d3dqLa5xEMxLUZNbUPll93Ii+mAAUoQAEKUCBgApxDKEurZs/GkmIUzH8QDks9oNUi+/55MF4wQFkAlhaxAjLxQ10xLCWu/eUspQfgaKzrtL86kfghY/CZwNwQJn6I2LsCaKw7hZrCL1BbsAm2GleWyraHIekcufItIWcc74MIvg/CpWtFNRuxv/hPcGpcr+Cn7U/EiCufhS4uvtMulH78Cqp2fOBx95IvvAoZl830+HwG4vyiCv3Fap70hV6HLaAABShAAQpQoCsBziGUvTfU6umw2VC4aC5sBcdlh3v85H+ReuU1ynaepUWVQGvih/2oL90nX0V02m2dGujjzXKlXHNGVq6GCu9bpclaidoTW+S+bw0V33TojD4+/UzwbQJik88J786y9REnUFr3JfYUPAGH3iH7llqUgZETlkKn7bg/Yfnn/0LFxrc8NuCKOI+pIuNEtU76IkOXvaAABShAAQpErgDnEMqOrVo9T720DDWfr5edNV04Gpm/vgcajUbZzrO0qBZw2hthOX0YFpn4YT8aKo8ATtcX3fYHEz+E363iaKxH7cntct83sSqyfVIPrSERiTnjZMbTuLQL+PdL+A1xVLW4ouoAdhU8Anuc6++oZEsO8gcvhl4b5+bQcOo4Cl7+vcc23CPOY6rIOFGtk77I0GUvKEABClCAApErwDmEsmOrRs/qzz5Fyct/kR2N6ZmJ3AWPQmvs/DUcZTVYWjQLtCR+EK+xluzv9LVF6aPRyWQPrtVyQ5j4QUU3jciqW3/qS7nyrb54F5yORrfWafRxSMgahQSR8TQjjwk7VDR2bEr3ApUFu7Cr5Ak0JbvOTUQO8s+dD4Mu0e3iwlULYC34utsCmTW1W6LIO0GNk77IU2aPKEABClCAApEnwDmEsmOqNs+G48dQuHgunI2N0BgMyJm7GLG5fFVM2VFnaZ4INFkr5L5yYrWcpXRfN4kfBsKYMYSJHzyBVfgc8cqxGCex8q3u5DaIlXDu0TcdTJkj5Mo3U+ZIaPWxCreAxVEgeAJl29ZhX9PLaDS76ozXZSI/Zw6MMektjbCdLkbhq3PhsLTL/tummVpjInJuXgRDWqbfjVfbPKJ9h5isoY2I2gfL77uRBVCAAhSgAAUoEBABziGUZVWTp72uDgULHkBTaYnspPn2O5E04SJlO8zSKOCDQMfED1/B0VjbaUlM/OADsJeXiPFoqPzuTMbTzbA3VLYrQSOz4oqVbwm9RkNnSPCyBp5OAfUKFP3jRXxj/i+sZ35HFatLQ36vB5AQm+sWjCt5f1mnK+PESjjz1XcoEoQTFappHtHZqDEQx0Ccep9mtowCFKAABSgQJgJqn/CFCWNLM9XiKb5YF//xD6jbvUO2LeniS2G+5fZw42R7o0TAPfHDfljLvz574oeMPBjN4lXWPDDxg+83ia3mREvGU5ERt/0Rm3KeXPkm9n7TG9N8r4hXUkDFAs6mJhQ8OR/fDz2C+v6uhuq1JozIug+pxjN/cKb9DSXHUXdoO+zWOujiTDD1H4VYc29Fe6eWeURXnWIgjoE4RW94FkYBClCAAhSIRgG1T/jCbUzU4lnx/lqUv/G65IvtfS6yH1oIrcEQbpxsb5QKtE38IJI/WCvOkvgh6RzEm4fIPeaM6QOh1btvth6lhF12u8lSjprCzagt3ISGyqMdzosxZcqVb4k5E2BI7EU+CkSFQFPFaXw///c4NakGNSNcXdZqYjA0826YTflBNVDLPIKBOA+GXe2D5UEXeAoFKEABClCAAiEQ4BxCWXQ1eFq+/gonHl8EOJ3QmkzIXbAEMRlnNsBRtrssjQJBERD7lFnKDqK+dF/3iR9S+7WslotLO58JBADYbbWoPbFVBt+EY/uMp7q4VCRkj0Ni7gSIVXDMqByU25qVqEyg/qv9OPHkYlRcBFROcjVOAy0GmX+J7KSL5X/XNBzHqdptaHLUQ6+NR8+E0UiM5Yo4lQ1l8Jqjhklf8HrLmihAAQpQgAIUUEqAcwilJF3lhNqzqbICBfPuh726SrYn6+77YBo+UtlOsjQKhFjA+8QPeXLVnCEpFxqNNsStD071jqYG1BXvRG3BJtSd+hJw2t0q1sbEI6HXGLn6Tez/Fi0uwdFnLeEqUPHvd1D+5t9RdSFQfoWMxMmjd8q1qLJ+i0prx+ypKXEDkGe+A/EG/xM1qGEe0d3Y8dXUNkKhnvR1N1j8nAIUoAAFKEABdQpwDqHsuITS02m348QTi2E9JFa8AKnXTkWPGT9RtoMsjQIqE2hJ/FC6/0xG1gNdJn7QGhIRnzG4JSNrjKmnynrjX3OcjibUl+yTK99qT26H097gVqBGGwNTVr4r42nP4dDoYvyrkFdTIMIEnA4Hip8T+6vuRO0goHSqBk6ts9texmgTMSZnkSLBuFDOI7rtqIhNOsXfujykgNoHi8NEAQpQgAIUoIA6BTiHUHZcQulZ9s/XUPnBu7JDxoGD0eveh6DRRsfqH2VHkaWFs4BM/FB1XL7CKl5lPXvihwzEZwyRr7IaMwZDH5scdl0X/bWe/kaufKs5sRkOW417HzRa2UeZ8TTrQoiVcDwoQIGuBUTG8cKFD6Kx5BTqzwWKf6YR0aduycTKuNE5C7o9r7sTQjmP6K5t4nMG4tooqX2wPBlQnkMBClCAAhSgQPAFOIdQ1jxUnrU7t8vf4otDl5KK3Icfgz4p/IIKyo4GS6OAeCOzEdaKb+RKMU8SP4ikDzL5g8oTPzRUfY8asfKtcBOa6ss6DLXYH0+sfEvIHgt9XApvBQpQwAuBhu+Po3DRHFhTG3Hil55fOC73cb/3jAvVPMLTXnodiLNYLFiyZAn+8Y9/4Pvvv0daWhquvPJKLFq0CNnZ2R7Vu2LFCtx6663dnvvKK6/g5ptvdjvPbrfjj3/8I15++WV8++23SEhIwCWXXIKFCxdi4MCB3ZZ5thPUPlh+dY4XU4ACFKAABSgQMAHOIZSlDYWn7VQxChc8AIfFAuh0yL5/Hozn91e2YyyNAhEi0Jr4Yb8MzNmqCzrvmUaHOJUlfmisK0FN4Rcy+NZZuw2JOWcyno5HpL12GyG3H7sRRgLVGzfg68MvoPIizxvdN20GxI8/RyjmEd6016tAnNVqlUGvLVu2ICsrC5MmTcKxY8ewbds2ZGRkyD8/77zzuq1/48aNWL58eafnVVVV4e2335afHTlyxK08h8OBGTNmYM2aNUhJScGll16KsrIyfPbZZzAajfj0008xevTobuvv6gS1D5bPHeOFFKAABShAAQoEVIBzCGV5g+3psNlQuGgubAXHZUfSf/q/SLniGmU7xdIoEMECTdZKGZCrL90vX2dtsnRcXSa6r9HFylVycsVcRh4Myed4neBAvDJbe3IbRDDQlSxhNGKTz55xsamhCrUntshXT62nD3cYCb0xHQk542XGU0OSaNOZ3eUjeMzYNQoES2DXhntRlt1FsL6TRpyTfBUGZMz0q3nBnkd421ivAnFz5szBI488gnHjxmHdunVyNZo4li5ditmzZ2Py5MlYv369t21wO/+FF17AnXfeiQkTJkAE7NoeInh3++234/zzz8fnn3+Onj1dG4O+9dZbMkDXr18/HDx4EHq93qc2BHKwChuO48vabbA46mHUxmN4wmjkKJyi16dO8yIKUKBbAT6/3RLxBAqoWiAYz3Ag5xCqxg1Q4wLpKV6Vqd25DY76emjj45Fw4WhUrvsANZ+75rCmC8cg89d384t4gMaWxUa+gCvxwylXYE6+yupJ4gfXq6xnW4Fmqy1Gya5lcr+69kdcjwEwj7wDhoTWjIuORgtqi7bLlW+iHXA63C4TSSfEK6eJORMQ1+MCrwOCkT+S7CEFlBH4puQfOFrtWmzlycEVcW2UbDYbzGYzxIo1MTkaMWKEm+GwYcOwd+9e7NixA/n5+VkPLf4AACAASURBVJ74dnqOCMB98cUXWLZsGX71q1+5nTNo0CAZaBMr4qZOner22fXXX4+1a9fizTffxPTp032qPxCTvhJbMVaVLMO3naTo7Rc3ADeZ74BZoRS9PnWaF1GAAl0K8PnlzUGB8BYI5jMciDlEeOv71/pAeIpXT0teWgbr4Y5f4ptbG5OZhdz5j0Br5Ebs/o0gr6ZAq0DbxA8iOGcpPwin3dYpkT5eJH7Ig1HsL9cm8YOttgiFG+Z1TKLQphQRWMuZOB+2umLUFm5EXdFOOB2NbvWIFXmmXqNk8E0E/jRa3xZwcHwpQAHPBWoajmNzwe89voB7xLWhEq99TpkyBX379pV7s7U/xB5x8+bNw/z587FggW9ZLo4ePSpfRTUYDCguLkZqampLNc2fiVdQRTAwJsY9TfTKlSvlfnIzZ86E2IPOl0PpSV+JrQhPFs5DnaNd1p02jTNpE3FvziIG43wZMF5DgQAK8PkNIC6LpkAQBIL9DCs9hwgCkaqrUNrTVlyEwsXz4Kjtek4mQDLvvg8Jw0eq2oaNo0C4C7QmfhD7y+2DteJIh9VqzX0Ur4mK11gtJXthqynsvusabceyNDrE9xwuXzs1ZY6EVh/XfTk8gwIUUFRg4+ZbUZ9h6bbM+FIjJo77W7fndXeC0vOI7urz9nOPX0195plncM899+CGG27AG2+80aGe9957D9deey2mTZuG1atXe9sOeX5zMK+zMsS+ceLPR40aJfeka38cOHAAeXl5cqWeQPflUHqwlhYu6HQlXPu2iZVxsxRI0etLn3kNBSjQuQCfX94ZFAhvgWA/w0rPIcJb3//WK+1Z+OiCs66Ea25x3AUDkPOgb79Q9r/XLIEC0SngceIHr3g0iEsfIFe+JfQaA11soldX82QKUEA5AbElxHdP/x4nbgEcpq7L1dYB2SuA82Y9jtjcs+/72F3rlJ5HdFeft597HIibNWsWnn76aRmME3vCtT/27NmD4cOHY+TIkdi5c6e37ZDn9+/fH4cPH5aBPBF0a3uITKl33XVXl4E+sUpOJHAQWVzLy8t9ql/JwRL70TzqxfLLB3Mf555xPo0aL6KA8gJ8fpU3ZYkUCKZAKJ5hJecQwbRSa11KeoovAAXzPH8lJneR/18A1OrKdlEgHAQ8TfzQVV/Ea63mEb9CTHx6OHSXbaRAxAuUr/kXKt55C42pQOl1gPWcjl2O+x7IeBeIqQDSps6QP/4cSs4j/GlHV9d6HIj75S9/iRdffBEPPfQQFi9e3KE88bqqSKIgfkQwzdtDrHIbM2aMDKQVFRXJ11PbHo8++qis+8Ybb8SqVas6FN/U1CRfVxU/Yj+77g5Rh/hpe4j952666SYZSBQBRX+Of5f/C+9XvOVxEUm6FCTrW1/F9fhCnkgBCiguUNVUgWp7pcfl8vn1mIonUiAoAt4+w9ekzYD48edQ+4TPn76F4lolPZu/AHjaDyW+AHhaF8+jAAXOLiASP5TsegE133/mMVVy36uQMdS/jIseV8YTKUCBbgVKX3sFVR9/0HJegxmo7w/Y4wCdFYg/BMSWtBaTfNlVyLjRv2dYyXlEtx304QTVBOJ++9vf4rnnnsMdd9wBkTm1/aF0IE7sY7dw4cJOyZQIxP2r9BV8WtV6s/kwNryEAhSgAAUoQIEgCFySfBVuyIjsCV8QGBWtQskJdPsvAN01VIkvAN3Vwc8pQAHPBcoP/gsVX3u+wCFtwAykDfTvlyuet45nUoAC3QmE4hdiSs4juuufL597HIgL5KupYjVbdnY2SkpKsGnTJowfP75DX5R+NZUr4ny5XXgNBaJDwNvVNFwRFx33BXsZPgLePsNcEae+sVVyAh2KLwDqE2WLKBC+Ag1Vx1HwiRevl095HLHJ/u0vFb5abDkF1CcQii0ilJxHBELU40BcIJM1vP/++7jmmmtkxtQjR4502s9wS9YQiv1pAnGDsEwKRKMAn99oHHX2OZIEQvEMq33CF27jq6RnKL4AhJs320sBtQsUfrYA1vKvu21mXI8ByLmICVe6heIJFAiyQLCTJik5jwgElceBuE8//RRTpkxB3759IfaDa380ZzydP38+xGuf3hw/+9nP8Pe//x3z5s3r8nXRo0ePykCd0WiESMwg9oJre6xcuRI333wzZs6ciRUrVnhTfcu5Sg9WsDO2+dRpXkQBCnQqwOeXNwYFwlsg2M+w0nOI8Nb3v/VKewb7C4D/AiyBAhRoK2CrLUbhhrlw2Gq6hNEaEpEzeREMCZnEowAFVCZgO1WMwkVz4ag9yzOckIicuYtg6On/M6z0PEJpTo8DcSIBgtlslkGw3bt3ywypbY9hw4Zh79692LFjB/Lz8z1uZ21tLXr27In6+nqZ5EEke+jqGDRoEERChTVr1mDq1Klup11//fVYu3Yt3nzzTUyfPt3j+tueqPRgldiK8WThXNQ5ur7ZTNpE3JuzCGaD/zebT53mRRSgQKcCfH55Y1AgvAWC/QwrPYcIb33/W6+0Z7C/APgvwBIoQIH2AiIYV7JrWacr48RKOPPIOxiE421DARULiH+LS15aBuvhjqtb4y4YAPPP71AkCCcIlJ5HKM3qcSBOVDxnzhw88sgjcg+3devWwWQyyfYsXboUs2fPxuTJk7F+/fqWNj7//PMQP9OmTcOSJUs6bfurr74qV7GNHTsWmzdvPmv/li9fjttvv10G6zZu3CgDg+JYvXq1DL7169dPBur0er1PToEYLPFFYFXJMnxr7Xiz9YsbgJvMdzAI59No8SIKBF6Az2/gjVkDBQIpEMxnOBBziEDaqL3sQHgG8wuA2n3ZPgqEs4DYM67u5HbYG+ugizHB1GsU94QL5wFl26NOoKHgOOp2boe9rg46kwmm/FGIzVV2X8dAzCOUHCivAnFWqxUXX3wxtm7diqysLEyaNAnHjx+X/52RkYEtW7bI10ebj+bMpGd7XfTyyy/Hxx9/jD/96U+48847z9o3h8OBGTNmyBVxqampuPTSS1FWVoYNGzYgLi4O4vXZMWPG+OwTyMES+9XsqduOensd4nUmDDONQk6ssjebzx3nhRSgwFkF+PzyBqFAeAsE4xkO5BwivPV9a30gPYPxBcC3XvMqClCAAhSgAAWUEAjkPEKJ9nkViBMVWiwWubrt9ddfR0FBAdLS0nDllVdC7BGXk5Pj1qbuAnEic2lubi60Wi3E/+/Ro0e3fbLb7Xj22Wfx8ssvy8QOYlXeJZdcIveWE6+u+nOofbD86RuvpQAFKEABClAgcAKcQyhrS09lPVkaBShAAQpQIJoE1D6P8DoQF8mDp/bBimR79o0CFKAABSgQzgKcQyg7evRU1pOlUYACFKAABaJJQO3zCAbi2tyNah+saHpw2FcKUIACFKBAOAlwDqHsaNFTWU+WRgEKUIACFIgmAbXPIxiIYyAump5H9pUCFKAABSgQEAG1T/gC0ukAFkrPAOKyaApQgAIUoECEC6h9HsFAHANxEf4IsnsUoAAFKECBwAuofcIXeAFla6Cnsp4sjQIUoAAFKBBNAmqfRzAQx0BcND2P7CsFKEABClAgIAJqn/AFpNMBLJSeAcRl0RSgAAUoQIEIF1D7PIKBOAbiIvwRZPcoQAEKUIACgRdQ+4Qv8ALK1kBPZT1ZGgUoQAEKUCCaBNQ+j2AgjoG4aHoe2VcKUIACFKBAQATUPuELSKcDWCg9A4jLoilAAQpQgAIRLqD2eQQDcQzERfgjyO5RgAIUoAAFAi+g9glf4AWUrYGeynqyNApQgAIUoEA0Cah9HsFAHANx0fQ8sq8UoAAFKECBgAiofcIXkE4HsFB6BhCXRVOAAhSgAAUiXEDt8wgG4hiIi/BHkN2jAAUoQAEKBF5A7RO+wAsoWwM9lfVkaRSgAAUoQIFoElD7PIKBOAbioul5ZF8pQAEKUIACARFQ+4QvIJ0OYKH0DCAui6YABShAAQpEuIDa5xEMxDEQF+GPILtHAQpQgAIUCLyA2id8gRdQtgZ6KuvJ0ihAAQpQgALRJKD2eQQDcQzERdPzyL5SgAIUoAAFAiKg9glfQDodwELpGUBcFk0BClCAAhSIcAG1zyMYiGMgLsIfQXaPAhSgAAUoEHgBtU/4Ai+gbA30VNaTpVGAAhSgAAWiSUDt8wgG4hiIi6bnkX2lAAUoQAEKBERA7RO+gHQ6gIXSM4C4LJoCFKAABSgQ4QJqn0cwEMdAXIQ/guweBShAAQpQIPACap/wBV5A2RroqawnS6MABShAAQpEk4Da5xEMxLW5Gzdt2oSJEydi1apVGDhwYDTdp+wrBShAAQpQgAJ+CBw8eBA33XQTNm7ciAkTJvhREi8VApyT8T6gAAUoQAEKUMBXAbXPyxiIazOyr732mpxE86AABShAAQpQgAK+CIhf5t14442+XMprOCfjPUABClCAAhSggIICap2XMRDXZpDLysrw0UcfoU+fPjAajQoOv6uo5qgsV9wpTssCKRBwAT6/ASdmBRQIqECgn2GLxYJjx47hiiuuQHp6ekD7Eg2Fc04WDaPMPlLAd4FA/53ue8t4JQUo4IlAoJ9htc/LGIjz5C5R6By1v6esUDdZDAUiUoDPb0QOKzsVRQJ8hqNosD3oKu8HD5B4CgVULMBnWMWDw6ZRwAOBaH+GGYjz4CZR6pRov9mUcmQ5FAiFAJ/fUKizTgooJ8BnWDnLSCiJ90MkjCL7EM0CfIajefTZ90gQiPZnmIG4IN7F0X6zBZGaVVFAcQE+v4qTskAKBFWAz3BQuVVfGe8H1Q8RG0iBswrwGeYNQoHwFoj2Z5iBuCDev9F+swWRmlVRQHEBPr+Kk7JACgRVgM9wULlVXxnvB9UPERtIAQbieA9QIIIFov3fYQbignhzFxUV4S9/+Qt+9atfISsrK4g1syoKUMBfAT6//gryegqEVoDPcGj91VY77we1jQjbQwHvBPgMe+fFsymgNoFof4YZiFPbHcn2UIACFKAABShAAQpQgAIUoAAFKEABCkSkAANxETms7BQFKEABClCAAhSgAAUoQAEKUIACFKCA2gQYiFPbiLA9FKAABShAAQpQgAIUoAAFKEABClCAAhEpwEBcRA4rO0UBClCAAhSgAAUoQAEKUIACFKAABSigNgEG4tQ2ImwPBShAAQpQgAIUoAAFKEABClCAAhSgQEQKMBAXwGHduXMnPv74Y2zbtk3+nDhxQtbmdDoDWCuLpgAFlBCor6/HunXr8O6772Ljxo04fvw4dDod+vXrh+nTp2PWrFlISEhQoiqWQQEKBEhg6dKl8vndt28fSkpKYLVakZmZicmTJ+Pee+/FkCFDAlQzi1WjAOdlahwVtokCnglwXuaZE8+igJoFOC9rHR0G4gJ4p06dOhXvvPNOhxoYiAsgOoumgEICy5cvx+233y5LGzhwIPLy8lBdXY0vvvgCNTU1GDBgADZs2ACz2axQjSyGAhRQWiA9PR11dXUYOnQosrOzZfEHDhzA4cOHERMTg9WrV+Paa69VulqWp1IBzstUOjBsFgU8EOC8zAMknkIBlQtwXsZAXFBu0ccff1x+ARg1apT86dOnDxoaGrgiLij6rIQC/gm88sorMuh29913y0Bc81FUVIRrrrkGu3fvxk9/+lO8/vrr/lXEqylAgYAJbNq0Cfn5+YiLi3Or489//jN+/etfo2fPnigsLIRerw9YG1iwegQ4L1PPWLAlFPBWgPMyb8V4PgXUJ8B5GQNxIbkrxRcBBuJCQs9KKaCowObNmzF+/HjExsbKVXIGg0HR8lkYBSgQeAHxmvmRI0ewZ88euWKOR/QJcF4WfWPOHkemAOdlkTmu7FV0CUTbvIyvpgbx/uaEL4jYrIoCARQQ+5SYTCZZw8mTJ5GVlRXA2lg0BSgQCAGx0vXrr7/GwYMH5avmPKJPgPOy6Btz9jgyBTgvi8xxZa+iSyDa5mUMxAXx/uaEL4jYrIoCARTYv3+/3ORd7DEl9osTK+N4UIAC4SOwcuVKzJw5UyZfEYE4kYiFR/QJcF4WfWPOHkemAOdlkTmu7FX0CETjvIyBuCDe35zwBRGbVVEggAIiiYPYNPi6667D2rVrA1gTi6YABZQQePLJJ2WSBrFvqwi8if/fq1cv+fyKPeR4RKcA52XROe7sdeQJcF4WeWPKHkW2AOdlAANxQbzHOeELIjarokCABN5//32ZZVFs7r59+3YMGzYsQDWxWApQQCmBH/zgB/jvf//bUlzv3r3x6quv4qKLLlKqCpYThgKcl4XhoLHJFGgnwHkZbwkKhJ8A52UMxAX1ruWEL6jcrIwCiguI/aREkoaKigo888wzuOuuuxSvgwVSgAKBE6isrMS+ffvw8MMP4z//+Q8WL16Mhx56KHAVsmRVC3BepurhYeMo0K0A52XdEvEECqhaIJrnZVwRF8RbkxO+IGKzKgooLHDixAlMmDABx48fx6xZs/CHP/xB4RpYHAUoECyBxsZGjBs3Drt27cLWrVsxatSoYFXNelQkwHmZigaDTaGAlwKcl3kJxtMpoGKBaJyXMRAXxBuSE74gYrMqCigocPr0aUyaNAlfffUVbr31Vrz00kvQaDQK1sCiKECBYAuI/Unuu+8+zJ07V66Q4xF9ApyXRd+Ys8eRIcB5WWSMI3tBgbYC0TYvYyAuiPc/J3xBxGZVFFBIoLa2Fpdeeim2bduGH/3oR3jjjTeYYVEhWxZDgVAK/O1vf8Ntt92GO+64Ay+88EIom8K6QyTAeVmI4FktBfwQ4LzMDzxeSgEVC0TbvIyBuCDejJzwBRGbVVFAAYGGhgZcffXV+OSTT3DFFVfIDIsGg0GBklkEBSgQaoFbbrkFr7zyCsRvYH/3u9+FujmsPwQCnJeFAJ1VUsAPAc7L/MDjpRRQuUC0zcsYiAviDckJXxCxWRUF/BSw2+244YYbsGbNGvla6ocffoj4+Hg/S+XlFKBAsAQ2bdqEmpoaXH755dBqtS3Vin1Ili1bhrvvvhuxsbE4dOgQcnNzg9Us1qMiAc7LVDQYbAoFuhHgvIy3CAXCW4DzMvfxYyAugPfze++9h0WLFrXUIF5tczqdGDNmTMufib1prrnmmgC2gkVTgAK+CDz77LPyi7o4pk2bhqSkpE6Leeqpp5Cenu5LFbyGAhQIoMCKFSvkno7i+czPz0ePHj1QVlYms6YWFRVBBGHEirgf//jHAWwFi1aTAOdlahoNtoUC3glwXuadF8+mgNoEOC9jIC5o92TzzXa2CsW70GIZJg8KUEBdAgsWLMDChQu7bdTRo0fRp0+fbs/jCRSgQHAFxLO5fPlybNiwAd99950MwolXy8XzOmXKFPz2t79Fv379gtso1hZSAc7LQsrPyinglwDnZX7x8WIKhFyA8zIG4kJ+E7IBFKAABShAAQpQgAIUoAAFKEABClCAAtEnwFdTo2/M2WMKUIACFKAABShAAQpQgAIUoAAFKECBEAgwEBcCdFZJAQpQgAIUoAAFKEABClCAAhSgAAUoEH0CDMRF35izxxSgAAUoQAEKUIACFKAABShAAQpQgAIhEGAgLgTorJICFKAABShAAQpQgAIUoAAFKEABClAg+gQYiIu+MWePKUABClCAAhSgAAUoQAEKUIACFKAABUIgwEBcCNBZJQUoQAEKUIACFKAABShAAQpQgAIUoED0CTAQF31jzh5TgAIUoAAFKEABClCAAhSgAAUoQAEKhECAgbgQoLNKClCAAhSgAAUoQAEKUIACFKAABShAgegTYCAu+sacPaYABShAAQpQgAIUoAAFKEABClCAAhQIgQADcSFAZ5UUoAAFKEABClCAAhSgAAUoQAEKUIAC0SfAQFz0jTl7TAHVCWg0Gtkmp9OpurYp0aAFCxZg4cKF+Nvf/oZbbrlFiSJZBgUoQAEKUIACFAiIAOdlAWFloRSgAAVaBBiI481AAQqEXKCzCd+xY8dw7rnnYvLkyVi/fn3I23i2Blx88cXYsGEDjh49ij59+nQ4lYE4VQ8fG0cBClCAAhSgQBsBzst4O1CAAhQIrAADcYH1ZekUoIAHApE+4SsrK4P4ycrKQnJysgciPIUCFKAABShAAQqERoDzstC4s1YKUCB6BBiIi56xZk8poFqBSJ/wqRaeDaMABShAAQpQgALtBDgv4y1BAQpQILACDMQF1pelU4ACHgi0n/A1v8rZ2aUzZ87EihUrWj46ffo0nnzySbzzzjvy1VCDwYD8/HzMmjUL1157rVsRbV93Xbt2LUQ9a9asQWFhIX7961/jmWeeQWVlJVauXIl///vf+Prrr1FcXIyEhASMGjUKs2fPxmWXXdZSZnN5XXWxec+7s72aWl5ejsceewxvv/02CgoKEB8fj9GjR8v2X3755R2KFla9e/fGkSNH8NRTT+Gll17C8ePHYTab8bOf/QwPP/wwYmNjPVDnKRSgAAUoQAEKUKCjAOdlnJfxuaAABQIrwEBcYH1ZOgUo4IFA+wmfCEqtWrUKb731Fnr27Ikrr7yypZSJEyfiF7/4hfzvw4cP4wc/+IEMYIm92YYNG4aamhps2bIF9fX1MkD3u9/9rkPgTAS6bDabDGCJPei0Wi2GDh2K+fPn48MPP8RVV10lyzv//PORlpaG77//XpYpjuXLl+O2226T/1+8birKF9ecOnUK06dPl0G75qM5YNhVIO7EiRO46KKL8N133+Gcc87BuHHjUFpaKvebs9vtWLp0Ke655x43weZA3JgxY/D+++9D7E8njs8//xxVVVW48cYbpR0PClCAAhSgAAUo4IsA52Wcl/ly3/AaClDAcwEG4jy34pkUoECABHx5BUIEqkaMGIF9+/bhiSeekKvVREBNHN9++61cTSYCaF9++SXy8vLkn7ddwSaCXiKQlZKS4tYrsapOBNXGjh3r9ue7d+/GlClT4HA4IAJobQNuviZruO666+TKO7GSTWRUFav5xLFx40ZcccUVaGhowI4dOzB8+PCWtjRbDRw4EJ988gkyMzPlZ6LdI0eOlCv6RP/79u0boNFisRSgAAUoQAEKRLIA52Wcl0Xy/c2+UUANAgzEqWEU2AYKRLmALxM+sWpu2rRpchXam2++2UFQvHL6ox/9CL/97W/x7LPPdgjEbd++HRdeeKFX8nPmzMEjjzwC8VqrCKI1H74E4sQqOBEsEwE9sTJPrLxre4jAolgRJ1b/vfjiix0CcR9//LFcDdj2+L//+z88//zzMqh3yy23eNU3nkwBClCAAhSgAAWEAOdlnJfxSaAABQIrwEBcYH1ZOgUo4IGALxO+O++8Ey+88AJee+01uaKs/SFeG83IyJAr2zZv3uwWiBPZS0+ePNlly8Rqu//+97/44osvUFRUJFemieObb76Rf/aHP/xB7uHmTyDu1VdfhdjvbsaMGfjXv/7VoS1iJZ9Y8de/f3+5V13zIaxiYmJgsVig0+ncrnvuuedk4PHRRx/FAw884IE8T6EABShAAQpQgALuApyXcV7GZ4ICFAisAANxgfVl6RSggAcCvkz4rr76anzwwQfdlt6vXz8ZQBNH86upYn+15j3f2hcgEjeIJA979uzpsmyx55vYT86fQJxI0CCCZWKPObGXXftDvGKampoqV8yJfe/aBuJyc3Pla7ftD7En3a233irbJtrIgwIUoAAFKEABCngrwHkZ52Xe3jM8nwIU8E6AgTjvvHg2BSgQAAFfJnwigcNHH30kEzmIhA5dHenp6TK7qDjaZk1dv359p5c0lyteeb3vvvvkirTExES5/9xf//pX/OpXv+oQ6PLl1dTuAnEi8YLYv66zQJzImir6wkBcAG5GFkkBClCAAhSIcgHOyzoG4jgvi/KHgt2ngMICDMQpDMriKEAB7wV8mfCJvdNeeukluT+cCJp5cnQXiKurq0NSUpJ8pVUkZGj/6uf999+Pxx9/XJFAXPOrqTfccAPeeOONDs0XK/JEkobOXk1lIM6T0eY5FKAABShAAQr4IsB5Gedlvtw3vIYCFPBcgIE4z614JgUoECCBziZ8Yg+37OxsTJgwQWYRbX/885//xE9+8hPceOONWLVqlUct6y4Q11ynCICJLKltj8bGRpl99fDhwx0CcSJDq0ieIF6BFa/Ctj/Ea6ILFy50S6LQnKxBrLYTr5m2z9567733ypV8nSVrYCDOo+HmSRSgAAUoQAEK+CDAeRnnZT7cNryEAhTwQoCBOC+weCoFKBAYgc4mfDabTb6W2aNHD4h929qvTmtqasKwYcPw1Vdf4eGHH5avkcbGxrY00Ol0ysQK4hDBPHF0F4gTZYpXWcXKOPHqavN1InmDCIw9/fTTspz2e7CJDKWvvPIK/v3vf+Oaa67xKBAnThJ70b333nu4+eabsXz5cpmEQRwiucRll10Gq9WKHTt2yJVxzYewYiAuMPchS6UABShAAQpQoPOsqZyXcV7GZ4MCFFBOgIE45SxZEgUo4KNAZ4E4UdQPf/hDvPvuuxg8eDBGjhwJg8Egg2MiIYE4xAq0K664AkePHoXZbMbQoUPl/4qMqSLraElJiQye3X333R4F4sRJIuPoQw89JAN/U6ZMQVpaGrZu3YpTp07htttuw5/+9KcOgbjVq1fL12PFa61idVxycrKsTwTXxNHZijjx5+L110mTJsn2i+DauHHjUFpaKoOAIvjXPjuruIaBOB9vMl5GAQpQgAIUoIBHApyXcV7m0Y3CkyhAAZ8FGIjzmY4XUoACSgl0NeETgTSRVVS89ikCVCI4NXPmTIjsoM2H2Dz3+eefhwiGiddGxaq2zMxMubeaCOT9+Mc/lqvcxNHdirjmMsX+bc888wwOHToEo9GIiRMnylV3u3bt6jIrqTj/xRdfxJEjR9DQ0CCLEqvyzhaIE5+Vl5djyZIlePvtt1FQUID4+HiMHj0as2fPlkG99gcDcUrddSyHAhSgAAUoQIHOBDgv47yMTwYFKBBYAQbiAuvL0ilAAQpQwjblmAAAADhJREFUgAIUoAAFKEABClCAAhSgAAUoIAUYiOONQAEKUIACFKAABShAAQpQgAIUoAAFKECBIAj8f9q0M76EDGelAAAAAElFTkSuQmCC\" width=\"1000\">"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.legend.Legend at 0x130da4150>"
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_results = %sql SELECT * FROM iris_multi_model_info ORDER BY validation_loss ASC LIMIT 7;\n",
+ "df_results = df_results.DataFrame()\n",
+ "\n",
+ "df_summary = %sql SELECT * FROM iris_multi_model_summary;\n",
+ "df_summary = df_summary.DataFrame()\n",
+ "\n",
+ "#set up plots\n",
+ "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(10,5))\n",
+ "fig.legend(ncol=4)\n",
+ "fig.tight_layout()\n",
+ "\n",
+ "ax_metric = axs[0]\n",
+ "ax_loss = axs[1]\n",
+ "\n",
+ "ax_metric.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
+ "ax_metric.set_xlabel('Iteration')\n",
+ "ax_metric.set_ylabel('Metric')\n",
+ "ax_metric.set_title('Validation metric curve')\n",
+ "\n",
+ "ax_loss.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
+ "ax_loss.set_xlabel('Iteration')\n",
+ "ax_loss.set_ylabel('Loss')\n",
+ "ax_loss.set_title('Validation loss curve')\n",
+ "\n",
+ "iters = df_summary['metrics_iters'][0]\n",
+ "\n",
+ "for mst_key in df_results['mst_key']:\n",
+ " df_output_info = %sql SELECT validation_metrics,validation_loss FROM iris_multi_model_info WHERE mst_key = $mst_key\n",
+ " df_output_info = df_output_info.DataFrame()\n",
+ " validation_metrics = df_output_info['validation_metrics'][0]\n",
+ " validation_loss = df_output_info['validation_loss'][0]\n",
+ " \n",
+ " ax_metric.plot(iters, validation_metrics, label=mst_key, marker='o')\n",
+ " ax_loss.plot(iters, validation_loss, label=mst_key, marker='o')\n",
+ "\n",
+ "plt.legend()\n",
+ "# fig.savefig('./lc_keras_fit.png', dpi = 300)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.16"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/community-artifacts/Deep-learning/.ipynb_checkpoints/Preprocessor-for-images-distribution-rules-v1-checkpoint.ipynb b/community-artifacts/Deep-learning/.ipynb_checkpoints/Preprocessor-for-images-distribution-rules-v1-checkpoint.ipynb
new file mode 100644
index 0000000..b457303
--- /dev/null
+++ b/community-artifacts/Deep-learning/.ipynb_checkpoints/Preprocessor-for-images-distribution-rules-v1-checkpoint.ipynb
@@ -0,0 +1,1966 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Preprocessor for image data - distribution rules\n",
+ "\n",
+ "This notebook shows how to set distribution rules parameter for moving training and validation datasets to certain segments where training will be done. How you distribute data may depend, for example, on how GPUs are attached to your database cluster.\n",
+ "\n",
+ "The distribution rules parameter is part of the mini-batch preprocessor utility for image data:\n",
+ "* `training_preprocessor_dl()` for training datasets\n",
+ "* `validation_preprocessor_dl()` for validation datasets\n",
+ "\n",
+ "\n",
+ "## Table of contents\n",
+ "\n",
+ "<a href=\"#distr\">1. Setup distribution rules</a>\n",
+ "\n",
+ "<a href=\"#pp_train\">2. Run preprocessor for training image data</a>\n",
+ "<ul>\n",
+ "<a href=\"#pp_train2a\">2a. Distribute to all segments</a>\n",
+ " \n",
+ "<a href=\"#pp_train2b\">2b. Distribute to segments on hosts with GPUs attached</a>\n",
+ "\n",
+ "<a href=\"#pp_train2c\">2c. Distribute to segments on a subset of hosts</a>\n",
+ "\n",
+ "<a href=\"#pp_train2d\">2d. Distribute to 1 segment only</a>\n",
+ "\n",
+ "</ul>\n",
+ "\n",
+ "<a href=\"#pp_val\">3. Run preprocessor for validation image data</a>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The sql extension is already loaded. To reload it, use:\n",
+ " %reload_ext sql\n"
+ ]
+ }
+ ],
+ "source": [
+ "%load_ext sql"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "u'Connected: gpadmin@cifar_places'"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Greenplum Database 5.x on GCP - via tunnel\n",
+ "%sql postgresql://gpadmin@localhost:8000/cifar_places\n",
+ " \n",
+ "# PostgreSQL local\n",
+ "#%sql postgresql://fmcquillan@localhost:5432/madlib"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>version</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>MADlib version: 1.17-dev, git revision: rc/1.16-rc1-95-gc62dfe7, cmake configuration time: Tue Mar 17 16:53:55 UTC 2020, build type: RelWithDebInfo, build system: Linux-2.6.32-754.6.3.el6.x86_64, C compiler: gcc 4.4.7, C++ compiler: g++ 4.4.7</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'MADlib version: 1.17-dev, git revision: rc/1.16-rc1-95-gc62dfe7, cmake configuration time: Tue Mar 17 16:53:55 UTC 2020, build type: RelWithDebInfo, build system: Linux-2.6.32-754.6.3.el6.x86_64, C compiler: gcc 4.4.7, C++ compiler: g++ 4.4.7',)]"
+ ]
+ },
+ "execution_count": 30,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%sql select madlib.version();\n",
+ "#%sql select version();"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"distr\"></a>\n",
+ "# 1. Setup distribution rules\n",
+ "\n",
+ "Here are different ways to set up distribution rules tables.\n",
+ "\n",
+ "First get the GPU configuration in the cluster using the MADlib helper function `gpu_configuration`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "Done.\n",
+ "1 rows affected.\n",
+ "20 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>hostname</th>\n",
+ " <th>gpu_descr</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix0</td>\n",
+ " <td>device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix0</td>\n",
+ " <td>device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix0</td>\n",
+ " <td>device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix0</td>\n",
+ " <td>device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix1</td>\n",
+ " <td>device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix1</td>\n",
+ " <td>device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix1</td>\n",
+ " <td>device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix1</td>\n",
+ " <td>device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix2</td>\n",
+ " <td>device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix2</td>\n",
+ " <td>device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix2</td>\n",
+ " <td>device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix2</td>\n",
+ " <td>device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix3</td>\n",
+ " <td>device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix3</td>\n",
+ " <td>device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix3</td>\n",
+ " <td>device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix3</td>\n",
+ " <td>device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix4</td>\n",
+ " <td>device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix4</td>\n",
+ " <td>device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix4</td>\n",
+ " <td>device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix4</td>\n",
+ " <td>device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
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+ " (u'gpsix2', u'device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0'),\n",
+ " (u'gpsix2', u'device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0'),\n",
+ " (u'gpsix2', u'device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0'),\n",
+ " (u'gpsix3', u'device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0'),\n",
+ " (u'gpsix3', u'device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0'),\n",
+ " (u'gpsix3', u'device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0'),\n",
+ " (u'gpsix3', u'device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0'),\n",
+ " (u'gpsix4', u'device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0'),\n",
+ " (u'gpsix4', u'device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0'),\n",
+ " (u'gpsix4', u'device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0'),\n",
+ " (u'gpsix4', u'device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0')]"
+ ]
+ },
+ "execution_count": 31,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS host_gpu_mapping_tf;\n",
+ "SELECT * FROM madlib.gpu_configuration('host_gpu_mapping_tf');\n",
+ "SELECT * FROM host_gpu_mapping_tf ORDER BY hostname, gpu_descr;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Review the Greenplum segments in the `gp_segment_configuration` table:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "21 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
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+ " </tr>\n",
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+ ],
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+ " (13, 11, u'p', u'p', u'n', u'u', 40003, u'gpsix2', u'gpsix2', u'/data/primary3/gpseg11'),\n",
+ " (14, 12, u'p', u'p', u'n', u'u', 40000, u'gpsix3', u'gpsix3', u'/data/primary0/gpseg12'),\n",
+ " (15, 13, u'p', u'p', u'n', u'u', 40001, u'gpsix3', u'gpsix3', u'/data/primary1/gpseg13'),\n",
+ " (16, 14, u'p', u'p', u'n', u'u', 40002, u'gpsix3', u'gpsix3', u'/data/primary2/gpseg14'),\n",
+ " (17, 15, u'p', u'p', u'n', u'u', 40003, u'gpsix3', u'gpsix3', u'/data/primary3/gpseg15'),\n",
+ " (18, 16, u'p', u'p', u'n', u'u', 40000, u'gpsix4', u'gpsix4', u'/data/primary0/gpseg16'),\n",
+ " (19, 17, u'p', u'p', u'n', u'u', 40001, u'gpsix4', u'gpsix4', u'/data/primary1/gpseg17'),\n",
+ " (20, 18, u'p', u'p', u'n', u'u', 40002, u'gpsix4', u'gpsix4', u'/data/primary2/gpseg18'),\n",
+ " (21, 19, u'p', u'p', u'n', u'u', 40003, u'gpsix4', u'gpsix4', u'/data/primary3/gpseg19')]"
+ ]
+ },
+ "execution_count": 32,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT * FROM gp_segment_configuration ORDER BY dbid;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now JOIN the above 2 tables to build up various distribution rules, depending on your needs.\n",
+ "\n",
+ "Build distribution rules table for 4 VMs:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "Done.\n",
+ "16 rows affected.\n",
+ "16 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
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+ " <td>2</td>\n",
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+ " <tr>\n",
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+ " <td>15</td>\n",
+ " <td>gpsix3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>16</td>\n",
+ " <td>gpsix3</td>\n",
+ " </tr>\n",
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+ " <td>17</td>\n",
+ " <td>gpsix3</td>\n",
+ " </tr>\n",
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+ " (13, u'gpsix2'),\n",
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+ " (15, u'gpsix3'),\n",
+ " (16, u'gpsix3'),\n",
+ " (17, u'gpsix3')]"
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS segments_to_use_4VMs;\n",
+ "CREATE TABLE segments_to_use_4VMs AS\n",
+ " SELECT DISTINCT dbid, hostname FROM gp_segment_configuration JOIN host_gpu_mapping_tf USING (hostname)\n",
+ " WHERE role='p' AND content>=0 AND hostname!='gpsix4';\n",
+ "SELECT * FROM segments_to_use_4VMs ORDER BY hostname, dbid;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Build distribution rules table for 2 VMs:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "Done.\n",
+ "8 rows affected.\n",
+ "8 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
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+ " <td>2</td>\n",
+ " <td>gpsix0</td>\n",
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+ " <tr>\n",
+ " <td>7</td>\n",
+ " <td>gpsix1</td>\n",
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+ " <tr>\n",
+ " <td>8</td>\n",
+ " <td>gpsix1</td>\n",
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+ " <td>9</td>\n",
+ " <td>gpsix1</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(2, u'gpsix0'),\n",
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+ " (6, u'gpsix1'),\n",
+ " (7, u'gpsix1'),\n",
+ " (8, u'gpsix1'),\n",
+ " (9, u'gpsix1')]"
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS segments_to_use_2VMs;\n",
+ "CREATE TABLE segments_to_use_2VMs AS\n",
+ " SELECT DISTINCT dbid, hostname FROM gp_segment_configuration JOIN host_gpu_mapping_tf USING (hostname)\n",
+ " WHERE role='p' AND content>=0 AND (hostname='gpsix0' OR hostname='gpsix1');\n",
+ "SELECT * FROM segments_to_use_2VMs ORDER BY hostname, dbid;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Build distribution rules table for 1 VM:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "Done.\n",
+ "4 rows affected.\n",
+ "4 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>dbid</th>\n",
+ " <th>hostname</th>\n",
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+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>gpsix0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>gpsix0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>4</td>\n",
+ " <td>gpsix0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>5</td>\n",
+ " <td>gpsix0</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(2, u'gpsix0'), (3, u'gpsix0'), (4, u'gpsix0'), (5, u'gpsix0')]"
+ ]
+ },
+ "execution_count": 35,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS segments_to_use_1VM;\n",
+ "CREATE TABLE segments_to_use_1VM AS\n",
+ " SELECT DISTINCT dbid, hostname FROM gp_segment_configuration JOIN host_gpu_mapping_tf USING (hostname)\n",
+ " WHERE role='p' AND content>=0 AND hostname='gpsix0';\n",
+ "SELECT * FROM segments_to_use_1VM ORDER BY hostname, dbid;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Build distribution rules table for 1 segment:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "Done.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>dbid</th>\n",
+ " <th>hostname</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>gpsix0</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(2, u'gpsix0')]"
+ ]
+ },
+ "execution_count": 36,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS segments_to_use_1seg;\n",
+ "CREATE TABLE segments_to_use_1seg AS\n",
+ " SELECT DISTINCT dbid, hostname FROM gp_segment_configuration JOIN host_gpu_mapping_tf USING (hostname)\n",
+ " WHERE dbid=2;\n",
+ "SELECT * FROM segments_to_use_1seg ORDER BY hostname, dbid;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"pp_train\"></a>\n",
+ "# 2. Run preprocessor for training image data\n",
+ "\n",
+ "Run the preprocessor to generate the packed output table on the segments that you want to use for training and validation. The training data in our example is CIFAR-10 and is in table `image_data_train` and the validation data is in `image_data_val` .\n",
+ "\n",
+ "<a id=\"pp_train2a\"></a>\n",
+ "## 2a. All segments\n",
+ "\n",
+ "First distribute to all segments:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "Done.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>source_table</th>\n",
+ " <th>output_table</th>\n",
+ " <th>dependent_varname</th>\n",
+ " <th>independent_varname</th>\n",
+ " <th>dependent_vartype</th>\n",
+ " <th>class_values</th>\n",
+ " <th>buffer_size</th>\n",
+ " <th>normalizing_const</th>\n",
+ " <th>num_classes</th>\n",
+ " <th>distribution_rules</th>\n",
+ " <th>__internal_gpu_config__</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>image_data_train</td>\n",
+ " <td>image_data_train_packed</td>\n",
+ " <td>y</td>\n",
+ " <td>x</td>\n",
+ " <td>smallint</td>\n",
+ " <td>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]</td>\n",
+ " <td>2500</td>\n",
+ " <td>255.0</td>\n",
+ " <td>10</td>\n",
+ " <td>all_segments</td>\n",
+ " <td>all_segments</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'image_data_train', u'image_data_train_packed', u'y', u'x', u'smallint', [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], 2500, 255.0, 10, 'all_segments', 'all_segments')]"
+ ]
+ },
+ "execution_count": 37,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS image_data_train_packed, image_data_train_packed_summary;\n",
+ "\n",
+ "SELECT madlib.training_preprocessor_dl('image_data_train', -- Source table\n",
+ " 'image_data_train_packed', -- Output table\n",
+ " 'y', -- Dependent variable\n",
+ " 'x', -- Independent variable\n",
+ " NULL, -- Buffer size\n",
+ " 255, -- Normalizing constant\n",
+ " NULL, -- Number of classes\n",
+ " 'all_segments' -- Distribution rules\n",
+ " );\n",
+ "SELECT * FROM image_data_train_packed_summary;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Check distribution:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "20 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>__dist_key__</th>\n",
+ " <th>independent_var_shape</th>\n",
+ " <th>dependent_var_shape</th>\n",
+ " <th>buffer_id</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>0</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>4</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>9</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>7</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>4</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>14</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>5</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>17</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>6</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>16</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>7</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>9</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>13</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>12</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>15</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>14</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>6</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>19</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>21</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>18</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>27</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>28</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>29</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>8</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>33</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>19</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>34</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>55</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>56</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(0, [2500, 32, 32, 3], [2500, 10], 1),\n",
+ " (1, [2500, 32, 32, 3], [2500, 10], 4),\n",
+ " (2, [2500, 32, 32, 3], [2500, 10], 9),\n",
+ " (3, [2500, 32, 32, 3], [2500, 10], 7),\n",
+ " (4, [2500, 32, 32, 3], [2500, 10], 14),\n",
+ " (5, [2500, 32, 32, 3], [2500, 10], 17),\n",
+ " (6, [2500, 32, 32, 3], [2500, 10], 16),\n",
+ " (7, [2500, 32, 32, 3], [2500, 10], 11),\n",
+ " (9, [2500, 32, 32, 3], [2500, 10], 13),\n",
+ " (12, [2500, 32, 32, 3], [2500, 10], 15),\n",
+ " (14, [2500, 32, 32, 3], [2500, 10], 6),\n",
+ " (19, [2500, 32, 32, 3], [2500, 10], 12),\n",
+ " (21, [2500, 32, 32, 3], [2500, 10], 18),\n",
+ " (27, [2500, 32, 32, 3], [2500, 10], 10),\n",
+ " (28, [2500, 32, 32, 3], [2500, 10], 5),\n",
+ " (29, [2500, 32, 32, 3], [2500, 10], 8),\n",
+ " (33, [2500, 32, 32, 3], [2500, 10], 19),\n",
+ " (34, [2500, 32, 32, 3], [2500, 10], 0),\n",
+ " (55, [2500, 32, 32, 3], [2500, 10], 3),\n",
+ " (56, [2500, 32, 32, 3], [2500, 10], 2)]"
+ ]
+ },
+ "execution_count": 38,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT __dist_key__, independent_var_shape, dependent_var_shape, buffer_id FROM image_data_train_packed ORDER BY __dist_key__;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"pp_train2b\"></a>\n",
+ "## 2b. All segments on hosts with GPUs\n",
+ "\n",
+ "Now distribute to all segments on hosts with GPUs attached:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "Done.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>source_table</th>\n",
+ " <th>output_table</th>\n",
+ " <th>dependent_varname</th>\n",
+ " <th>independent_varname</th>\n",
+ " <th>dependent_vartype</th>\n",
+ " <th>class_values</th>\n",
+ " <th>buffer_size</th>\n",
+ " <th>normalizing_const</th>\n",
+ " <th>num_classes</th>\n",
+ " <th>distribution_rules</th>\n",
+ " <th>__internal_gpu_config__</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>image_data_train</td>\n",
+ " <td>image_data_train_packed</td>\n",
+ " <td>y</td>\n",
+ " <td>x</td>\n",
+ " <td>smallint</td>\n",
+ " <td>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]</td>\n",
+ " <td>2500</td>\n",
+ " <td>255.0</td>\n",
+ " <td>10</td>\n",
+ " <td>[2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21]</td>\n",
+ " <td>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'image_data_train', u'image_data_train_packed', u'y', u'x', u'smallint', [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], 2500, 255.0, 10, [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19])]"
+ ]
+ },
+ "execution_count": 39,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS image_data_train_packed, image_data_train_packed_summary;\n",
+ "\n",
+ "SELECT madlib.training_preprocessor_dl('image_data_train', -- Source table\n",
+ " 'image_data_train_packed', -- Output table\n",
+ " 'y', -- Dependent variable\n",
+ " 'x', -- Independent variable\n",
+ " NULL, -- Buffer size\n",
+ " 255, -- Normalizing constant\n",
+ " NULL, -- Number of classes\n",
+ " 'gpu_segments' -- Distribution rules\n",
+ " );\n",
+ "SELECT * FROM image_data_train_packed_summary;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Check distribution:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "20 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>__dist_key__</th>\n",
+ " <th>independent_var_shape</th>\n",
+ " <th>dependent_var_shape</th>\n",
+ " <th>buffer_id</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>0</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>4</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>9</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>7</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>4</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>14</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>5</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>17</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>6</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>16</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>7</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>9</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>13</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>12</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>15</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>14</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>6</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>19</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>21</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>18</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>27</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>28</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>29</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>8</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>33</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>19</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>34</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>55</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>56</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(0, [2500, 32, 32, 3], [2500, 10], 1),\n",
+ " (1, [2500, 32, 32, 3], [2500, 10], 4),\n",
+ " (2, [2500, 32, 32, 3], [2500, 10], 9),\n",
+ " (3, [2500, 32, 32, 3], [2500, 10], 7),\n",
+ " (4, [2500, 32, 32, 3], [2500, 10], 14),\n",
+ " (5, [2500, 32, 32, 3], [2500, 10], 17),\n",
+ " (6, [2500, 32, 32, 3], [2500, 10], 16),\n",
+ " (7, [2500, 32, 32, 3], [2500, 10], 11),\n",
+ " (9, [2500, 32, 32, 3], [2500, 10], 13),\n",
+ " (12, [2500, 32, 32, 3], [2500, 10], 15),\n",
+ " (14, [2500, 32, 32, 3], [2500, 10], 6),\n",
+ " (19, [2500, 32, 32, 3], [2500, 10], 12),\n",
+ " (21, [2500, 32, 32, 3], [2500, 10], 18),\n",
+ " (27, [2500, 32, 32, 3], [2500, 10], 10),\n",
+ " (28, [2500, 32, 32, 3], [2500, 10], 5),\n",
+ " (29, [2500, 32, 32, 3], [2500, 10], 8),\n",
+ " (33, [2500, 32, 32, 3], [2500, 10], 19),\n",
+ " (34, [2500, 32, 32, 3], [2500, 10], 0),\n",
+ " (55, [2500, 32, 32, 3], [2500, 10], 3),\n",
+ " (56, [2500, 32, 32, 3], [2500, 10], 2)]"
+ ]
+ },
+ "execution_count": 40,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT __dist_key__, independent_var_shape, dependent_var_shape, buffer_id FROM image_data_train_packed ORDER BY __dist_key__;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"pp_train2c\"></a>\n",
+ "## 2c. Segments on 2 hosts with GPUs\n",
+ "\n",
+ "Now distribute to segments on 2 hosts with GPUs attached (if for some reason I need to do this):"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For small datasets like in this example, buffer size is mainly determined by the number of segments in the database. For a Greenplum database with 2 segments, there will be 2 rows with a buffer size of 26. For PostgresSQL, there would be only one row with a buffer size of 52 since it is a single node database. For larger data sets, other factors go into computing buffers size besides number of segments. \n",
+ "\n",
+ "Review the output summary table:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "Done.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>source_table</th>\n",
+ " <th>output_table</th>\n",
+ " <th>dependent_varname</th>\n",
+ " <th>independent_varname</th>\n",
+ " <th>dependent_vartype</th>\n",
+ " <th>class_values</th>\n",
+ " <th>buffer_size</th>\n",
+ " <th>normalizing_const</th>\n",
+ " <th>num_classes</th>\n",
+ " <th>distribution_rules</th>\n",
+ " <th>__internal_gpu_config__</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>image_data_train</td>\n",
+ " <td>image_data_train_packed</td>\n",
+ " <td>y</td>\n",
+ " <td>x</td>\n",
+ " <td>smallint</td>\n",
+ " <td>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]</td>\n",
+ " <td>6250</td>\n",
+ " <td>255.0</td>\n",
+ " <td>10</td>\n",
+ " <td>[2, 3, 4, 5, 6, 7, 8, 9]</td>\n",
+ " <td>[0, 1, 2, 3, 4, 5, 6, 7]</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'image_data_train', u'image_data_train_packed', u'y', u'x', u'smallint', [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], 6250, 255.0, 10, [2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7])]"
+ ]
+ },
+ "execution_count": 41,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS image_data_train_packed, image_data_train_packed_summary;\n",
+ "\n",
+ "SELECT madlib.training_preprocessor_dl('image_data_train', -- Source table\n",
+ " 'image_data_train_packed', -- Output table\n",
+ " 'y', -- Dependent variable\n",
+ " 'x', -- Independent variable\n",
+ " NULL, -- Buffer size\n",
+ " 255, -- Normalizing constant\n",
+ " NULL, -- Number of classes\n",
+ " 'segments_to_use_2VMs' -- Distribution rules\n",
+ " );\n",
+ "SELECT * FROM image_data_train_packed_summary;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Check distribution:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "8 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>__dist_key__</th>\n",
+ " <th>independent_var_shape</th>\n",
+ " <th>dependent_var_shape</th>\n",
+ " <th>buffer_id</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>0</td>\n",
+ " <td>[6250, 32, 32, 3]</td>\n",
+ " <td>[6250, 10]</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>[6250, 32, 32, 3]</td>\n",
+ " <td>[6250, 10]</td>\n",
+ " <td>4</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>[6250, 32, 32, 3]</td>\n",
+ " <td>[6250, 10]</td>\n",
+ " <td>7</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>14</td>\n",
+ " <td>[6250, 32, 32, 3]</td>\n",
+ " <td>[6250, 10]</td>\n",
+ " <td>6</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>28</td>\n",
+ " <td>[6250, 32, 32, 3]</td>\n",
+ " <td>[6250, 10]</td>\n",
+ " <td>5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>34</td>\n",
+ " <td>[6250, 32, 32, 3]</td>\n",
+ " <td>[6250, 10]</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>55</td>\n",
+ " <td>[6250, 32, 32, 3]</td>\n",
+ " <td>[6250, 10]</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>56</td>\n",
+ " <td>[6250, 32, 32, 3]</td>\n",
+ " <td>[6250, 10]</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(0, [6250, 32, 32, 3], [6250, 10], 1),\n",
+ " (1, [6250, 32, 32, 3], [6250, 10], 4),\n",
+ " (3, [6250, 32, 32, 3], [6250, 10], 7),\n",
+ " (14, [6250, 32, 32, 3], [6250, 10], 6),\n",
+ " (28, [6250, 32, 32, 3], [6250, 10], 5),\n",
+ " (34, [6250, 32, 32, 3], [6250, 10], 0),\n",
+ " (55, [6250, 32, 32, 3], [6250, 10], 3),\n",
+ " (56, [6250, 32, 32, 3], [6250, 10], 2)]"
+ ]
+ },
+ "execution_count": 42,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT __dist_key__, independent_var_shape, dependent_var_shape, buffer_id FROM image_data_train_packed ORDER BY __dist_key__;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"pp_train2d\"></a>\n",
+ "## 2d. Segments on 1 segment\n",
+ "\n",
+ "Now distribute 1 segment on a host with GPUs attached (if for some reason I need to do this):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "Done.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>source_table</th>\n",
+ " <th>output_table</th>\n",
+ " <th>dependent_varname</th>\n",
+ " <th>independent_varname</th>\n",
+ " <th>dependent_vartype</th>\n",
+ " <th>class_values</th>\n",
+ " <th>buffer_size</th>\n",
+ " <th>normalizing_const</th>\n",
+ " <th>num_classes</th>\n",
+ " <th>distribution_rules</th>\n",
+ " <th>__internal_gpu_config__</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>image_data_train</td>\n",
+ " <td>image_data_train_packed</td>\n",
+ " <td>y</td>\n",
+ " <td>x</td>\n",
+ " <td>smallint</td>\n",
+ " <td>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]</td>\n",
+ " <td>16667</td>\n",
+ " <td>255.0</td>\n",
+ " <td>10</td>\n",
+ " <td>[2]</td>\n",
+ " <td>[0]</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'image_data_train', u'image_data_train_packed', u'y', u'x', u'smallint', [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], 16667, 255.0, 10, [2], [0])]"
+ ]
+ },
+ "execution_count": 43,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS image_data_train_packed, image_data_train_packed_summary;\n",
+ "\n",
+ "SELECT madlib.training_preprocessor_dl('image_data_train', -- Source table\n",
+ " 'image_data_train_packed', -- Output table\n",
+ " 'y', -- Dependent variable\n",
+ " 'x', -- Independent variable\n",
+ " NULL, -- Buffer size\n",
+ " 255, -- Normalizing constant\n",
+ " NULL, -- Number of classes\n",
+ " 'segments_to_use_1seg' -- Distribution rules\n",
+ " );\n",
+ "SELECT * FROM image_data_train_packed_summary;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Check distribution:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "3 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>__dist_key__</th>\n",
+ " <th>independent_var_shape</th>\n",
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+ " <th>buffer_id</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>34</td>\n",
+ " <td>[16667, 32, 32, 3]</td>\n",
+ " <td>[16667, 10]</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>34</td>\n",
+ " <td>[16666, 32, 32, 3]</td>\n",
+ " <td>[16666, 10]</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>34</td>\n",
+ " <td>[16667, 32, 32, 3]</td>\n",
+ " <td>[16667, 10]</td>\n",
+ " <td>0</td>\n",
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+ ],
+ "text/plain": [
+ "[(34, [16667, 32, 32, 3], [16667, 10], 1),\n",
+ " (34, [16666, 32, 32, 3], [16666, 10], 2),\n",
+ " (34, [16667, 32, 32, 3], [16667, 10], 0)]"
+ ]
+ },
+ "execution_count": 44,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT __dist_key__, independent_var_shape, dependent_var_shape, buffer_id FROM image_data_train_packed ORDER BY __dist_key__;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"pp_val\"></a>\n",
+ "# 3. Run preprocessor for validation image data\n",
+ "\n",
+ "The same idea applies to the validation data set for distribution rules. Continuing the example above with distribution to a single segment:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "Done.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>source_table</th>\n",
+ " <th>output_table</th>\n",
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+ " <th>independent_varname</th>\n",
+ " <th>dependent_vartype</th>\n",
+ " <th>class_values</th>\n",
+ " <th>buffer_size</th>\n",
+ " <th>normalizing_const</th>\n",
+ " <th>num_classes</th>\n",
+ " <th>distribution_rules</th>\n",
+ " <th>__internal_gpu_config__</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>image_data_val</td>\n",
+ " <td>image_data_val_packed</td>\n",
+ " <td>y</td>\n",
+ " <td>x</td>\n",
+ " <td>smallint</td>\n",
+ " <td>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]</td>\n",
+ " <td>10000</td>\n",
+ " <td>255.0</td>\n",
+ " <td>10</td>\n",
+ " <td>[2]</td>\n",
+ " <td>[0]</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'image_data_val', u'image_data_val_packed', u'y', u'x', u'smallint', [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], 10000, 255.0, 10, [2], [0])]"
+ ]
+ },
+ "execution_count": 45,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS image_data_val_packed, image_data_val_packed_summary;\n",
+ "\n",
+ "SELECT madlib.validation_preprocessor_dl('image_data_val', -- Source table\n",
+ " 'image_data_val_packed', -- Output table\n",
+ " 'y', -- Dependent variable\n",
+ " 'x', -- Independent variable\n",
+ " 'image_data_train_packed', -- Training preprocessor output table \n",
+ " NULL, -- Buffer size\n",
+ " 'segments_to_use_1seg' -- Distribution rules\n",
+ " );\n",
+ "SELECT * FROM image_data_val_packed_summary;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Check distribution:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>__dist_key__</th>\n",
+ " <th>independent_var_shape</th>\n",
+ " <th>dependent_var_shape</th>\n",
+ " <th>buffer_id</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>34</td>\n",
+ " <td>[10000, 32, 32, 3]</td>\n",
+ " <td>[10000, 10]</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(34, [10000, 32, 32, 3], [10000, 10], 0)]"
+ ]
+ },
+ "execution_count": 46,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT __dist_key__, independent_var_shape, dependent_var_shape, buffer_id FROM image_data_val_packed ORDER BY __dist_key__;"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.16"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/community-artifacts/Deep-learning/Load-images-v1.ipynb b/community-artifacts/Deep-learning/Load-images-v1.ipynb
index 1750cfc..2c8c108 100644
--- a/community-artifacts/Deep-learning/Load-images-v1.ipynb
+++ b/community-artifacts/Deep-learning/Load-images-v1.ipynb
@@ -693,7 +693,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
- "version": "2.7.10"
+ "version": "2.7.16"
}
},
"nbformat": 4,
diff --git a/community-artifacts/Deep-learning/MADlib-Keras-cifar10-cnn-v3.ipynb b/community-artifacts/Deep-learning/MADlib-Keras-cifar10-cnn-v3.ipynb
index fb01c4d..987ff4b 100644
--- a/community-artifacts/Deep-learning/MADlib-Keras-cifar10-cnn-v3.ipynb
+++ b/community-artifacts/Deep-learning/MADlib-Keras-cifar10-cnn-v3.ipynb
@@ -1289,7 +1289,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
- "version": "2.7.10"
+ "version": "2.7.16"
}
},
"nbformat": 4,
diff --git a/community-artifacts/Deep-learning/MADlib-Keras-model-selection-CNN-cifar10-v1.ipynb b/community-artifacts/Deep-learning/MADlib-Keras-model-selection-CNN-cifar10-v1.ipynb
new file mode 100755
index 0000000..70b9129
--- /dev/null
+++ b/community-artifacts/Deep-learning/MADlib-Keras-model-selection-CNN-cifar10-v1.ipynb
@@ -0,0 +1,8037 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Model Selection for CNN Using Keras and MADlib on CIFAR-10\n",
+ "\n",
+ "E2E classification example using MADlib calling a Keras CNN for different hyperparameters and model architectures on the CIFAR-10 dataset.\n",
+ "\n",
+ "The CIFAR-10 dataset consists of 60,000 32x32 colour images in 10 classes, with 6,000 images per class. There are 50,000 training images and 10,000 test images.\n",
+ "https://www.cs.toronto.edu/~kriz/cifar.html\n",
+ "\n",
+ "## Table of contents \n",
+ "\n",
+ "<a href=\"#setup\">0. Setup</a>\n",
+ "\n",
+ "<a href=\"#load_dataset\">1. Load dataset into table</a>\n",
+ "\n",
+ "<a href=\"#distr\">2. Setup distribution rules and call preprocessor</a>\n",
+ "\n",
+ "<a href=\"#arch\">3. Define and load model architectures</a>\n",
+ "\n",
+ "<a href=\"#mst\">4. Define and load model selection tuples</a>\n",
+ "\n",
+ "<a href=\"#train\">5. Train</a>\n",
+ "\n",
+ "<a href=\"#plot\">6. Plot results</a>\n",
+ "\n",
+ "<a href=\"#predict\">7. Inference</a>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"setup\"></a>\n",
+ "# 0. Setup"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [],
+ "source": [
+ "%load_ext sql"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "u'Connected: gpadmin@cifar_places'"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Greenplum Database 5.x on GCP - via tunnel\n",
+ "%sql postgresql://gpadmin@localhost:8000/cifar_places\n",
+ " \n",
+ "# PostgreSQL local\n",
+ "#%sql postgresql://fmcquillan@localhost:5432/madlib"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>version</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>MADlib version: 1.17-dev, git revision: rc/1.16-rc1-95-gc62dfe7, cmake configuration time: Tue Mar 17 16:53:55 UTC 2020, build type: RelWithDebInfo, build system: Linux-2.6.32-754.6.3.el6.x86_64, C compiler: gcc 4.4.7, C++ compiler: g++ 4.4.7</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'MADlib version: 1.17-dev, git revision: rc/1.16-rc1-95-gc62dfe7, cmake configuration time: Tue Mar 17 16:53:55 UTC 2020, build type: RelWithDebInfo, build system: Linux-2.6.32-754.6.3.el6.x86_64, C compiler: gcc 4.4.7, C++ compiler: g++ 4.4.7',)]"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%sql select madlib.version();\n",
+ "#%sql select version();"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Import libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Using TensorFlow backend.\n"
+ ]
+ }
+ ],
+ "source": [
+ "from __future__ import print_function\n",
+ "import keras\n",
+ "from keras.datasets import cifar10\n",
+ "from keras.preprocessing.image import ImageDataGenerator\n",
+ "from keras.models import Sequential\n",
+ "from keras.layers import Dense, Dropout, Activation, Flatten, BatchNormalization\n",
+ "from keras.layers import Conv2D, MaxPooling2D\n",
+ "import os"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Others needed in this workbook"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import sys\n",
+ "import os\n",
+ "from matplotlib import pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"load_dataset\"></a>\n",
+ "# 1. Load dataset into table\n",
+ "\n",
+ "PXF can be used to load image data into Greenplum database.\n",
+ "\n",
+ "But for this demo, we will get the dataset from Keras and use the script called madlib_image_loader.py located at https://github.com/apache/madlib-site/tree/asf-site/community-artifacts/Deep-learning .\n",
+ "\n",
+ "If the script is not in the same folder as the notebook, you can use the following lines to import it."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import sys\n",
+ "sys.path.insert(1, '/Users/fmcquillan/workspace/madlib-site/community-artifacts/Deep-learning')\n",
+ "from madlib_image_loader import ImageLoader, DbCredentials\n",
+ "\n",
+ "# Specify database credentials, for connecting to db\n",
+ "db_creds = DbCredentials(db_name='cifar_places',\n",
+ " user='gpadmin',\n",
+ " host='localhost',\n",
+ " port='8000',\n",
+ " password='')\n",
+ "\n",
+ "#db_creds = DbCredentials(db_name='cifar_places',\n",
+ "# user='fmcquillan',\n",
+ "# host='localhost',\n",
+ "# port='5432',\n",
+ "# password='')\n",
+ "\n",
+ "# Initialize ImageLoader (increase num_workers to run faster)\n",
+ "iloader = ImageLoader(num_workers=5, db_creds=db_creds)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Load dataset into tables"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Load dataset into np array\n",
+ "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
+ "\n",
+ "%sql DROP TABLE IF EXISTS cifar10_train, cifar10_val;\n",
+ "\n",
+ "# Save images to temporary directories and load into database\n",
+ "iloader.load_dataset_from_np(x_train, y_train, 'cifar10_train', append=False)\n",
+ "iloader.load_dataset_from_np(x_test, y_test, 'cifar10_val', append=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>50000</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(50000L,)]"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%sql SELECT COUNT(*) FROM cifar10_train;"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>10000</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(10000L,)]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%sql SELECT COUNT(*) FROM cifar10_val;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"distr\"></a>\n",
+ "# 2. Setup distribution rules and call preprocessor\n",
+ "\n",
+ "In this example we will train on 4 VMs with 4 segments/VM and 4 GPUs/VM (i.e., 16 workers).\n",
+ "\n",
+ "First get the GPU configuration in the cluster using the MADlib helper function `gpu_configuration`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "Done.\n",
+ "1 rows affected.\n",
+ "20 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>hostname</th>\n",
+ " <th>gpu_descr</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix0</td>\n",
+ " <td>device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix0</td>\n",
+ " <td>device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix0</td>\n",
+ " <td>device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix0</td>\n",
+ " <td>device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix1</td>\n",
+ " <td>device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix1</td>\n",
+ " <td>device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix1</td>\n",
+ " <td>device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix1</td>\n",
+ " <td>device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix2</td>\n",
+ " <td>device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix2</td>\n",
+ " <td>device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix2</td>\n",
+ " <td>device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix2</td>\n",
+ " <td>device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix3</td>\n",
+ " <td>device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix3</td>\n",
+ " <td>device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix3</td>\n",
+ " <td>device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix3</td>\n",
+ " <td>device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix4</td>\n",
+ " <td>device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix4</td>\n",
+ " <td>device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix4</td>\n",
+ " <td>device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix4</td>\n",
+ " <td>device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'gpsix0', u'device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0'),\n",
+ " (u'gpsix0', u'device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0'),\n",
+ " (u'gpsix0', u'device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0'),\n",
+ " (u'gpsix0', u'device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0'),\n",
+ " (u'gpsix1', u'device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0'),\n",
+ " (u'gpsix1', u'device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0'),\n",
+ " (u'gpsix1', u'device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0'),\n",
+ " (u'gpsix1', u'device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0'),\n",
+ " (u'gpsix2', u'device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0'),\n",
+ " (u'gpsix2', u'device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0'),\n",
+ " (u'gpsix2', u'device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0'),\n",
+ " (u'gpsix2', u'device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0'),\n",
+ " (u'gpsix3', u'device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0'),\n",
+ " (u'gpsix3', u'device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0'),\n",
+ " (u'gpsix3', u'device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0'),\n",
+ " (u'gpsix3', u'device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0'),\n",
+ " (u'gpsix4', u'device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0'),\n",
+ " (u'gpsix4', u'device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0'),\n",
+ " (u'gpsix4', u'device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0'),\n",
+ " (u'gpsix4', u'device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0')]"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS host_gpu_mapping_tf;\n",
+ "SELECT * FROM madlib.gpu_configuration('host_gpu_mapping_tf');\n",
+ "SELECT * FROM host_gpu_mapping_tf ORDER BY hostname, gpu_descr;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Review the Greenplum segments in the `gp_segment_configuration` table:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "21 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>dbid</th>\n",
+ " <th>content</th>\n",
+ " <th>role</th>\n",
+ " <th>preferred_role</th>\n",
+ " <th>mode</th>\n",
+ " <th>status</th>\n",
+ " <th>port</th>\n",
+ " <th>hostname</th>\n",
+ " <th>address</th>\n",
+ " <th>datadir</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>-1</td>\n",
+ " <td>p</td>\n",
+ " <td>p</td>\n",
+ " <td>n</td>\n",
+ " <td>u</td>\n",
+ " <td>5432</td>\n",
+ " <td>gpsix0</td>\n",
+ " <td>gpsix0</td>\n",
+ " <td>/data/master/gpseg-1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>0</td>\n",
+ " <td>p</td>\n",
+ " <td>p</td>\n",
+ " <td>n</td>\n",
+ " <td>u</td>\n",
+ " <td>40000</td>\n",
+ " <td>gpsix0</td>\n",
+ " <td>gpsix0</td>\n",
+ " <td>/data/primary0/gpseg0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>1</td>\n",
+ " <td>p</td>\n",
+ " <td>p</td>\n",
+ " <td>n</td>\n",
+ " <td>u</td>\n",
+ " <td>40001</td>\n",
+ " <td>gpsix0</td>\n",
+ " <td>gpsix0</td>\n",
+ " <td>/data/primary1/gpseg1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>4</td>\n",
+ " <td>2</td>\n",
+ " <td>p</td>\n",
+ " <td>p</td>\n",
+ " <td>n</td>\n",
+ " <td>u</td>\n",
+ " <td>40002</td>\n",
+ " <td>gpsix0</td>\n",
+ " <td>gpsix0</td>\n",
+ " <td>/data/primary2/gpseg2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>5</td>\n",
+ " <td>3</td>\n",
+ " <td>p</td>\n",
+ " <td>p</td>\n",
+ " <td>n</td>\n",
+ " <td>u</td>\n",
+ " <td>40003</td>\n",
+ " <td>gpsix0</td>\n",
+ " <td>gpsix0</td>\n",
+ " <td>/data/primary3/gpseg3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>6</td>\n",
+ " <td>4</td>\n",
+ " <td>p</td>\n",
+ " <td>p</td>\n",
+ " <td>n</td>\n",
+ " <td>u</td>\n",
+ " <td>40000</td>\n",
+ " <td>gpsix1</td>\n",
+ " <td>gpsix1</td>\n",
+ " <td>/data/primary0/gpseg4</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>7</td>\n",
+ " <td>5</td>\n",
+ " <td>p</td>\n",
+ " <td>p</td>\n",
+ " <td>n</td>\n",
+ " <td>u</td>\n",
+ " <td>40001</td>\n",
+ " <td>gpsix1</td>\n",
+ " <td>gpsix1</td>\n",
+ " <td>/data/primary1/gpseg5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>8</td>\n",
+ " <td>6</td>\n",
+ " <td>p</td>\n",
+ " <td>p</td>\n",
+ " <td>n</td>\n",
+ " <td>u</td>\n",
+ " <td>40002</td>\n",
+ " <td>gpsix1</td>\n",
+ " <td>gpsix1</td>\n",
+ " <td>/data/primary2/gpseg6</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>9</td>\n",
+ " <td>7</td>\n",
+ " <td>p</td>\n",
+ " <td>p</td>\n",
+ " <td>n</td>\n",
+ " <td>u</td>\n",
+ " <td>40003</td>\n",
+ " <td>gpsix1</td>\n",
+ " <td>gpsix1</td>\n",
+ " <td>/data/primary3/gpseg7</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>10</td>\n",
+ " <td>8</td>\n",
+ " <td>p</td>\n",
+ " <td>p</td>\n",
+ " <td>n</td>\n",
+ " <td>u</td>\n",
+ " <td>40000</td>\n",
+ " <td>gpsix2</td>\n",
+ " <td>gpsix2</td>\n",
+ " <td>/data/primary0/gpseg8</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>11</td>\n",
+ " <td>9</td>\n",
+ " <td>p</td>\n",
+ " <td>p</td>\n",
+ " <td>n</td>\n",
+ " <td>u</td>\n",
+ " <td>40001</td>\n",
+ " <td>gpsix2</td>\n",
+ " <td>gpsix2</td>\n",
+ " <td>/data/primary1/gpseg9</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>12</td>\n",
+ " <td>10</td>\n",
+ " <td>p</td>\n",
+ " <td>p</td>\n",
+ " <td>n</td>\n",
+ " <td>u</td>\n",
+ " <td>40002</td>\n",
+ " <td>gpsix2</td>\n",
+ " <td>gpsix2</td>\n",
+ " <td>/data/primary2/gpseg10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>13</td>\n",
+ " <td>11</td>\n",
+ " <td>p</td>\n",
+ " <td>p</td>\n",
+ " <td>n</td>\n",
+ " <td>u</td>\n",
+ " <td>40003</td>\n",
+ " <td>gpsix2</td>\n",
+ " <td>gpsix2</td>\n",
+ " <td>/data/primary3/gpseg11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>14</td>\n",
+ " <td>12</td>\n",
+ " <td>p</td>\n",
+ " <td>p</td>\n",
+ " <td>n</td>\n",
+ " <td>u</td>\n",
+ " <td>40000</td>\n",
+ " <td>gpsix3</td>\n",
+ " <td>gpsix3</td>\n",
+ " <td>/data/primary0/gpseg12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>15</td>\n",
+ " <td>13</td>\n",
+ " <td>p</td>\n",
+ " <td>p</td>\n",
+ " <td>n</td>\n",
+ " <td>u</td>\n",
+ " <td>40001</td>\n",
+ " <td>gpsix3</td>\n",
+ " <td>gpsix3</td>\n",
+ " <td>/data/primary1/gpseg13</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>16</td>\n",
+ " <td>14</td>\n",
+ " <td>p</td>\n",
+ " <td>p</td>\n",
+ " <td>n</td>\n",
+ " <td>u</td>\n",
+ " <td>40002</td>\n",
+ " <td>gpsix3</td>\n",
+ " <td>gpsix3</td>\n",
+ " <td>/data/primary2/gpseg14</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>17</td>\n",
+ " <td>15</td>\n",
+ " <td>p</td>\n",
+ " <td>p</td>\n",
+ " <td>n</td>\n",
+ " <td>u</td>\n",
+ " <td>40003</td>\n",
+ " <td>gpsix3</td>\n",
+ " <td>gpsix3</td>\n",
+ " <td>/data/primary3/gpseg15</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>18</td>\n",
+ " <td>16</td>\n",
+ " <td>p</td>\n",
+ " <td>p</td>\n",
+ " <td>n</td>\n",
+ " <td>u</td>\n",
+ " <td>40000</td>\n",
+ " <td>gpsix4</td>\n",
+ " <td>gpsix4</td>\n",
+ " <td>/data/primary0/gpseg16</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>19</td>\n",
+ " <td>17</td>\n",
+ " <td>p</td>\n",
+ " <td>p</td>\n",
+ " <td>n</td>\n",
+ " <td>u</td>\n",
+ " <td>40001</td>\n",
+ " <td>gpsix4</td>\n",
+ " <td>gpsix4</td>\n",
+ " <td>/data/primary1/gpseg17</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>20</td>\n",
+ " <td>18</td>\n",
+ " <td>p</td>\n",
+ " <td>p</td>\n",
+ " <td>n</td>\n",
+ " <td>u</td>\n",
+ " <td>40002</td>\n",
+ " <td>gpsix4</td>\n",
+ " <td>gpsix4</td>\n",
+ " <td>/data/primary2/gpseg18</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>21</td>\n",
+ " <td>19</td>\n",
+ " <td>p</td>\n",
+ " <td>p</td>\n",
+ " <td>n</td>\n",
+ " <td>u</td>\n",
+ " <td>40003</td>\n",
+ " <td>gpsix4</td>\n",
+ " <td>gpsix4</td>\n",
+ " <td>/data/primary3/gpseg19</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(1, -1, u'p', u'p', u'n', u'u', 5432, u'gpsix0', u'gpsix0', u'/data/master/gpseg-1'),\n",
+ " (2, 0, u'p', u'p', u'n', u'u', 40000, u'gpsix0', u'gpsix0', u'/data/primary0/gpseg0'),\n",
+ " (3, 1, u'p', u'p', u'n', u'u', 40001, u'gpsix0', u'gpsix0', u'/data/primary1/gpseg1'),\n",
+ " (4, 2, u'p', u'p', u'n', u'u', 40002, u'gpsix0', u'gpsix0', u'/data/primary2/gpseg2'),\n",
+ " (5, 3, u'p', u'p', u'n', u'u', 40003, u'gpsix0', u'gpsix0', u'/data/primary3/gpseg3'),\n",
+ " (6, 4, u'p', u'p', u'n', u'u', 40000, u'gpsix1', u'gpsix1', u'/data/primary0/gpseg4'),\n",
+ " (7, 5, u'p', u'p', u'n', u'u', 40001, u'gpsix1', u'gpsix1', u'/data/primary1/gpseg5'),\n",
+ " (8, 6, u'p', u'p', u'n', u'u', 40002, u'gpsix1', u'gpsix1', u'/data/primary2/gpseg6'),\n",
+ " (9, 7, u'p', u'p', u'n', u'u', 40003, u'gpsix1', u'gpsix1', u'/data/primary3/gpseg7'),\n",
+ " (10, 8, u'p', u'p', u'n', u'u', 40000, u'gpsix2', u'gpsix2', u'/data/primary0/gpseg8'),\n",
+ " (11, 9, u'p', u'p', u'n', u'u', 40001, u'gpsix2', u'gpsix2', u'/data/primary1/gpseg9'),\n",
+ " (12, 10, u'p', u'p', u'n', u'u', 40002, u'gpsix2', u'gpsix2', u'/data/primary2/gpseg10'),\n",
+ " (13, 11, u'p', u'p', u'n', u'u', 40003, u'gpsix2', u'gpsix2', u'/data/primary3/gpseg11'),\n",
+ " (14, 12, u'p', u'p', u'n', u'u', 40000, u'gpsix3', u'gpsix3', u'/data/primary0/gpseg12'),\n",
+ " (15, 13, u'p', u'p', u'n', u'u', 40001, u'gpsix3', u'gpsix3', u'/data/primary1/gpseg13'),\n",
+ " (16, 14, u'p', u'p', u'n', u'u', 40002, u'gpsix3', u'gpsix3', u'/data/primary2/gpseg14'),\n",
+ " (17, 15, u'p', u'p', u'n', u'u', 40003, u'gpsix3', u'gpsix3', u'/data/primary3/gpseg15'),\n",
+ " (18, 16, u'p', u'p', u'n', u'u', 40000, u'gpsix4', u'gpsix4', u'/data/primary0/gpseg16'),\n",
+ " (19, 17, u'p', u'p', u'n', u'u', 40001, u'gpsix4', u'gpsix4', u'/data/primary1/gpseg17'),\n",
+ " (20, 18, u'p', u'p', u'n', u'u', 40002, u'gpsix4', u'gpsix4', u'/data/primary2/gpseg18'),\n",
+ " (21, 19, u'p', u'p', u'n', u'u', 40003, u'gpsix4', u'gpsix4', u'/data/primary3/gpseg19')]"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT * FROM gp_segment_configuration ORDER BY dbid;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now JOIN the above 2 tables to build up various distribution rules, depending on your needs.\n",
+ "\n",
+ "We build distribution rules table for 4 VMs:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "Done.\n",
+ "16 rows affected.\n",
+ "16 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>dbid</th>\n",
+ " <th>hostname</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>gpsix0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>gpsix0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>4</td>\n",
+ " <td>gpsix0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>5</td>\n",
+ " <td>gpsix0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>6</td>\n",
+ " <td>gpsix1</td>\n",
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+ " <tr>\n",
+ " <td>7</td>\n",
+ " <td>gpsix1</td>\n",
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+ " <tr>\n",
+ " <td>8</td>\n",
+ " <td>gpsix1</td>\n",
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+ " <tr>\n",
+ " <td>9</td>\n",
+ " <td>gpsix1</td>\n",
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+ " <tr>\n",
+ " <td>10</td>\n",
+ " <td>gpsix2</td>\n",
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+ " <tr>\n",
+ " <td>11</td>\n",
+ " <td>gpsix2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>12</td>\n",
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+ " <td>13</td>\n",
+ " <td>gpsix2</td>\n",
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+ " <td>14</td>\n",
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+ " </tr>\n",
+ " <tr>\n",
+ " <td>15</td>\n",
+ " <td>gpsix3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>16</td>\n",
+ " <td>gpsix3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>17</td>\n",
+ " <td>gpsix3</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(2, u'gpsix0'),\n",
+ " (3, u'gpsix0'),\n",
+ " (4, u'gpsix0'),\n",
+ " (5, u'gpsix0'),\n",
+ " (6, u'gpsix1'),\n",
+ " (7, u'gpsix1'),\n",
+ " (8, u'gpsix1'),\n",
+ " (9, u'gpsix1'),\n",
+ " (10, u'gpsix2'),\n",
+ " (11, u'gpsix2'),\n",
+ " (12, u'gpsix2'),\n",
+ " (13, u'gpsix2'),\n",
+ " (14, u'gpsix3'),\n",
+ " (15, u'gpsix3'),\n",
+ " (16, u'gpsix3'),\n",
+ " (17, u'gpsix3')]"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS segments_to_use_4VMs;\n",
+ "CREATE TABLE segments_to_use_4VMs AS\n",
+ " SELECT DISTINCT dbid, hostname FROM gp_segment_configuration JOIN host_gpu_mapping_tf USING (hostname)\n",
+ " WHERE role='p' AND content>=0 AND hostname!='gpsix4';\n",
+ "SELECT * FROM segments_to_use_4VMs ORDER BY hostname, dbid;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Run the preprocessor to generate the packed output table on the segments we want to use for training:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "Done.\n",
+ "1 rows affected.\n",
+ "16 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>__dist_key__</th>\n",
+ " <th>independent_var_shape</th>\n",
+ " <th>dependent_var_shape</th>\n",
+ " <th>buffer_id</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>0</td>\n",
+ " <td>[3125, 32, 32, 3]</td>\n",
+ " <td>[3125, 10]</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>[3125, 32, 32, 3]</td>\n",
+ " <td>[3125, 10]</td>\n",
+ " <td>4</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>[3125, 32, 32, 3]</td>\n",
+ " <td>[3125, 10]</td>\n",
+ " <td>9</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>[3125, 32, 32, 3]</td>\n",
+ " <td>[3125, 10]</td>\n",
+ " <td>7</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>4</td>\n",
+ " <td>[3125, 32, 32, 3]</td>\n",
+ " <td>[3125, 10]</td>\n",
+ " <td>14</td>\n",
+ " </tr>\n",
+ " <tr>\n",
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+ " <td>[3125, 32, 32, 3]</td>\n",
+ " <td>[3125, 10]</td>\n",
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+ " <td>9</td>\n",
+ " <td>[3125, 32, 32, 3]</td>\n",
+ " <td>[3125, 10]</td>\n",
+ " <td>13</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>12</td>\n",
+ " <td>[3125, 32, 32, 3]</td>\n",
+ " <td>[3125, 10]</td>\n",
+ " <td>15</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>14</td>\n",
+ " <td>[3125, 32, 32, 3]</td>\n",
+ " <td>[3125, 10]</td>\n",
+ " <td>6</td>\n",
+ " </tr>\n",
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+ " <td>19</td>\n",
+ " <td>[3125, 32, 32, 3]</td>\n",
+ " <td>[3125, 10]</td>\n",
+ " <td>12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>27</td>\n",
+ " <td>[3125, 32, 32, 3]</td>\n",
+ " <td>[3125, 10]</td>\n",
+ " <td>10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>28</td>\n",
+ " <td>[3125, 32, 32, 3]</td>\n",
+ " <td>[3125, 10]</td>\n",
+ " <td>5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>29</td>\n",
+ " <td>[3125, 32, 32, 3]</td>\n",
+ " <td>[3125, 10]</td>\n",
+ " <td>8</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>34</td>\n",
+ " <td>[3125, 32, 32, 3]</td>\n",
+ " <td>[3125, 10]</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>55</td>\n",
+ " <td>[3125, 32, 32, 3]</td>\n",
+ " <td>[3125, 10]</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>56</td>\n",
+ " <td>[3125, 32, 32, 3]</td>\n",
+ " <td>[3125, 10]</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
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+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS cifar10_train_packed, cifar10_train_packed_summary;\n",
+ "\n",
+ "SELECT madlib.training_preprocessor_dl('cifar10_train', -- Source table\n",
+ " 'cifar10_train_packed', -- Output table\n",
+ " 'y', -- Dependent variable\n",
+ " 'x', -- Independent variable\n",
+ " NULL, -- Buffer size\n",
+ " 256.0, -- Normalizing constant\n",
+ " NULL, -- Number of classes\n",
+ " 'segments_to_use_4VMs' -- Distribution rules\n",
+ " );\n",
+ "\n",
+ "SELECT __dist_key__, independent_var_shape, dependent_var_shape, buffer_id FROM cifar10_train_packed ORDER BY __dist_key__;"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
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+ " <th>independent_varname</th>\n",
+ " <th>dependent_vartype</th>\n",
+ " <th>class_values</th>\n",
+ " <th>buffer_size</th>\n",
+ " <th>normalizing_const</th>\n",
+ " <th>num_classes</th>\n",
+ " <th>distribution_rules</th>\n",
+ " <th>__internal_gpu_config__</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>cifar10_train</td>\n",
+ " <td>cifar10_train_packed</td>\n",
+ " <td>y</td>\n",
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+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT * FROM cifar10_train_packed_summary;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Same for validation dataset:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "Done.\n",
+ "1 rows affected.\n",
+ "16 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
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+ " <th>buffer_id</th>\n",
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+ " (56, [625, 32, 32, 3], [625, 10], 2)]"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS cifar10_val_packed, cifar10_val_packed_summary;\n",
+ "\n",
+ "SELECT madlib.validation_preprocessor_dl('cifar10_val', -- Source table\n",
+ " 'cifar10_val_packed', -- Output table\n",
+ " 'y', -- Dependent variable\n",
+ " 'x', -- Independent variable\n",
+ " 'cifar10_train_packed', -- From training preprocessor step\n",
+ " NULL, -- Buffer size\n",
+ " 'segments_to_use_4VMs' -- Distribution rules\n",
+ " ); \n",
+ "\n",
+ "SELECT __dist_key__, independent_var_shape, dependent_var_shape, buffer_id FROM cifar10_val_packed ORDER BY __dist_key__;"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>source_table</th>\n",
+ " <th>output_table</th>\n",
+ " <th>dependent_varname</th>\n",
+ " <th>independent_varname</th>\n",
+ " <th>dependent_vartype</th>\n",
+ " <th>class_values</th>\n",
+ " <th>buffer_size</th>\n",
+ " <th>normalizing_const</th>\n",
+ " <th>num_classes</th>\n",
+ " <th>distribution_rules</th>\n",
+ " <th>__internal_gpu_config__</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>cifar10_val</td>\n",
+ " <td>cifar10_val_packed</td>\n",
+ " <td>y</td>\n",
+ " <td>x</td>\n",
+ " <td>smallint</td>\n",
+ " <td>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]</td>\n",
+ " <td>625</td>\n",
+ " <td>256.0</td>\n",
+ " <td>10</td>\n",
+ " <td>[2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]</td>\n",
+ " <td>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'cifar10_val', u'cifar10_val_packed', u'y', u'x', u'smallint', [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], 625, 256.0, 10, [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15])]"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT * FROM cifar10_val_packed_summary;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"arch\"></a>\n",
+ "# 3. Define and load model architectures\n",
+ "\n",
+ "Model architecture from https://keras.io/examples/cifar10_cnn/"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "num_classes = 10"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "WARNING:tensorflow:From /Users/fmcquillan/Library/Python/2.7/lib/python/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
+ "\n",
+ "WARNING:tensorflow:From /Users/fmcquillan/Library/Python/2.7/lib/python/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
+ "\n",
+ "WARNING:tensorflow:From /Users/fmcquillan/Library/Python/2.7/lib/python/site-packages/keras/backend/tensorflow_backend.py:3976: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
+ "\n",
+ "WARNING:tensorflow:From /Users/fmcquillan/Library/Python/2.7/lib/python/site-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
+ "\n",
+ "WARNING:tensorflow:From /Users/fmcquillan/Library/Python/2.7/lib/python/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
+ "Instructions for updating:\n",
+ "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
+ "_________________________________________________________________\n",
+ "Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ "conv2d_1 (Conv2D) (None, 32, 32, 32) 896 \n",
+ "_________________________________________________________________\n",
+ "activation_1 (Activation) (None, 32, 32, 32) 0 \n",
+ "_________________________________________________________________\n",
+ "conv2d_2 (Conv2D) (None, 30, 30, 32) 9248 \n",
+ "_________________________________________________________________\n",
+ "activation_2 (Activation) (None, 30, 30, 32) 0 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_1 (MaxPooling2 (None, 15, 15, 32) 0 \n",
+ "_________________________________________________________________\n",
+ "dropout_1 (Dropout) (None, 15, 15, 32) 0 \n",
+ "_________________________________________________________________\n",
+ "conv2d_3 (Conv2D) (None, 15, 15, 64) 18496 \n",
+ "_________________________________________________________________\n",
+ "activation_3 (Activation) (None, 15, 15, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "conv2d_4 (Conv2D) (None, 13, 13, 64) 36928 \n",
+ "_________________________________________________________________\n",
+ "activation_4 (Activation) (None, 13, 13, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_2 (MaxPooling2 (None, 6, 6, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "dropout_2 (Dropout) (None, 6, 6, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "flatten_1 (Flatten) (None, 2304) 0 \n",
+ "_________________________________________________________________\n",
+ "dense_1 (Dense) (None, 512) 1180160 \n",
+ "_________________________________________________________________\n",
+ "activation_5 (Activation) (None, 512) 0 \n",
+ "_________________________________________________________________\n",
+ "dropout_3 (Dropout) (None, 512) 0 \n",
+ "_________________________________________________________________\n",
+ "dense_2 (Dense) (None, 10) 5130 \n",
+ "_________________________________________________________________\n",
+ "activation_6 (Activation) (None, 10) 0 \n",
+ "=================================================================\n",
+ "Total params: 1,250,858\n",
+ "Trainable params: 1,250,858\n",
+ "Non-trainable params: 0\n",
+ "_________________________________________________________________\n"
+ ]
+ }
+ ],
+ "source": [
+ "model1 = Sequential()\n",
+ "\n",
+ "model1.add(Conv2D(32, (3, 3), padding='same',\n",
+ " input_shape=x_train.shape[1:]))\n",
+ "model1.add(Activation('relu'))\n",
+ "model1.add(Conv2D(32, (3, 3)))\n",
+ "model1.add(Activation('relu'))\n",
+ "model1.add(MaxPooling2D(pool_size=(2, 2)))\n",
+ "model1.add(Dropout(0.25))\n",
+ "\n",
+ "model1.add(Conv2D(64, (3, 3), padding='same'))\n",
+ "model1.add(Activation('relu'))\n",
+ "model1.add(Conv2D(64, (3, 3)))\n",
+ "model1.add(Activation('relu'))\n",
+ "model1.add(MaxPooling2D(pool_size=(2, 2)))\n",
+ "model1.add(Dropout(0.25))\n",
+ "\n",
+ "model1.add(Flatten())\n",
+ "model1.add(Dense(512))\n",
+ "model1.add(Activation('relu'))\n",
+ "model1.add(Dropout(0.5))\n",
+ "model1.add(Dense(num_classes))\n",
+ "model1.add(Activation('softmax'))\n",
+ "\n",
+ "model1.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'{\"class_name\": \"Sequential\", \"keras_version\": \"2.2.4\", \"config\": {\"layers\": [{\"class_name\": \"Conv2D\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"conv2d_1\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"linear\", \"trainable\": true, \"data_format\": \"channels_last\", \"filters\": 32, \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"batch_input_shape\": [null, 32, 32, 3], \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"Activation\", \"config\": {\"activation\": \"relu\", \"trainable\": true, \"name\": \"activation_1\"}}, {\"class_name\": \"Conv2D\", \"config\": 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\"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"conv2d_4\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"linear\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"valid\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 64, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"Activation\", \"config\": {\"activation\": \"relu\", \"trainable\": true, \"name\": \"activation_4\"}}, {\"class_name\": \"MaxPooling2D\", \"config\": {\"name\": \"max_pooling2d_2\", \"trainable\": true, \"data_format\": \"channels_last\", \"pool_size\": [2, 2], \"padding\": \"valid\", \"strides\": [2, 2]}}, {\"class_name\": \"Dropout\", \"config\": {\"rate\": 0.25, \"noise_shape\": null, \"trainable\": true, \"seed\": null, \"name\": \"dropout_2\"}}, {\"class_name\": \"Flatten\", \"config\": {\"trainable\": true, \"name\": \"flatten_1\", \"data_format\": \"channels_last\"}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_1\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"linear\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 512, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Activation\", \"config\": {\"activation\": \"relu\", \"trainable\": true, \"name\": \"activation_5\"}}, {\"class_name\": \"Dropout\", \"config\": {\"rate\": 0.5, \"noise_shape\": null, \"trainable\": true, \"seed\": null, \"name\": \"dropout_3\"}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_2\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"linear\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Activation\", \"config\": {\"activation\": \"softmax\", \"trainable\": true, \"name\": \"activation_6\"}}], \"name\": \"sequential_2\"}, \"backend\": \"tensorflow\"}'"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model1.to_json()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Model architecture from https://machinelearningmastery.com/how-to-develop-a-cnn-from-scratch-for-cifar-10-photo-classification/"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "WARNING:tensorflow:From /Users/fmcquillan/Library/Python/2.7/lib/python/site-packages/keras/backend/tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
+ "\n",
+ "WARNING:tensorflow:From /Users/fmcquillan/Library/Python/2.7/lib/python/site-packages/keras/backend/tensorflow_backend.py:1834: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead.\n",
+ "\n",
+ "_________________________________________________________________\n",
+ "Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ "conv2d_5 (Conv2D) (None, 32, 32, 32) 896 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_1 (Batch (None, 32, 32, 32) 128 \n",
+ "_________________________________________________________________\n",
+ "conv2d_6 (Conv2D) (None, 32, 32, 32) 9248 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_2 (Batch (None, 32, 32, 32) 128 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_3 (MaxPooling2 (None, 16, 16, 32) 0 \n",
+ "_________________________________________________________________\n",
+ "dropout_4 (Dropout) (None, 16, 16, 32) 0 \n",
+ "_________________________________________________________________\n",
+ "conv2d_7 (Conv2D) (None, 16, 16, 64) 18496 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_3 (Batch (None, 16, 16, 64) 256 \n",
+ "_________________________________________________________________\n",
+ "conv2d_8 (Conv2D) (None, 16, 16, 64) 36928 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_4 (Batch (None, 16, 16, 64) 256 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_4 (MaxPooling2 (None, 8, 8, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "dropout_5 (Dropout) (None, 8, 8, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "conv2d_9 (Conv2D) (None, 8, 8, 128) 73856 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_5 (Batch (None, 8, 8, 128) 512 \n",
+ "_________________________________________________________________\n",
+ "conv2d_10 (Conv2D) (None, 8, 8, 128) 147584 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_6 (Batch (None, 8, 8, 128) 512 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_5 (MaxPooling2 (None, 4, 4, 128) 0 \n",
+ "_________________________________________________________________\n",
+ "dropout_6 (Dropout) (None, 4, 4, 128) 0 \n",
+ "_________________________________________________________________\n",
+ "flatten_2 (Flatten) (None, 2048) 0 \n",
+ "_________________________________________________________________\n",
+ "dense_3 (Dense) (None, 128) 262272 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_7 (Batch (None, 128) 512 \n",
+ "_________________________________________________________________\n",
+ "dropout_7 (Dropout) (None, 128) 0 \n",
+ "_________________________________________________________________\n",
+ "dense_4 (Dense) (None, 10) 1290 \n",
+ "=================================================================\n",
+ "Total params: 552,874\n",
+ "Trainable params: 551,722\n",
+ "Non-trainable params: 1,152\n",
+ "_________________________________________________________________\n"
+ ]
+ }
+ ],
+ "source": [
+ "model2 = Sequential()\n",
+ "\n",
+ "model2.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same', input_shape=(32, 32, 3)))\n",
+ "model2.add(BatchNormalization())\n",
+ "model2.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
+ "model2.add(BatchNormalization())\n",
+ "model2.add(MaxPooling2D((2, 2)))\n",
+ "model2.add(Dropout(0.2))\n",
+ "model2.add(Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
+ "model2.add(BatchNormalization())\n",
+ "model2.add(Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
+ "model2.add(BatchNormalization())\n",
+ "model2.add(MaxPooling2D((2, 2)))\n",
+ "model2.add(Dropout(0.3))\n",
+ "model2.add(Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
+ "model2.add(BatchNormalization())\n",
+ "model2.add(Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
+ "model2.add(BatchNormalization())\n",
+ "model2.add(MaxPooling2D((2, 2)))\n",
+ "model2.add(Dropout(0.4))\n",
+ "model2.add(Flatten())\n",
+ "model2.add(Dense(128, activation='relu', kernel_initializer='he_uniform'))\n",
+ "model2.add(BatchNormalization())\n",
+ "model2.add(Dropout(0.5))\n",
+ "model2.add(Dense(10, activation='softmax'))\n",
+ "\n",
+ "model2.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'{\"class_name\": \"Sequential\", \"keras_version\": \"2.2.4\", \"config\": {\"layers\": [{\"class_name\": \"Conv2D\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"conv2d_5\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"data_format\": \"channels_last\", \"filters\": 32, \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"batch_input_shape\": [null, 32, 32, 3], \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"BatchNormalization\", \"config\": {\"beta_constraint\": null, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"name\": \"batch_normalization_1\", \"epsilon\": 0.001, \"trainable\": true, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"scale\": true, \"axis\": -1, \"gamma_constraint\": null, \"gamma_regularizer\": null, \"beta_regularizer\": null, \"momentum\": 0.99, \"center\": true}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"conv2d_6\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 32, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"BatchNormalization\", \"config\": {\"beta_constraint\": null, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"name\": \"batch_normalization_2\", \"epsilon\": 0.001, \"trainable\": true, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"scale\": true, \"axis\": -1, \"gamma_constraint\": null, \"gamma_regularizer\": null, \"beta_regularizer\": null, \"momentum\": 0.99, \"center\": true}}, {\"class_name\": \"MaxPooling2D\", \"config\": {\"name\": \"max_pooling2d_3\", \"trainable\": true, \"data_format\": \"channels_last\", \"pool_size\": [2, 2], \"padding\": \"valid\", \"strides\": [2, 2]}}, {\"class_name\": \"Dropout\", \"config\": {\"rate\": 0.2, \"noise_shape\": null, \"trainable\": true, \"seed\": null, \"name\": \"dropout_4\"}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"conv2d_7\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 64, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"BatchNormalization\", \"config\": {\"beta_constraint\": null, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"name\": \"batch_normalization_3\", \"epsilon\": 0.001, \"trainable\": true, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"scale\": true, \"axis\": -1, \"gamma_constraint\": null, \"gamma_regularizer\": null, \"beta_regularizer\": null, \"momentum\": 0.99, \"center\": true}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"conv2d_8\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 64, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"BatchNormalization\", \"config\": {\"beta_constraint\": null, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"name\": \"batch_normalization_4\", \"epsilon\": 0.001, \"trainable\": true, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"scale\": true, \"axis\": -1, \"gamma_constraint\": null, \"gamma_regularizer\": null, \"beta_regularizer\": null, \"momentum\": 0.99, \"center\": true}}, {\"class_name\": \"MaxPooling2D\", \"config\": {\"name\": \"max_pooling2d_4\", \"trainable\": true, \"data_format\": \"channels_last\", \"pool_size\": [2, 2], \"padding\": \"valid\", \"strides\": [2, 2]}}, {\"class_name\": \"Dropout\", \"config\": {\"rate\": 0.3, \"noise_shape\": null, \"trainable\": true, \"seed\": null, \"name\": \"dropout_5\"}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"conv2d_9\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 128, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"BatchNormalization\", \"config\": {\"beta_constraint\": null, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"name\": \"batch_normalization_5\", \"epsilon\": 0.001, \"trainable\": true, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"scale\": true, \"axis\": -1, \"gamma_constraint\": null, \"gamma_regularizer\": null, \"beta_regularizer\": null, \"momentum\": 0.99, \"center\": true}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"conv2d_10\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 128, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"BatchNormalization\", \"config\": {\"beta_constraint\": null, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"name\": \"batch_normalization_6\", \"epsilon\": 0.001, \"trainable\": true, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"scale\": true, \"axis\": -1, \"gamma_constraint\": null, \"gamma_regularizer\": null, \"beta_regularizer\": null, \"momentum\": 0.99, \"center\": true}}, {\"class_name\": \"MaxPooling2D\", \"config\": {\"name\": \"max_pooling2d_5\", \"trainable\": true, \"data_format\": \"channels_last\", \"pool_size\": [2, 2], \"padding\": \"valid\", \"strides\": [2, 2]}}, {\"class_name\": \"Dropout\", \"config\": {\"rate\": 0.4, \"noise_shape\": null, \"trainable\": true, \"seed\": null, \"name\": \"dropout_6\"}}, {\"class_name\": \"Flatten\", \"config\": {\"trainable\": true, \"name\": \"flatten_2\", \"data_format\": \"channels_last\"}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"dense_3\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 128, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"BatchNormalization\", \"config\": {\"beta_constraint\": null, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"name\": \"batch_normalization_7\", \"epsilon\": 0.001, \"trainable\": true, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"scale\": true, \"axis\": -1, \"gamma_constraint\": null, \"gamma_regularizer\": null, \"beta_regularizer\": null, \"momentum\": 0.99, \"center\": true}}, {\"class_name\": \"Dropout\", \"config\": {\"rate\": 0.5, \"noise_shape\": null, \"trainable\": true, \"seed\": null, \"name\": \"dropout_7\"}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_4\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"softmax\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}], \"name\": \"sequential_3\"}, \"backend\": \"tensorflow\"}'"
+ ]
+ },
+ "execution_count": 28,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model2.to_json()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Another model architecture from https://machinelearningmastery.com/how-to-develop-a-cnn-from-scratch-for-cifar-10-photo-classification/"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "_________________________________________________________________\n",
+ "Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ "conv2d_11 (Conv2D) (None, 32, 32, 32) 896 \n",
+ "_________________________________________________________________\n",
+ "conv2d_12 (Conv2D) (None, 32, 32, 32) 9248 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_6 (MaxPooling2 (None, 16, 16, 32) 0 \n",
+ "_________________________________________________________________\n",
+ "dropout_8 (Dropout) (None, 16, 16, 32) 0 \n",
+ "_________________________________________________________________\n",
+ "conv2d_13 (Conv2D) (None, 16, 16, 64) 18496 \n",
+ "_________________________________________________________________\n",
+ "conv2d_14 (Conv2D) (None, 16, 16, 64) 36928 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_7 (MaxPooling2 (None, 8, 8, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "dropout_9 (Dropout) (None, 8, 8, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "conv2d_15 (Conv2D) (None, 8, 8, 128) 73856 \n",
+ "_________________________________________________________________\n",
+ "conv2d_16 (Conv2D) (None, 8, 8, 128) 147584 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_8 (MaxPooling2 (None, 4, 4, 128) 0 \n",
+ "_________________________________________________________________\n",
+ "dropout_10 (Dropout) (None, 4, 4, 128) 0 \n",
+ "_________________________________________________________________\n",
+ "flatten_3 (Flatten) (None, 2048) 0 \n",
+ "_________________________________________________________________\n",
+ "dense_5 (Dense) (None, 128) 262272 \n",
+ "_________________________________________________________________\n",
+ "dropout_11 (Dropout) (None, 128) 0 \n",
+ "_________________________________________________________________\n",
+ "dense_6 (Dense) (None, 10) 1290 \n",
+ "=================================================================\n",
+ "Total params: 550,570\n",
+ "Trainable params: 550,570\n",
+ "Non-trainable params: 0\n",
+ "_________________________________________________________________\n"
+ ]
+ }
+ ],
+ "source": [
+ "model3 = Sequential()\n",
+ "\n",
+ "model3.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same', input_shape=(32, 32, 3)))\n",
+ "model3.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
+ "model3.add(MaxPooling2D((2, 2)))\n",
+ "model3.add(Dropout(0.2))\n",
+ "model3.add(Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
+ "model3.add(Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
+ "model3.add(MaxPooling2D((2, 2)))\n",
+ "model3.add(Dropout(0.3))\n",
+ "model3.add(Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
+ "model3.add(Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
+ "model3.add(MaxPooling2D((2, 2)))\n",
+ "model3.add(Dropout(0.4))\n",
+ "model3.add(Flatten())\n",
+ "model3.add(Dense(128, activation='relu', kernel_initializer='he_uniform'))\n",
+ "model3.add(Dropout(0.5))\n",
+ "model3.add(Dense(10, activation='softmax'))\n",
+ "\n",
+ "model3.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'{\"class_name\": \"Sequential\", \"keras_version\": \"2.1.6\", \"config\": [{\"class_name\": \"Conv2D\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"conv2d_17\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"data_format\": \"channels_last\", \"filters\": 32, \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"batch_input_shape\": [null, 32, 32, 3], \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"conv2d_18\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 32, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"MaxPooling2D\", \"config\": {\"name\": \"max_pooling2d_9\", \"trainable\": true, \"data_format\": \"channels_last\", \"pool_size\": [2, 2], \"padding\": \"valid\", \"strides\": [2, 2]}}, {\"class_name\": \"Dropout\", \"config\": {\"rate\": 0.2, \"noise_shape\": null, \"trainable\": true, \"seed\": null, \"name\": \"dropout_12\"}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"conv2d_19\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 64, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"conv2d_20\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 64, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"MaxPooling2D\", \"config\": {\"name\": \"max_pooling2d_10\", \"trainable\": true, \"data_format\": \"channels_last\", \"pool_size\": [2, 2], \"padding\": \"valid\", \"strides\": [2, 2]}}, {\"class_name\": \"Dropout\", \"config\": {\"rate\": 0.3, \"noise_shape\": null, \"trainable\": true, \"seed\": null, \"name\": \"dropout_13\"}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"conv2d_21\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 128, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"conv2d_22\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 128, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"MaxPooling2D\", \"config\": {\"name\": \"max_pooling2d_11\", \"trainable\": true, \"data_format\": \"channels_last\", \"pool_size\": [2, 2], \"padding\": \"valid\", \"strides\": [2, 2]}}, {\"class_name\": \"Dropout\", \"config\": {\"rate\": 0.4, \"noise_shape\": null, \"trainable\": true, \"seed\": null, \"name\": \"dropout_14\"}}, {\"class_name\": \"Flatten\", \"config\": {\"trainable\": true, \"name\": \"flatten_4\", \"data_format\": \"channels_last\"}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"dense_7\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 128, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dropout\", \"config\": {\"rate\": 0.5, \"noise_shape\": null, \"trainable\": true, \"seed\": null, \"name\": \"dropout_15\"}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_8\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"softmax\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}], \"backend\": \"tensorflow\"}'"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model3.to_json()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Load into model architecture table using psycopg2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "Done.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 31,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "3 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>model_id</th>\n",
+ " <th>name</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>CNN from Keras docs for CIFAR-10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>CNN from Jason Brownlee blog post</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>CNN from Jason Brownlee blog post - no batch normalization</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(1, u'CNN from Keras docs for CIFAR-10'),\n",
+ " (2, u'CNN from Jason Brownlee blog post'),\n",
+ " (3, u'CNN from Jason Brownlee blog post - no batch normalization')]"
+ ]
+ },
+ "execution_count": 31,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import psycopg2 as p2\n",
+ "#conn = p2.connect('postgresql://gpadmin@35.239.240.26:5432/madlib')\n",
+ "#conn = p2.connect('postgresql://fmcquillan@localhost:5432/madlib')\n",
+ "conn = p2.connect('postgresql://gpadmin@localhost:8000/cifar_places')\n",
+ "cur = conn.cursor()\n",
+ "\n",
+ "%sql DROP TABLE IF EXISTS model_arch_library;\n",
+ "query = \"SELECT madlib.load_keras_model('model_arch_library', %s, NULL, %s)\"\n",
+ "cur.execute(query,[model1.to_json(), \"CNN from Keras docs for CIFAR-10\"])\n",
+ "conn.commit()\n",
+ "\n",
+ "query = \"SELECT madlib.load_keras_model('model_arch_library', %s, NULL, %s)\"\n",
+ "cur.execute(query,[model2.to_json(), \"CNN from Jason Brownlee blog post\"])\n",
+ "conn.commit()\n",
+ "\n",
+ "query = \"SELECT madlib.load_keras_model('model_arch_library', %s, NULL, %s)\"\n",
+ "cur.execute(query,[model3.to_json(), \"CNN from Jason Brownlee blog post - no batch normalization\"])\n",
+ "conn.commit()\n",
+ "\n",
+ "# check model loaded OK\n",
+ "%sql SELECT model_id, name FROM model_arch_library ORDER BY model_id;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"mst\"></a>\n",
+ "# 4. Define and load model selection tuples"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Select the model(s) from the model architecture table that you want to run, along with the compile and fit parameters. Permutations for grid search will be created for the set of model selection parameters will be loaded:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "Done.\n",
+ "1 rows affected.\n",
+ "16 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>mst_key</th>\n",
+ " <th>model_id</th>\n",
+ " <th>compile_params</th>\n",
+ " <th>fit_params</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='adam(lr=0.0001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=64,epochs=5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='adam(lr=0.0001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=128,epochs=5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='rmsprop(lr=0.0001, decay=1e-6)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=64,epochs=5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>4</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='rmsprop(lr=0.0001, decay=1e-6)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=128,epochs=5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>5</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='rmsprop(lr=0.001, decay=1e-6)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=64,epochs=5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>6</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='rmsprop(lr=0.001, decay=1e-6)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=128,epochs=5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>7</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='adam(lr=0.001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=64,epochs=5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>8</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='adam(lr=0.001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=128,epochs=5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>9</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='adam(lr=0.0001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=64,epochs=5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>10</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='adam(lr=0.0001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=128,epochs=5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>11</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='rmsprop(lr=0.0001, decay=1e-6)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=64,epochs=5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>12</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='rmsprop(lr=0.0001, decay=1e-6)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=128,epochs=5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>13</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='rmsprop(lr=0.001, decay=1e-6)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=64,epochs=5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>14</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='rmsprop(lr=0.001, decay=1e-6)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=128,epochs=5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>15</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='adam(lr=0.001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=64,epochs=5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>16</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='adam(lr=0.001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=128,epochs=5</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(1, 1, u\"loss='categorical_crossentropy',optimizer='adam(lr=0.0001)',metrics=['accuracy']\", u'batch_size=64,epochs=5'),\n",
+ " (2, 1, u\"loss='categorical_crossentropy',optimizer='adam(lr=0.0001)',metrics=['accuracy']\", u'batch_size=128,epochs=5'),\n",
+ " (3, 1, u\"loss='categorical_crossentropy',optimizer='rmsprop(lr=0.0001, decay=1e-6)',metrics=['accuracy']\", u'batch_size=64,epochs=5'),\n",
+ " (4, 1, u\"loss='categorical_crossentropy',optimizer='rmsprop(lr=0.0001, decay=1e-6)',metrics=['accuracy']\", u'batch_size=128,epochs=5'),\n",
+ " (5, 1, u\"loss='categorical_crossentropy',optimizer='rmsprop(lr=0.001, decay=1e-6)',metrics=['accuracy']\", u'batch_size=64,epochs=5'),\n",
+ " (6, 1, u\"loss='categorical_crossentropy',optimizer='rmsprop(lr=0.001, decay=1e-6)',metrics=['accuracy']\", u'batch_size=128,epochs=5'),\n",
+ " (7, 1, u\"loss='categorical_crossentropy',optimizer='adam(lr=0.001)',metrics=['accuracy']\", u'batch_size=64,epochs=5'),\n",
+ " (8, 1, u\"loss='categorical_crossentropy',optimizer='adam(lr=0.001)',metrics=['accuracy']\", u'batch_size=128,epochs=5'),\n",
+ " (9, 2, u\"loss='categorical_crossentropy',optimizer='adam(lr=0.0001)',metrics=['accuracy']\", u'batch_size=64,epochs=5'),\n",
+ " (10, 2, u\"loss='categorical_crossentropy',optimizer='adam(lr=0.0001)',metrics=['accuracy']\", u'batch_size=128,epochs=5'),\n",
+ " (11, 2, u\"loss='categorical_crossentropy',optimizer='rmsprop(lr=0.0001, decay=1e-6)',metrics=['accuracy']\", u'batch_size=64,epochs=5'),\n",
+ " (12, 2, u\"loss='categorical_crossentropy',optimizer='rmsprop(lr=0.0001, decay=1e-6)',metrics=['accuracy']\", u'batch_size=128,epochs=5'),\n",
+ " (13, 2, u\"loss='categorical_crossentropy',optimizer='rmsprop(lr=0.001, decay=1e-6)',metrics=['accuracy']\", u'batch_size=64,epochs=5'),\n",
+ " (14, 2, u\"loss='categorical_crossentropy',optimizer='rmsprop(lr=0.001, decay=1e-6)',metrics=['accuracy']\", u'batch_size=128,epochs=5'),\n",
+ " (15, 2, u\"loss='categorical_crossentropy',optimizer='adam(lr=0.001)',metrics=['accuracy']\", u'batch_size=64,epochs=5'),\n",
+ " (16, 2, u\"loss='categorical_crossentropy',optimizer='adam(lr=0.001)',metrics=['accuracy']\", u'batch_size=128,epochs=5')]"
+ ]
+ },
+ "execution_count": 32,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS mst_table, mst_table_summary;\n",
+ "\n",
+ "SELECT madlib.load_model_selection_table('model_arch_library', -- model architecture table\n",
+ " 'mst_table', -- model selection table output\n",
+ " ARRAY[1,2], -- model ids from model architecture table\n",
+ " ARRAY[ -- compile params \n",
+ " $$loss='categorical_crossentropy',optimizer='rmsprop(lr=0.0001, decay=1e-6)',metrics=['accuracy']$$,\n",
+ " $$loss='categorical_crossentropy',optimizer='rmsprop(lr=0.001, decay=1e-6)',metrics=['accuracy']$$,\n",
+ " $$loss='categorical_crossentropy',optimizer='adam(lr=0.0001)',metrics=['accuracy']$$,\n",
+ " $$loss='categorical_crossentropy',optimizer='adam(lr=0.001)',metrics=['accuracy']$$\n",
+ " ],\n",
+ " ARRAY[ -- fit params\n",
+ " $$batch_size=64,epochs=5$$, \n",
+ " $$batch_size=128,epochs=5$$\n",
+ " ]\n",
+ " );\n",
+ " \n",
+ "SELECT * FROM mst_table ORDER BY mst_key;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This is the name of the model architecture table that corresponds to the model selection table:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>model_arch_table</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>model_arch_library</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'model_arch_library',)]"
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT * FROM mst_table_summary;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"train\"></a>\n",
+ "# 5. Train\n",
+ "Train multiple models:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>madlib_keras_fit_multiple_model</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td></td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[('',)]"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS cifar10_multi_model, cifar10_multi_model_summary, cifar10_multi_model_info;\n",
+ "\n",
+ "SELECT madlib.madlib_keras_fit_multiple_model('cifar10_train_packed', -- source_table\n",
+ " 'cifar10_multi_model', -- model_output_table\n",
+ " 'mst_table', -- model_selection_table\n",
+ " 10, -- num_iterations\n",
+ " TRUE, -- use gpus\n",
+ " 'cifar10_val_packed', -- validation dataset\n",
+ " 1 -- metrics compute frequency\n",
+ " );"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "View the model summary:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>source_table</th>\n",
+ " <th>validation_table</th>\n",
+ " <th>model</th>\n",
+ " <th>model_info</th>\n",
+ " <th>dependent_varname</th>\n",
+ " <th>independent_varname</th>\n",
+ " <th>model_arch_table</th>\n",
+ " <th>num_iterations</th>\n",
+ " <th>metrics_compute_frequency</th>\n",
+ " <th>warm_start</th>\n",
+ " <th>name</th>\n",
+ " <th>description</th>\n",
+ " <th>start_training_time</th>\n",
+ " <th>end_training_time</th>\n",
+ " <th>madlib_version</th>\n",
+ " <th>num_classes</th>\n",
+ " <th>class_values</th>\n",
+ " <th>dependent_vartype</th>\n",
+ " <th>normalizing_const</th>\n",
+ " <th>metrics_iters</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>cifar10_train_packed</td>\n",
+ " <td>cifar10_val_packed</td>\n",
+ " <td>cifar10_multi_model</td>\n",
+ " <td>cifar10_multi_model_info</td>\n",
+ " <td>y</td>\n",
+ " <td>x</td>\n",
+ " <td>model_arch_library</td>\n",
+ " <td>10</td>\n",
+ " <td>1</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>2020-03-17 23:21:35.497938</td>\n",
+ " <td>2020-03-17 23:50:01.109448</td>\n",
+ " <td>1.17-dev</td>\n",
+ " <td>10</td>\n",
+ " <td>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]</td>\n",
+ " <td>smallint</td>\n",
+ " <td>256.0</td>\n",
+ " <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'cifar10_train_packed', u'cifar10_val_packed', u'cifar10_multi_model', u'cifar10_multi_model_info', u'y', u'x', u'model_arch_library', 10, 1, False, None, None, datetime.datetime(2020, 3, 17, 23, 21, 35, 497938), datetime.datetime(2020, 3, 17, 23, 50, 1, 109448), u'1.17-dev', 10, [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], u'smallint', 256.0, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10])]"
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT * FROM cifar10_multi_model_summary;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "View performance of each model:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "16 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>mst_key</th>\n",
+ " <th>model_id</th>\n",
+ " <th>compile_params</th>\n",
+ " <th>fit_params</th>\n",
+ " <th>model_type</th>\n",
+ " <th>model_size</th>\n",
+ " <th>metrics_elapsed_time</th>\n",
+ " <th>metrics_type</th>\n",
+ " <th>training_metrics_final</th>\n",
+ " <th>training_loss_final</th>\n",
+ " <th>training_metrics</th>\n",
+ " <th>training_loss</th>\n",
+ " <th>validation_metrics_final</th>\n",
+ " <th>validation_loss_final</th>\n",
+ " <th>validation_metrics</th>\n",
+ " <th>validation_loss</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>15</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='adam(lr=0.001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=64,epochs=5</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>2159.70019531</td>\n",
+ " <td>[160.21645283699, 325.693609952927, 493.035051822662, 661.953631877899, 831.346253871918, 1001.65144181252, 1171.72806191444, 1342.43474984169, 1515.81184601784, 1689.71926879883]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.958739995956</td>\n",
+ " <td>0.12002915144</td>\n",
+ " <td>[0.779980003833771, 0.845019996166229, 0.873939990997314, 0.893540024757385, 0.905359983444214, 0.92519998550415, 0.942740023136139, 0.936339974403381, 0.944379985332489, 0.958739995956421]</td>\n",
+ " <td>[0.63620263338089, 0.451914638280869, 0.365966022014618, 0.304257303476334, 0.272642701864243, 0.214935272932053, 0.167475894093513, 0.184087827801704, 0.162149116396904, 0.120029151439667]</td>\n",
+ " <td>0.838599979877</td>\n",
+ " <td>0.570000112057</td>\n",
+ " <td>[0.749100029468536, 0.795099973678589, 0.809499979019165, 0.814599990844727, 0.817300021648407, 0.825900018215179, 0.831399977207184, 0.829599976539612, 0.826099991798401, 0.838599979877472]</td>\n",
+ " <td>[0.729304790496826, 0.612815201282501, 0.598590016365051, 0.574969530105591, 0.585948467254639, 0.566278994083405, 0.566746890544891, 0.570696115493774, 0.600630104541779, 0.570000112056732]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>16</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='adam(lr=0.001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=128,epochs=5</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>2159.70019531</td>\n",
+ " <td>[168.006911993027, 334.143662929535, 501.23655295372, 670.235726833344, 840.096035003662, 1010.38422298431, 1180.51074194908, 1351.65223288536, 1524.53146886826, 1699.84652090073]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.959839999676</td>\n",
+ " <td>0.119847580791</td>\n",
+ " <td>[0.763499975204468, 0.829859972000122, 0.858560025691986, 0.889379978179932, 0.903039991855621, 0.922519981861115, 0.938279986381531, 0.941100001335144, 0.953220009803772, 0.959839999675751]</td>\n",
+ " <td>[0.676432132720947, 0.491187304258347, 0.408329516649246, 0.319939345121384, 0.279028236865997, 0.222155645489693, 0.179503843188286, 0.17168553173542, 0.136883869767189, 0.11984758079052]</td>\n",
+ " <td>0.833100020885</td>\n",
+ " <td>0.599731981754</td>\n",
+ " <td>[0.741900026798248, 0.784900009632111, 0.796500027179718, 0.814000010490417, 0.820100009441376, 0.828100025653839, 0.83160001039505, 0.831499993801117, 0.834399998188019, 0.833100020885468]</td>\n",
+ " <td>[0.74375057220459, 0.641318619251251, 0.627871870994568, 0.585915207862854, 0.588144600391388, 0.569212675094604, 0.577586710453033, 0.590799033641815, 0.581186473369598, 0.599731981754303]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>13</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='rmsprop(lr=0.001, decay=1e-6)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=64,epochs=5</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>2159.70019531</td>\n",
+ " <td>[165.07025885582, 331.04939699173, 498.211872816086, 667.060778856277, 836.708249807358, 1007.20118093491, 1176.76868081093, 1348.24542784691, 1520.97673892975, 1695.76891899109]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.94892001152</td>\n",
+ " <td>0.146594136953</td>\n",
+ " <td>[0.786400020122528, 0.839680016040802, 0.869140028953552, 0.878480017185211, 0.909940004348755, 0.913100004196167, 0.934099972248077, 0.94021999835968, 0.936819970607758, 0.948920011520386]</td>\n",
+ " <td>[0.625118017196655, 0.466760665178299, 0.38435173034668, 0.353523939847946, 0.260264813899994, 0.252679228782654, 0.190901413559914, 0.176555588841438, 0.181787580251694, 0.146594136953354]</td>\n",
+ " <td>0.82959997654</td>\n",
+ " <td>0.581429600716</td>\n",
+ " <td>[0.76120001077652, 0.787599980831146, 0.804199993610382, 0.805400013923645, 0.815400004386902, 0.824199974536896, 0.827499985694885, 0.826099991798401, 0.822399973869324, 0.829599976539612]</td>\n",
+ " <td>[0.711538255214691, 0.647833049297333, 0.601243674755096, 0.620489895343781, 0.593936264514923, 0.592362821102142, 0.572449862957001, 0.586679399013519, 0.630510628223419, 0.581429600715637]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>14</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='rmsprop(lr=0.001, decay=1e-6)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=128,epochs=5</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>2159.70019531</td>\n",
+ " <td>[161.95653796196, 327.381590843201, 494.75689291954, 663.697657823563, 833.140888929367, 1003.58734488487, 1173.66367697716, 1344.3388338089, 1517.75524687767, 1691.74780988693]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.942839980125</td>\n",
+ " <td>0.167295366526</td>\n",
+ " <td>[0.770560026168823, 0.822459995746613, 0.871060013771057, 0.886059999465942, 0.906120002269745, 0.91347998380661, 0.916339993476868, 0.929560005664825, 0.938979983329773, 0.942839980125427]</td>\n",
+ " <td>[0.656668424606323, 0.515599489212036, 0.367846250534058, 0.332457065582275, 0.276119023561478, 0.253687649965286, 0.2399021089077, 0.203119158744812, 0.174109742045403, 0.16729536652565]</td>\n",
+ " <td>0.827600002289</td>\n",
+ " <td>0.60536968708</td>\n",
+ " <td>[0.734799981117249, 0.784600019454956, 0.806299984455109, 0.811600029468536, 0.817499995231628, 0.823800027370453, 0.828299999237061, 0.829900026321411, 0.828999996185303, 0.827600002288818]</td>\n",
+ " <td>[0.777421414852142, 0.645794928073883, 0.587472915649414, 0.603748321533203, 0.590349853038788, 0.586721003055573, 0.58501935005188, 0.603322744369507, 0.596234917640686, 0.605369687080383]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>11</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='rmsprop(lr=0.0001, decay=1e-6)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=64,epochs=5</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>2159.70019531</td>\n",
+ " <td>[173.035629987717, 339.004810810089, 506.329674959183, 675.320897817612, 845.306622982025, 1015.91489100456, 1185.85607385635, 1357.71336293221, 1530.14687585831, 1705.61137890816]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.892719984055</td>\n",
+ " <td>0.309859842062</td>\n",
+ " <td>[0.583959996700287, 0.695259988307953, 0.754980027675629, 0.787720024585724, 0.814040005207062, 0.835359990596771, 0.856899976730347, 0.871460020542145, 0.884779989719391, 0.892719984054565]</td>\n",
+ " <td>[1.16789627075195, 0.861167967319489, 0.705251038074493, 0.607605278491974, 0.531997323036194, 0.470408618450165, 0.413226217031479, 0.370105147361755, 0.334705889225006, 0.309859842061996]</td>\n",
+ " <td>0.813000023365</td>\n",
+ " <td>0.566866695881</td>\n",
+ " <td>[0.579999983310699, 0.690999984741211, 0.738099992275238, 0.763599991798401, 0.773800015449524, 0.783800005912781, 0.798500001430511, 0.802699983119965, 0.80620002746582, 0.813000023365021]</td>\n",
+ " <td>[1.17161071300507, 0.881313383579254, 0.754627048969269, 0.680095791816711, 0.643820106983185, 0.620280385017395, 0.590712904930115, 0.576853334903717, 0.574894845485687, 0.56686669588089]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>9</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='adam(lr=0.0001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=64,epochs=5</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>2159.70019531</td>\n",
+ " <td>[156.6842648983, 322.059647798538, 489.514621019363, 658.153139829636, 827.505573987961, 997.966463804245, 1168.05154681206, 1338.58688092232, 1511.8177728653, 1685.66397881508]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.89484000206</td>\n",
+ " <td>0.305973917246</td>\n",
+ " <td>[0.607659995555878, 0.689279973506927, 0.748199999332428, 0.779420018196106, 0.812300026416779, 0.830940008163452, 0.854920029640198, 0.868260025978088, 0.885720014572144, 0.894840002059937]</td>\n",
+ " <td>[1.11698472499847, 0.874710261821747, 0.711394190788269, 0.626727402210236, 0.533221900463104, 0.478961884975433, 0.414784848690033, 0.376370459794998, 0.330575972795486, 0.305973917245865]</td>\n",
+ " <td>0.81099998951</td>\n",
+ " <td>0.550887346268</td>\n",
+ " <td>[0.602699995040894, 0.681299984455109, 0.728600025177002, 0.754400014877319, 0.774600028991699, 0.784300029277802, 0.795799970626831, 0.80129998922348, 0.807399988174438, 0.810999989509583]</td>\n",
+ " <td>[1.11438655853271, 0.900401651859283, 0.765824854373932, 0.704216003417969, 0.643769145011902, 0.616570711135864, 0.586219370365143, 0.571570515632629, 0.558478593826294, 0.5508873462677]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>10</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='adam(lr=0.0001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=128,epochs=5</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>2159.70019531</td>\n",
+ " <td>[158.506701946259, 323.779083013535, 491.302199840546, 660.148201942444, 829.340363025665, 999.844955921173, 1169.83032798767, 1340.5411260128, 1513.8498609066, 1687.69545793533]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.867579996586</td>\n",
+ " <td>0.380911707878</td>\n",
+ " <td>[0.576640009880066, 0.66431999206543, 0.707199990749359, 0.749520003795624, 0.778980016708374, 0.799520015716553, 0.820659995079041, 0.839940011501312, 0.854659974575043, 0.867579996585846]</td>\n",
+ " <td>[1.20035827159882, 0.945839285850525, 0.823047578334808, 0.703792750835419, 0.624295234680176, 0.566677749156952, 0.511338114738464, 0.459649682044983, 0.418204575777054, 0.380911707878113]</td>\n",
+ " <td>0.799700021744</td>\n",
+ " <td>0.571797966957</td>\n",
+ " <td>[0.572700023651123, 0.655099987983704, 0.69980001449585, 0.731299996376038, 0.756399989128113, 0.771399974822998, 0.778999984264374, 0.791599988937378, 0.798200011253357, 0.799700021743774]</td>\n",
+ " <td>[1.20565474033356, 0.964107036590576, 0.849484860897064, 0.752416431903839, 0.691979646682739, 0.65268349647522, 0.628291726112366, 0.599337160587311, 0.586054861545563, 0.571797966957092]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>12</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='rmsprop(lr=0.0001, decay=1e-6)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=128,epochs=5</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>2159.70019531</td>\n",
+ " <td>[170.873965024948, 337.211632966995, 504.536657810211, 673.510901927948, 843.392476797104, 1014.04761791229, 1183.94673490524, 1355.76256680489, 1528.15198993683, 1703.5478978157]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.856419980526</td>\n",
+ " <td>0.410014539957</td>\n",
+ " <td>[0.547559976577759, 0.647800028324127, 0.698180019855499, 0.746360003948212, 0.769439995288849, 0.794420003890991, 0.814459979534149, 0.829039990901947, 0.850260019302368, 0.85641998052597]</td>\n",
+ " <td>[1.27583348751068, 1.00206804275513, 0.853795170783997, 0.720450162887573, 0.650525093078613, 0.582838296890259, 0.527670443058014, 0.484861791133881, 0.428409487009048, 0.410014539957047]</td>\n",
+ " <td>0.798799991608</td>\n",
+ " <td>0.597697675228</td>\n",
+ " <td>[0.546700000762939, 0.638999998569489, 0.690900027751923, 0.731299996376038, 0.745500028133392, 0.763100028038025, 0.776300013065338, 0.78549998998642, 0.795000016689301, 0.798799991607666]</td>\n",
+ " <td>[1.27788388729095, 1.02294909954071, 0.881045579910278, 0.773496091365814, 0.725675582885742, 0.681352615356445, 0.649362862110138, 0.62593412399292, 0.598857045173645, 0.597697675228119]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>8</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='adam(lr=0.001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=128,epochs=5</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>4886.20019531</td>\n",
+ " <td>[165.667771816254, 331.63804101944, 498.80203294754, 667.662895917892, 837.58491396904, 1007.91998696327, 1177.68679785728, 1348.86184287071, 1521.96094989777, 1696.78608179092]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.909500002861</td>\n",
+ " <td>0.272008836269</td>\n",
+ " <td>[0.752040028572083, 0.828859984874725, 0.851639986038208, 0.888540029525757, 0.888599991798401, 0.894879996776581, 0.903320014476776, 0.910380005836487, 0.879000008106232, 0.909500002861023]</td>\n",
+ " <td>[0.712807893753052, 0.499261021614075, 0.437727391719818, 0.330645024776459, 0.325151771306992, 0.308288186788559, 0.296011507511139, 0.272213906049728, 0.394761264324188, 0.272008836269379]</td>\n",
+ " <td>0.779699981213</td>\n",
+ " <td>0.834259152412</td>\n",
+ " <td>[0.708500027656555, 0.751299977302551, 0.765500009059906, 0.772300004959106, 0.777100026607513, 0.783599972724915, 0.777999997138977, 0.784099996089935, 0.769800007343292, 0.779699981212616]</td>\n",
+ " <td>[0.841326177120209, 0.729629755020142, 0.776288628578186, 0.718003392219543, 0.76948493719101, 0.767067849636078, 0.827211439609528, 0.747310042381287, 0.895535230636597, 0.834259152412415]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='adam(lr=0.0001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=64,epochs=5</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>4886.20019531</td>\n",
+ " <td>[168.611300945282, 334.899528980255, 502.037551879883, 671.096584796906, 840.693498849869, 1011.29117488861, 1181.11248087883, 1352.68110394478, 1525.13323402405, 1700.88493180275]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.920239984989</td>\n",
+ " <td>0.272924244404</td>\n",
+ " <td>[0.584800004959106, 0.682219982147217, 0.732940018177032, 0.774779975414276, 0.806119978427887, 0.837840020656586, 0.862119972705841, 0.883340001106262, 0.90311998128891, 0.920239984989166]</td>\n",
+ " <td>[1.17424917221069, 0.919099688529968, 0.776308834552765, 0.661072790622711, 0.573073744773865, 0.487901866436005, 0.420156627893448, 0.368478238582611, 0.319370418787003, 0.272924244403839]</td>\n",
+ " <td>0.775600016117</td>\n",
+ " <td>0.679777920246</td>\n",
+ " <td>[0.576799988746643, 0.657599985599518, 0.694700002670288, 0.721199989318848, 0.741400003433228, 0.750500023365021, 0.758800029754639, 0.764599978923798, 0.771899998188019, 0.775600016117096]</td>\n",
+ " <td>[1.19727396965027, 0.98458856344223, 0.873661160469055, 0.809533834457397, 0.764622151851654, 0.735906422138214, 0.700538396835327, 0.705035388469696, 0.696578562259674, 0.679777920246124]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>7</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='adam(lr=0.001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=64,epochs=5</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>4886.20019531</td>\n",
+ " <td>[154.371929883957, 319.090498924255, 486.952117919922, 655.78808093071, 824.799381971359, 995.210795879364, 1165.24253582954, 1336.07785487175, 1508.86772489548, 1682.56205582619]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.861419975758</td>\n",
+ " <td>0.432331353426</td>\n",
+ " <td>[0.766240000724792, 0.827859997749329, 0.855539977550507, 0.868900001049042, 0.871540009975433, 0.88238000869751, 0.873179972171783, 0.878740012645721, 0.873459994792938, 0.861419975757599]</td>\n",
+ " <td>[0.682850182056427, 0.503075182437897, 0.426931649446487, 0.385531783103943, 0.383735597133636, 0.371502071619034, 0.389450550079346, 0.369848489761353, 0.391900181770325, 0.43233135342598]</td>\n",
+ " <td>0.767599999905</td>\n",
+ " <td>0.752326309681</td>\n",
+ " <td>[0.719299972057343, 0.751500010490417, 0.765399992465973, 0.769999980926514, 0.772499978542328, 0.776799976825714, 0.770099997520447, 0.77649998664856, 0.76800000667572, 0.767599999904633]</td>\n",
+ " <td>[0.819014072418213, 0.739358127117157, 0.74931389093399, 0.773028433322906, 0.763317465782166, 0.729169189929962, 0.805014729499817, 0.808122754096985, 0.812756359577179, 0.752326309680939]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='rmsprop(lr=0.0001, decay=1e-6)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=64,epochs=5</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>4886.20019531</td>\n",
+ " <td>[154.969959974289, 319.906549930573, 487.758535861969, 656.377619981766, 825.622062921524, 996.102727890015, 1166.17477893829, 1336.67210102081, 1509.83961796761, 1683.64339399338]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.821099996567</td>\n",
+ " <td>0.525239348412</td>\n",
+ " <td>[0.561699986457825, 0.668940007686615, 0.717679977416992, 0.75297999382019, 0.767099976539612, 0.795260012149811, 0.807739973068237, 0.811280012130737, 0.826099991798401, 0.821099996566772]</td>\n",
+ " <td>[1.220210313797, 0.947799980640411, 0.812605798244476, 0.718978762626648, 0.679905712604523, 0.613099038600922, 0.566433131694794, 0.552485108375549, 0.512454450130463, 0.52523934841156]</td>\n",
+ " <td>0.758599996567</td>\n",
+ " <td>0.737899065018</td>\n",
+ " <td>[0.559899985790253, 0.647000014781952, 0.683300018310547, 0.709100008010864, 0.723800003528595, 0.742299973964691, 0.749700009822845, 0.757099986076355, 0.765600025653839, 0.758599996566772]</td>\n",
+ " <td>[1.22284317016602, 0.988233506679535, 0.88149094581604, 0.825053095817566, 0.811959385871887, 0.752561450004578, 0.741220593452454, 0.739940404891968, 0.711078464984894, 0.7378990650177]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='adam(lr=0.0001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=128,epochs=5</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>4886.20019531</td>\n",
+ " <td>[169.201556921005, 335.50149679184, 502.804733991623, 671.735637903214, 841.536010980606, 1012.17449593544, 1182.0387070179, 1353.62765884399, 1526.14807486534, 1701.52705287933]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.866079986095</td>\n",
+ " <td>0.411734908819</td>\n",
+ " <td>[0.535120010375977, 0.633019983768463, 0.683080017566681, 0.727400004863739, 0.757719993591309, 0.788060009479523, 0.812640011310577, 0.835120022296906, 0.846180021762848, 0.866079986095428]</td>\n",
+ " <td>[1.29918956756592, 1.05417799949646, 0.917978286743164, 0.795661866664886, 0.70832484960556, 0.632884621620178, 0.562899112701416, 0.502775311470032, 0.463661164045334, 0.411734908819199]</td>\n",
+ " <td>0.758199989796</td>\n",
+ " <td>0.725014865398</td>\n",
+ " <td>[0.527800023555756, 0.620299994945526, 0.656300008296967, 0.690400004386902, 0.707400023937225, 0.726199984550476, 0.734799981117249, 0.746500015258789, 0.750999987125397, 0.758199989795685]</td>\n",
+ " <td>[1.31133198738098, 1.09069919586182, 0.985389471054077, 0.897808969020844, 0.84535801410675, 0.804515540599823, 0.770778298377991, 0.748202204704285, 0.736022055149078, 0.725014865398407]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>4</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='rmsprop(lr=0.0001, decay=1e-6)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=128,epochs=5</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>4886.20019531</td>\n",
+ " <td>[163.359121799469, 328.837852954865, 496.364542961121, 665.302300930023, 834.891145944595, 1005.38074088097, 1174.84563589096, 1345.91664791107, 1518.93280696869, 1693.73289394379]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.82415997982</td>\n",
+ " <td>0.518441617489</td>\n",
+ " <td>[0.521520018577576, 0.612500011920929, 0.638840019702911, 0.686819970607758, 0.740119993686676, 0.765739977359772, 0.78847998380661, 0.814140021800995, 0.825020015239716, 0.824159979820251]</td>\n",
+ " <td>[1.33973300457001, 1.10515522956848, 1.02763831615448, 0.890439391136169, 0.749050080776215, 0.679889440536499, 0.620155572891235, 0.560859560966492, 0.520996809005737, 0.518441617488861]</td>\n",
+ " <td>0.757799983025</td>\n",
+ " <td>0.732741773129</td>\n",
+ " <td>[0.515500009059906, 0.602699995040894, 0.630599975585938, 0.66619998216629, 0.70959997177124, 0.721300005912781, 0.73470002412796, 0.751500010490417, 0.756099998950958, 0.757799983024597]</td>\n",
+ " <td>[1.34494018554688, 1.12949633598328, 1.05980122089386, 0.951755106449127, 0.840038895606995, 0.81215900182724, 0.776918768882751, 0.734996318817139, 0.712943911552429, 0.73274177312851]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>6</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='rmsprop(lr=0.001, decay=1e-6)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=128,epochs=5</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>4886.20019531</td>\n",
+ " <td>[162.552975893021, 328.213756799698, 495.55163693428, 664.478772878647, 834.023772001266, 1004.50640487671, 1174.26192784309, 1344.94176697731, 1518.35087895393, 1692.74292802811]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.734799981117</td>\n",
+ " <td>0.835899949074</td>\n",
+ " <td>[0.71916002035141, 0.750440001487732, 0.720459997653961, 0.801540017127991, 0.725839972496033, 0.777220010757446, 0.780219972133636, 0.706719994544983, 0.750180006027222, 0.734799981117249]</td>\n",
+ " <td>[0.821036815643311, 0.778892278671265, 0.90295821428299, 0.621506929397583, 0.841251850128174, 0.677752196788788, 0.701953232288361, 0.95904815196991, 0.82386702299118, 0.835899949073792]</td>\n",
+ " <td>0.709599971771</td>\n",
+ " <td>0.930533230305</td>\n",
+ " <td>[0.680499970912933, 0.7185999751091, 0.670499980449677, 0.75220000743866, 0.685699999332428, 0.733200013637543, 0.733200013637543, 0.669700026512146, 0.712100028991699, 0.70959997177124]</td>\n",
+ " <td>[0.936905562877655, 0.941630959510803, 1.1544371843338, 0.890039265155792, 1.02143895626068, 0.915954768657684, 0.975173354148865, 1.12838399410248, 0.942200124263763, 0.930533230304718]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>5</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='rmsprop(lr=0.001, decay=1e-6)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=64,epochs=5</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>4886.20019531</td>\n",
+ " <td>[166.255312919617, 332.407908916473, 499.474656820297, 668.450613975525, 838.271963834763, 1008.50526785851, 1178.62056994438, 1349.74808692932, 1522.54409790039, 1697.81855082512]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.585380017757</td>\n",
+ " <td>1.2495957613</td>\n",
+ " <td>[0.71341997385025, 0.755879998207092, 0.7189000248909, 0.719219982624054, 0.683700025081635, 0.646440029144287, 0.565659999847412, 0.623939990997314, 0.66210001707077, 0.585380017757416]</td>\n",
+ " <td>[0.875595450401306, 0.737006664276123, 0.879582762718201, 0.826622664928436, 0.986110627651215, 1.02004647254944, 1.33022010326385, 1.12789785861969, 0.977221727371216, 1.24959576129913]</td>\n",
+ " <td>0.591700017452</td>\n",
+ " <td>1.32029783726</td>\n",
+ " <td>[0.690599977970123, 0.721400022506714, 0.707000017166138, 0.700900018215179, 0.673799991607666, 0.638000011444092, 0.565599977970123, 0.620199978351593, 0.65719997882843, 0.59170001745224]</td>\n",
+ " <td>[0.990004479885101, 0.883562862873077, 0.931297481060028, 0.941763758659363, 1.02153491973877, 1.06588494777679, 1.32568216323853, 1.17570757865906, 1.0156409740448, 1.32029783725739]</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(15, 2, u\"loss='categorical_crossentropy',optimizer='adam(lr=0.001)',metrics=['accuracy']\", u'batch_size=64,epochs=5', u'madlib_keras', 2159.70019531, [160.21645283699, 325.693609952927, 493.035051822662, 661.953631877899, 831.346253871918, 1001.65144181252, 1171.72806191444, 1342.43474984169, 1515.81184601784, 1689.71926879883], [u'accuracy'], 0.958739995956, 0.12002915144, [0.779980003833771, 0.845019996166229, 0.873939990997314, 0.893540024757385, 0.905359983444214, 0.92519998550415, 0.942740023136139, 0.936339974403381, 0.944379985332489, 0.958739995956421], [0.63620263338089, 0.451914638280869, 0.365966022014618, 0.304257303476334, 0.272642701864243, 0.214935272932053, 0.167475894093513, 0.184087827801704, 0.162149116396904, 0.120029151439667], 0.838599979877, 0.570000112057, [0.749100029468536, 0.795099973678589, 0.809499979019165, 0.814599990844727, 0.817300021648407, 0.825900018215179, 0.831399977207184, 0.829599976539612, 0.826099991798401, 0.838599979877472], [0.729304790496826, 0.612815201282501, 0.598590016365051, 0.574969530105591, 0.585948467254639, 0.566278994083405, 0.566746890544891, 0.570696115493774, 0.600630104541779, 0.570000112056732]),\n",
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+ " (7, 1, u\"loss='categorical_crossentropy',optimizer='adam(lr=0.001)',metrics=['accuracy']\", u'batch_size=64,epochs=5', u'madlib_keras', 4886.20019531, [154.371929883957, 319.090498924255, 486.952117919922, 655.78808093071, 824.799381971359, 995.210795879364, 1165.24253582954, 1336.07785487175, 1508.86772489548, 1682.56205582619], [u'accuracy'], 0.861419975758, 0.432331353426, [0.766240000724792, 0.827859997749329, 0.855539977550507, 0.868900001049042, 0.871540009975433, 0.88238000869751, 0.873179972171783, 0.878740012645721, 0.873459994792938, 0.861419975757599], [0.682850182056427, 0.503075182437897, 0.426931649446487, 0.385531783103943, 0.383735597133636, 0.371502071619034, 0.389450550079346, 0.369848489761353, 0.391900181770325, 0.43233135342598], 0.767599999905, 0.752326309681, [0.719299972057343, 0.751500010490417, 0.765399992465973, 0.769999980926514, 0.772499978542328, 0.776799976825714, 0.770099997520447, 0.77649998664856, 0.76800000667572, 0.767599999904633], [0.819014072418213, 0.739358127117157, 0.74931389093399, 0.773028433322906, 0.763317465782166, 0.729169189929962, 0.805014729499817, 0.808122754096985, 0.812756359577179, 0.752326309680939]),\n",
+ " (3, 1, u\"loss='categorical_crossentropy',optimizer='rmsprop(lr=0.0001, decay=1e-6)',metrics=['accuracy']\", u'batch_size=64,epochs=5', u'madlib_keras', 4886.20019531, [154.969959974289, 319.906549930573, 487.758535861969, 656.377619981766, 825.622062921524, 996.102727890015, 1166.17477893829, 1336.67210102081, 1509.83961796761, 1683.64339399338], [u'accuracy'], 0.821099996567, 0.525239348412, [0.561699986457825, 0.668940007686615, 0.717679977416992, 0.75297999382019, 0.767099976539612, 0.795260012149811, 0.807739973068237, 0.811280012130737, 0.826099991798401, 0.821099996566772], [1.220210313797, 0.947799980640411, 0.812605798244476, 0.718978762626648, 0.679905712604523, 0.613099038600922, 0.566433131694794, 0.552485108375549, 0.512454450130463, 0.52523934841156], 0.758599996567, 0.737899065018, [0.559899985790253, 0.647000014781952, 0.683300018310547, 0.709100008010864, 0.723800003528595, 0.742299973964691, 0.749700009822845, 0.757099986076355, 0.765600025653839, 0.758599996566772], [1.22284317016602, 0.988233506679535, 0.88149094581604, 0.825053095817566, 0.811959385871887, 0.752561450004578, 0.741220593452454, 0.739940404891968, 0.711078464984894, 0.7378990650177]),\n",
+ " (2, 1, u\"loss='categorical_crossentropy',optimizer='adam(lr=0.0001)',metrics=['accuracy']\", u'batch_size=128,epochs=5', u'madlib_keras', 4886.20019531, [169.201556921005, 335.50149679184, 502.804733991623, 671.735637903214, 841.536010980606, 1012.17449593544, 1182.0387070179, 1353.62765884399, 1526.14807486534, 1701.52705287933], [u'accuracy'], 0.866079986095, 0.411734908819, [0.535120010375977, 0.633019983768463, 0.683080017566681, 0.727400004863739, 0.757719993591309, 0.788060009479523, 0.812640011310577, 0.835120022296906, 0.846180021762848, 0.866079986095428], [1.29918956756592, 1.05417799949646, 0.917978286743164, 0.795661866664886, 0.70832484960556, 0.632884621620178, 0.562899112701416, 0.502775311470032, 0.463661164045334, 0.411734908819199], 0.758199989796, 0.725014865398, [0.527800023555756, 0.620299994945526, 0.656300008296967, 0.690400004386902, 0.707400023937225, 0.726199984550476, 0.734799981117249, 0.746500015258789, 0.750999987125397, 0.758199989795685], [1.31133198738098, 1.09069919586182, 0.985389471054077, 0.897808969020844, 0.84535801410675, 0.804515540599823, 0.770778298377991, 0.748202204704285, 0.736022055149078, 0.725014865398407]),\n",
+ " (4, 1, u\"loss='categorical_crossentropy',optimizer='rmsprop(lr=0.0001, decay=1e-6)',metrics=['accuracy']\", u'batch_size=128,epochs=5', u'madlib_keras', 4886.20019531, [163.359121799469, 328.837852954865, 496.364542961121, 665.302300930023, 834.891145944595, 1005.38074088097, 1174.84563589096, 1345.91664791107, 1518.93280696869, 1693.73289394379], [u'accuracy'], 0.82415997982, 0.518441617489, [0.521520018577576, 0.612500011920929, 0.638840019702911, 0.686819970607758, 0.740119993686676, 0.765739977359772, 0.78847998380661, 0.814140021800995, 0.825020015239716, 0.824159979820251], [1.33973300457001, 1.10515522956848, 1.02763831615448, 0.890439391136169, 0.749050080776215, 0.679889440536499, 0.620155572891235, 0.560859560966492, 0.520996809005737, 0.518441617488861], 0.757799983025, 0.732741773129, [0.515500009059906, 0.602699995040894, 0.630599975585938, 0.66619998216629, 0.70959997177124, 0.721300005912781, 0.73470002412796, 0.751500010490417, 0.756099998950958, 0.757799983024597], [1.34494018554688, 1.12949633598328, 1.05980122089386, 0.951755106449127, 0.840038895606995, 0.81215900182724, 0.776918768882751, 0.734996318817139, 0.712943911552429, 0.73274177312851]),\n",
+ " (6, 1, u\"loss='categorical_crossentropy',optimizer='rmsprop(lr=0.001, decay=1e-6)',metrics=['accuracy']\", u'batch_size=128,epochs=5', u'madlib_keras', 4886.20019531, [162.552975893021, 328.213756799698, 495.55163693428, 664.478772878647, 834.023772001266, 1004.50640487671, 1174.26192784309, 1344.94176697731, 1518.35087895393, 1692.74292802811], [u'accuracy'], 0.734799981117, 0.835899949074, [0.71916002035141, 0.750440001487732, 0.720459997653961, 0.801540017127991, 0.725839972496033, 0.777220010757446, 0.780219972133636, 0.706719994544983, 0.750180006027222, 0.734799981117249], [0.821036815643311, 0.778892278671265, 0.90295821428299, 0.621506929397583, 0.841251850128174, 0.677752196788788, 0.701953232288361, 0.95904815196991, 0.82386702299118, 0.835899949073792], 0.709599971771, 0.930533230305, [0.680499970912933, 0.7185999751091, 0.670499980449677, 0.75220000743866, 0.685699999332428, 0.733200013637543, 0.733200013637543, 0.669700026512146, 0.712100028991699, 0.70959997177124], [0.936905562877655, 0.941630959510803, 1.1544371843338, 0.890039265155792, 1.02143895626068, 0.915954768657684, 0.975173354148865, 1.12838399410248, 0.942200124263763, 0.930533230304718]),\n",
+ " (5, 1, u\"loss='categorical_crossentropy',optimizer='rmsprop(lr=0.001, decay=1e-6)',metrics=['accuracy']\", u'batch_size=64,epochs=5', u'madlib_keras', 4886.20019531, [166.255312919617, 332.407908916473, 499.474656820297, 668.450613975525, 838.271963834763, 1008.50526785851, 1178.62056994438, 1349.74808692932, 1522.54409790039, 1697.81855082512], [u'accuracy'], 0.585380017757, 1.2495957613, [0.71341997385025, 0.755879998207092, 0.7189000248909, 0.719219982624054, 0.683700025081635, 0.646440029144287, 0.565659999847412, 0.623939990997314, 0.66210001707077, 0.585380017757416], [0.875595450401306, 0.737006664276123, 0.879582762718201, 0.826622664928436, 0.986110627651215, 1.02004647254944, 1.33022010326385, 1.12789785861969, 0.977221727371216, 1.24959576129913], 0.591700017452, 1.32029783726, [0.690599977970123, 0.721400022506714, 0.707000017166138, 0.700900018215179, 0.673799991607666, 0.638000011444092, 0.565599977970123, 0.620199978351593, 0.65719997882843, 0.59170001745224], [0.990004479885101, 0.883562862873077, 0.931297481060028, 0.941763758659363, 1.02153491973877, 1.06588494777679, 1.32568216323853, 1.17570757865906, 1.0156409740448, 1.32029783725739])]"
+ ]
+ },
+ "execution_count": 37,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT * FROM cifar10_multi_model_info ORDER BY validation_metrics_final DESC;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"plot\"></a>\n",
+ "# 6. Plot results"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib notebook\n",
+ "import matplotlib.pyplot as plt\n",
+ "from matplotlib.ticker import MaxNLocator\n",
+ "from collections import defaultdict\n",
+ "import pandas as pd\n",
+ "plt.rcParams.update({'font.size': 12})\n",
+ "pd.set_option('display.max_colwidth', -1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Training data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "16 rows affected.\n",
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
+ " if (typeof(WebSocket) !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support.' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
+ "\n",
+ " this.ws = websocket;\n",
+ "\n",
+ " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ "\n",
+ " if (!this.supports_binary) {\n",
+ " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
+ "\n",
+ " this.context = undefined;\n",
+ " this.message = undefined;\n",
+ " this.canvas = undefined;\n",
+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
+ " if (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+ " }\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " }\n",
+ "\n",
+ " this.imageObj.onload = function() {\n",
+ " if (fig.image_mode == 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onunload = function() {\n",
+ " fig.ws.close();\n",
+ " }\n",
+ "\n",
+ " this.ws.onmessage = this._make_on_message_function(this);\n",
+ "\n",
+ " this.ondownload = ondownload;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option)\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width, fig.canvas.height);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var width = this.canvas.width/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " // select the cell after this one\n",
+ " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
+ " IPython.notebook.select(index + 1);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i<ncells; i++) {\n",
+ " var cell = cells[i];\n",
+ " if (cell.cell_type === 'code'){\n",
+ " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " var data = cell.output_area.outputs[j];\n",
+ " if (data.data) {\n",
+ " // IPython >= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.legend.Legend at 0x154945910>"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5,21,'Iteration')"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5,1,'Training Accuracy')"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5,21,'Iteration')"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5,1,'Training Loss (Cross Entropy)')"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x156a53950>]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "16 rows affected.\n",
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x156a331d0>]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "16 rows affected.\n",
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x156a5f250>]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "16 rows affected.\n",
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x156a45e90>]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "16 rows affected.\n",
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x156a5f310>]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "16 rows affected.\n",
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x156a82290>]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "16 rows affected.\n",
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x156a827d0>]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "16 rows affected.\n",
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x156a82d10>]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "16 rows affected.\n",
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x156a82790>]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "16 rows affected.\n",
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x156a45dd0>]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "16 rows affected.\n",
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x156a97610>]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "16 rows affected.\n",
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x156a97b50>]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "16 rows affected.\n",
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x156aa40d0>]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "16 rows affected.\n",
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x156a97b10>]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "16 rows affected.\n",
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x156aa4110>]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "16 rows affected.\n",
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x156aa4990>]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "16 rows affected.\n",
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
+ " if (typeof(WebSocket) !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support.' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
+ "\n",
+ " this.ws = websocket;\n",
+ "\n",
+ " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ "\n",
+ " if (!this.supports_binary) {\n",
+ " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
+ "\n",
+ " this.context = undefined;\n",
+ " this.message = undefined;\n",
+ " this.canvas = undefined;\n",
+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
+ " if (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+ " }\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " }\n",
+ "\n",
+ " this.imageObj.onload = function() {\n",
+ " if (fig.image_mode == 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onunload = function() {\n",
+ " fig.ws.close();\n",
+ " }\n",
+ "\n",
+ " this.ws.onmessage = this._make_on_message_function(this);\n",
+ "\n",
+ " this.ondownload = ondownload;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option)\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width, fig.canvas.height);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var width = this.canvas.width/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " // select the cell after this one\n",
+ " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
+ " IPython.notebook.select(index + 1);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i<ncells; i++) {\n",
+ " var cell = cells[i];\n",
+ " if (cell.cell_type === 'code'){\n",
+ " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " var data = cell.output_area.outputs[j];\n",
+ " if (data.data) {\n",
+ " // IPython >= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
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+ ]
+ },
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+ "output_type": "display_data"
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+ "[<matplotlib.lines.Line2D at 0x156b7a350>]"
+ ]
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+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
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+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
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+ "data": {
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+ "[<matplotlib.lines.Line2D at 0x156b3f090>]"
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+ "execution_count": 8,
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+ "data": {
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+ "[<matplotlib.lines.Line2D at 0x156b7a690>]"
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+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
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+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
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+ "data": {
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+ "data": {
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+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
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+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
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+ "data": {
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+ "[<matplotlib.lines.Line2D at 0x156b868d0>]"
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+ "data": {
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+ "[<matplotlib.lines.Line2D at 0x156b86990>]"
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+ "metadata": {},
+ "output_type": "execute_result"
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+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
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+ "[<matplotlib.lines.Line2D at 0x156b90190>]"
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+ "execution_count": 8,
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+ "output_type": "execute_result"
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+ "data": {
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+ "[<matplotlib.lines.Line2D at 0x156b90250>]"
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+ "output_type": "execute_result"
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+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x156b7af90>]"
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+ "execution_count": 8,
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+ "data": {
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+ "[<matplotlib.lines.Line2D at 0x156b3f310>]"
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+ "metadata": {},
+ "output_type": "execute_result"
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+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
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+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x156b90a90>]"
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+ "[<matplotlib.lines.Line2D at 0x156b90f50>]"
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+ "metadata": {},
+ "output_type": "execute_result"
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+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
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+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x156b9d610>]"
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+ "output_type": "execute_result"
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+ "[<matplotlib.lines.Line2D at 0x156b9d890>]"
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+ "metadata": {},
+ "output_type": "execute_result"
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+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
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+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x156bac090>]"
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+ "data": {
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+ "[<matplotlib.lines.Line2D at 0x156bac150>]"
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+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
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+ "[<matplotlib.lines.Line2D at 0x156b9d5d0>]"
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+ "output_type": "execute_result"
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+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x156b90ed0>]"
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+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
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+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x156baca50>]"
+ ]
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+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x156bacb50>]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.legend.Legend at 0x156abfc90>"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_results = %sql SELECT * FROM cifar10_multi_model_info ORDER BY training_loss_final ASC LIMIT 100;\n",
+ "df_results = df_results.DataFrame()\n",
+ "\n",
+ "df_summary = %sql SELECT * FROM cifar10_multi_model_summary;\n",
+ "df_summary = df_summary.DataFrame()\n",
+ "\n",
+ "#set up plots\n",
+ "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(10,5))\n",
+ "fig.legend(ncol=4)\n",
+ "fig.tight_layout()\n",
+ "\n",
+ "ax_metric = axs[0]\n",
+ "ax_loss = axs[1]\n",
+ "\n",
+ "ax_metric.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
+ "ax_metric.set_xlabel('Iteration')\n",
+ "#ax_metric.set_ylabel('Accuracy')\n",
+ "ax_metric.set_title('Training Accuracy')\n",
+ "\n",
+ "ax_loss.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
+ "ax_loss.set_xlabel('Iteration')\n",
+ "#ax_loss.set_ylabel('Cross Entropy Loss')\n",
+ "ax_loss.set_title('Training Loss (Cross Entropy)')\n",
+ "\n",
+ "iters = df_summary['metrics_iters'][0]\n",
+ "\n",
+ "for mst_key in df_results['mst_key']:\n",
+ " df_output_info = %sql SELECT training_metrics,training_loss FROM cifar10_multi_model_info WHERE mst_key = $mst_key\n",
+ " df_output_info = df_output_info.DataFrame()\n",
+ " training_metrics = df_output_info['training_metrics'][0]\n",
+ " training_loss = df_output_info['training_loss'][0]\n",
+ " \n",
+ " ax_metric.plot(iters, training_metrics, label=mst_key, marker='o')\n",
+ " df_results = %sql SELECT * FROM cifar10_multi_model_info ORDER BY training_loss_final ASC LIMIT 100;\n",
+ "df_results = df_results.DataFrame()\n",
+ "\n",
+ "df_summary = %sql SELECT * FROM cifar10_multi_model_summary;\n",
+ "df_summary = df_summary.DataFrame()\n",
+ "\n",
+ "#set up plots\n",
+ "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(10,5))\n",
+ "fig.legend(ncol=4)\n",
+ "fig.tight_layout()\n",
+ "\n",
+ "ax_metric = axs[0]\n",
+ "ax_loss = axs[1]\n",
+ "\n",
+ "ax_metric.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
+ "ax_metric.set_xlabel('Iteration')\n",
+ "#ax_metric.set_ylabel('Accuracy')\n",
+ "ax_metric.set_title('Training Accuracy')\n",
+ "\n",
+ "ax_loss.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
+ "ax_loss.set_xlabel('Iteration')\n",
+ "#ax_loss.set_ylabel('Cross Entropy Loss')\n",
+ "ax_loss.set_title('Training Loss (Cross Entropy)')\n",
+ "\n",
+ "iters = df_summary['metrics_iters'][0]\n",
+ "\n",
+ "for mst_key in df_results['mst_key']:\n",
+ " df_output_info = %sql SELECT training_metrics,training_loss FROM cifar10_multi_model_info WHERE mst_key = $mst_key\n",
+ " df_output_info = df_output_info.DataFrame()\n",
+ " training_metrics = df_output_info['training_metrics'][0]\n",
+ " training_loss = df_output_info['training_loss'][0]\n",
+ " \n",
+ " #ax_metric.plot(iters, training_metrics, label=mst_key, marker='o')\n",
+ " ax_metric.plot(iters, training_metrics)\n",
+ " \n",
+ " #ax_loss.plot(iters, training_loss, label=mst_key, marker='o')\n",
+ " ax_loss.plot(iters, training_loss)\n",
+ "\n",
+ "plt.legend()\n",
+ "# fig.savefig('./lc_keras_fit.png', dpi = 300)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Validation data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "16 rows affected.\n",
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
+ " if (typeof(WebSocket) !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support.' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
+ "\n",
+ " this.ws = websocket;\n",
+ "\n",
+ " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ "\n",
+ " if (!this.supports_binary) {\n",
+ " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
+ "\n",
+ " this.context = undefined;\n",
+ " this.message = undefined;\n",
+ " this.canvas = undefined;\n",
+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
+ " if (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+ " }\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " }\n",
+ "\n",
+ " this.imageObj.onload = function() {\n",
+ " if (fig.image_mode == 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onunload = function() {\n",
+ " fig.ws.close();\n",
+ " }\n",
+ "\n",
+ " this.ws.onmessage = this._make_on_message_function(this);\n",
+ "\n",
+ " this.ondownload = ondownload;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option)\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width, fig.canvas.height);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var width = this.canvas.width/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " // select the cell after this one\n",
+ " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
+ " IPython.notebook.select(index + 1);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i<ncells; i++) {\n",
+ " var cell = cells[i];\n",
+ " if (cell.cell_type === 'code'){\n",
+ " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " var data = cell.output_area.outputs[j];\n",
+ " if (data.data) {\n",
+ " // IPython >= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
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\" width=\"720\">"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.legend.Legend at 0x156c14f10>"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5,21,'Iteration')"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5,1,'Validation Accuracy')"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5,21,'Iteration')"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5,1,'Validation Loss (Cross Entropy)')"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
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+ "1 rows affected.\n"
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+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
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+ "[<matplotlib.lines.Line2D at 0x156d24050>]"
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+ "output_type": "execute_result"
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+ "output_type": "stream",
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+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
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+ },
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+ "[<matplotlib.lines.Line2D at 0x156d247d0>]"
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+ },
+ {
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+ "[<matplotlib.lines.Line2D at 0x156d248d0>]"
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+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
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+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
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+ "output_type": "execute_result"
+ },
+ {
+ "data": {
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+ "[<matplotlib.lines.Line2D at 0x156d33250>]"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x156d33a50>]"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x156d33bd0>]"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.legend.Legend at 0x156c72690>"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_results = %sql SELECT * FROM cifar10_multi_model_info ORDER BY validation_loss_final ASC LIMIT 100;\n",
+ "df_results = df_results.DataFrame()\n",
+ "\n",
+ "df_summary = %sql SELECT * FROM cifar10_multi_model_summary;\n",
+ "df_summary = df_summary.DataFrame()\n",
+ "\n",
+ "#set up plots\n",
+ "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(10,5))\n",
+ "fig.legend(ncol=4)\n",
+ "fig.tight_layout()\n",
+ "\n",
+ "ax_metric = axs[0]\n",
+ "ax_loss = axs[1]\n",
+ "\n",
+ "ax_metric.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
+ "ax_metric.set_xlabel('Iteration')\n",
+ "#ax_metric.set_ylabel('Accuracy')\n",
+ "ax_metric.set_title('Validation Accuracy')\n",
+ "\n",
+ "ax_loss.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
+ "ax_loss.set_xlabel('Iteration')\n",
+ "#ax_loss.set_ylabel('Cross Entropy Loss')\n",
+ "ax_loss.set_title('Validation Loss (Cross Entropy)')\n",
+ "\n",
+ "iters = df_summary['metrics_iters'][0]\n",
+ "\n",
+ "for mst_key in df_results['mst_key']:\n",
+ " df_output_info = %sql SELECT validation_metrics,validation_loss FROM cifar10_multi_model_info WHERE mst_key = $mst_key\n",
+ " df_output_info = df_output_info.DataFrame()\n",
+ " validation_metrics = df_output_info['validation_metrics'][0]\n",
+ " validation_loss = df_output_info['validation_loss'][0]\n",
+ " \n",
+ " #ax_metric.plot(iters, validation_metrics, label=mst_key, marker='o')\n",
+ " ax_metric.plot(iters, validation_metrics)\n",
+ " \n",
+ " #ax_loss.plot(iters, validation_loss, label=mst_key, marker='o')\n",
+ " ax_loss.plot(iters, validation_loss)\n",
+ "\n",
+ "plt.legend()\n",
+ "# fig.savefig('./lc_keras_fit.png', dpi = 300)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Plot training and validation curves together"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n",
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
+ " if (typeof(WebSocket) !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support.' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
+ "\n",
+ " this.ws = websocket;\n",
+ "\n",
+ " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ "\n",
+ " if (!this.supports_binary) {\n",
+ " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
+ "\n",
+ " this.context = undefined;\n",
+ " this.message = undefined;\n",
+ " this.canvas = undefined;\n",
+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
+ " if (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+ " }\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " }\n",
+ "\n",
+ " this.imageObj.onload = function() {\n",
+ " if (fig.image_mode == 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onunload = function() {\n",
+ " fig.ws.close();\n",
+ " }\n",
+ "\n",
+ " this.ws.onmessage = this._make_on_message_function(this);\n",
+ "\n",
+ " this.ondownload = ondownload;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option)\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width, fig.canvas.height);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var width = this.canvas.width/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " // select the cell after this one\n",
+ " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
+ " IPython.notebook.select(index + 1);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i<ncells; i++) {\n",
+ " var cell = cells[i];\n",
+ " if (cell.cell_type === 'code'){\n",
+ " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " var data = cell.output_area.outputs[j];\n",
+ " if (data.data) {\n",
+ " // IPython >= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
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\" width=\"720\">"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.legend.Legend at 0x1570e6a50>"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5,21,'Iteration')"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5,1,'Classification Accuracy')"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5,21,'Iteration')"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5,1,'Cross Entropy Loss')"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n",
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x157191790>]"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x157191c10>]"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x1571912d0>]"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x157184890>]"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.legend.Legend at 0x15719c810>"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# mst tuple(s) to plot\n",
+ "mst_key_to_plot = 10\n",
+ "df_results = %sql SELECT * FROM cifar10_multi_model_info WHERE mst_key = $mst_key_to_plot;\n",
+ "df_results = df_results.DataFrame()\n",
+ "\n",
+ "df_summary = %sql SELECT * FROM cifar10_multi_model_summary;\n",
+ "df_summary = df_summary.DataFrame()\n",
+ "\n",
+ "#set up plots\n",
+ "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(10,5))\n",
+ "fig.legend(ncol=4)\n",
+ "fig.tight_layout()\n",
+ "\n",
+ "ax_metric = axs[0]\n",
+ "ax_loss = axs[1]\n",
+ "\n",
+ "ax_metric.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
+ "ax_metric.set_xlabel('Iteration')\n",
+ "#ax_metric.set_ylabel('Accuracy')\n",
+ "ax_metric.set_title('Classification Accuracy')\n",
+ "\n",
+ "ax_loss.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
+ "ax_loss.set_xlabel('Iteration')\n",
+ "#ax_loss.set_ylabel('Loss')\n",
+ "ax_loss.set_title('Cross Entropy Loss')\n",
+ "\n",
+ "iters = df_summary['metrics_iters'][0]\n",
+ "\n",
+ "for mst_key in df_results['mst_key']:\n",
+ " \n",
+ " #train\n",
+ " df_output_info = %sql SELECT training_metrics,training_loss FROM cifar10_multi_model_info WHERE mst_key = $mst_key\n",
+ " df_output_info = df_output_info.DataFrame()\n",
+ " training_metrics = df_output_info['training_metrics'][0]\n",
+ " training_loss = df_output_info['training_loss'][0]\n",
+ " \n",
+ " #test\n",
+ " df_output_info = %sql SELECT validation_metrics,validation_loss FROM cifar10_multi_model_info WHERE mst_key = $mst_key\n",
+ " df_output_info = df_output_info.DataFrame()\n",
+ " validation_metrics = df_output_info['validation_metrics'][0]\n",
+ " validation_loss = df_output_info['validation_loss'][0]\n",
+ " \n",
+ " label_train = str(mst_key) + '-train'\n",
+ " #ax_metric.plot(iters, training_metrics, label=label_train, marker='x')\n",
+ " #ax_loss.plot(iters, training_loss, label=label_train, marker='x')\n",
+ " ax_metric.plot(iters, training_metrics, label=label_train)\n",
+ " ax_loss.plot(iters, training_loss, label=label_train)\n",
+ " \n",
+ " \n",
+ " label_test = str(mst_key) + '-test'\n",
+ " #ax_metric.plot(iters, validation_metrics, label=label_test, marker='o')\n",
+ " #ax_loss.plot(iters, validation_loss, label=label_test, marker='o')\n",
+ " ax_metric.plot(iters, validation_metrics, label=label_test)\n",
+ " ax_loss.plot(iters, validation_loss, label=label_test)\n",
+ "\n",
+ "\n",
+ "plt.legend()\n",
+ "# fig.savefig('./lc_keras_fit.png', dpi = 300)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"predict\"></a>\n",
+ "# 7. Inference\n",
+ "\n",
+ "## 7a. Run predict on the whole validation dataset\n",
+ "\n",
+ "Pick a reasonable model from the previous run."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>mst_key</th>\n",
+ " <th>model_id</th>\n",
+ " <th>compile_params</th>\n",
+ " <th>fit_params</th>\n",
+ " <th>model_type</th>\n",
+ " <th>model_size</th>\n",
+ " <th>metrics_elapsed_time</th>\n",
+ " <th>metrics_type</th>\n",
+ " <th>training_metrics_final</th>\n",
+ " <th>training_loss_final</th>\n",
+ " <th>training_metrics</th>\n",
+ " <th>training_loss</th>\n",
+ " <th>validation_metrics_final</th>\n",
+ " <th>validation_loss_final</th>\n",
+ " <th>validation_metrics</th>\n",
+ " <th>validation_loss</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>10</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='adam(lr=0.0001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=128,epochs=5</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>2159.70019531</td>\n",
+ " <td>[158.506701946259, 323.779083013535, 491.302199840546, 660.148201942444, 829.340363025665, 999.844955921173, 1169.83032798767, 1340.5411260128, 1513.8498609066, 1687.69545793533]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.867579996586</td>\n",
+ " <td>0.380911707878</td>\n",
+ " <td>[0.576640009880066, 0.66431999206543, 0.707199990749359, 0.749520003795624, 0.778980016708374, 0.799520015716553, 0.820659995079041, 0.839940011501312, 0.854659974575043, 0.867579996585846]</td>\n",
+ " <td>[1.20035827159882, 0.945839285850525, 0.823047578334808, 0.703792750835419, 0.624295234680176, 0.566677749156952, 0.511338114738464, 0.459649682044983, 0.418204575777054, 0.380911707878113]</td>\n",
+ " <td>0.799700021744</td>\n",
+ " <td>0.571797966957</td>\n",
+ " <td>[0.572700023651123, 0.655099987983704, 0.69980001449585, 0.731299996376038, 0.756399989128113, 0.771399974822998, 0.778999984264374, 0.791599988937378, 0.798200011253357, 0.799700021743774]</td>\n",
+ " <td>[1.20565474033356, 0.964107036590576, 0.849484860897064, 0.752416431903839, 0.691979646682739, 0.65268349647522, 0.628291726112366, 0.599337160587311, 0.586054861545563, 0.571797966957092]</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(10, 2, u\"loss='categorical_crossentropy',optimizer='adam(lr=0.0001)',metrics=['accuracy']\", u'batch_size=128,epochs=5', u'madlib_keras', 2159.70019531, [158.506701946259, 323.779083013535, 491.302199840546, 660.148201942444, 829.340363025665, 999.844955921173, 1169.83032798767, 1340.5411260128, 1513.8498609066, 1687.69545793533], [u'accuracy'], 0.867579996586, 0.380911707878, [0.576640009880066, 0.66431999206543, 0.707199990749359, 0.749520003795624, 0.778980016708374, 0.799520015716553, 0.820659995079041, 0.839940011501312, 0.854659974575043, 0.867579996585846], [1.20035827159882, 0.945839285850525, 0.823047578334808, 0.703792750835419, 0.624295234680176, 0.566677749156952, 0.511338114738464, 0.459649682044983, 0.418204575777054, 0.380911707878113], 0.799700021744, 0.571797966957, [0.572700023651123, 0.655099987983704, 0.69980001449585, 0.731299996376038, 0.756399989128113, 0.771399974822998, 0.778999984264374, 0.791599988937378, 0.798200011253357, 0.799700021743774], [1.20565474033356, 0.964107036590576, 0.849484860897064, 0.752416431903839, 0.691979646682739, 0.65268349647522, 0.628291726112366, 0.599337160587311, 0.586054861545563, 0.571797966957092])]"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT * FROM cifar10_multi_model_info WHERE mst_key=10;"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "Done.\n",
+ "1 rows affected.\n",
+ "5 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>id</th>\n",
+ " <th>estimated_y</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>8</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>8</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>4</td>\n",
+ " <td>8</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>5</td>\n",
+ " <td>6</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(1, 3), (2, 8), (3, 8), (4, 8), (5, 6)]"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS cifar10_val_predict;\n",
+ "\n",
+ "SELECT madlib.madlib_keras_predict('cifar10_multi_model', -- model\n",
+ " 'cifar10_val', -- test_table\n",
+ " 'id', -- id column\n",
+ " 'x', -- independent var\n",
+ " 'cifar10_val_predict', -- output table\n",
+ " 'response', -- prediction type\n",
+ " TRUE, -- use gpus\n",
+ " 10 -- mst_key to use\n",
+ " );\n",
+ "\n",
+ "SELECT * FROM cifar10_val_predict ORDER BY id LIMIT 5;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Count missclassifications"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2003</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(2003L,)]"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT COUNT(*) FROM cifar10_val_predict JOIN cifar10_val USING (id) \n",
+ "WHERE cifar10_val_predict.estimated_y != cifar10_val.y;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Accuracy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>test_accuracy_percent</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>79.97</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(Decimal('79.97'),)]"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT round(count(*)*100.0/10000.0,2) as test_accuracy_percent from\n",
+ " (select cifar10_val.y as actual, cifar10_val_predict.estimated_y as predicted\n",
+ " from cifar10_val_predict inner join cifar10_val\n",
+ " on cifar10_val.id=cifar10_val_predict.id) q\n",
+ "WHERE q.actual=q.predicted;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 7b. Select a random image from the validation dataset and run predict\n",
+ "\n",
+ "Label map"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "label_names = {\n",
+ " 0 :\"airplane\",\n",
+ " 1 :\"automobile\",\n",
+ " 2 :\"bird\",\n",
+ " 3 :\"cat\",\n",
+ " 4 :\"deer\",\n",
+ " 5 :\"dog\",\n",
+ " 6 :\"frog\",\n",
+ " 7 :\"horse\",\n",
+ " 8 :\"ship\",\n",
+ " 9 :\"truck\"\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Pick a random image"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "Done.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS cifar10_val_random;\n",
+ "CREATE TABLE cifar10_val_random AS\n",
+ " SELECT * FROM cifar10_val ORDER BY random() LIMIT 1;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Predict"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "Done.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>id</th>\n",
+ " <th>prob_0</th>\n",
+ " <th>prob_1</th>\n",
+ " <th>prob_2</th>\n",
+ " <th>prob_3</th>\n",
+ " <th>prob_4</th>\n",
+ " <th>prob_5</th>\n",
+ " <th>prob_6</th>\n",
+ " <th>prob_7</th>\n",
+ " <th>prob_8</th>\n",
+ " <th>prob_9</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>5221</td>\n",
+ " <td>1.0724234e-06</td>\n",
+ " <td>1.4683914e-07</td>\n",
+ " <td>1.5451868e-06</td>\n",
+ " <td>2.83348e-07</td>\n",
+ " <td>6.203704e-05</td>\n",
+ " <td>4.6814375e-06</td>\n",
+ " <td>6.654035e-09</td>\n",
+ " <td>0.99993</td>\n",
+ " <td>1.17486385e-08</td>\n",
+ " <td>5.6579474e-08</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(5221, 1.0724234e-06, 1.4683914e-07, 1.5451868e-06, 2.83348e-07, 6.203704e-05, 4.6814375e-06, 6.654035e-09, 0.99993, 1.17486385e-08, 5.6579474e-08)]"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS cifar10_val_random_predict;\n",
+ "\n",
+ "SELECT madlib.madlib_keras_predict('cifar10_multi_model', -- model\n",
+ " 'cifar10_val_random', -- test_table\n",
+ " 'id', -- id column\n",
+ " 'x', -- independent var\n",
+ " 'cifar10_val_random_predict', -- output table\n",
+ " 'prob', -- prediction type\n",
+ " TRUE, -- use gpus\n",
+ " 10 -- mst_key to use\n",
+ " );\n",
+ "\n",
+ "SELECT * FROM cifar10_val_random_predict;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Format output and display"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "Done.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>feature_vector</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[1.0724234e-06, 1.4683914e-07, 1.5451868e-06, 2.83348e-07, 6.203704e-05, 4.6814375e-06, 6.654035e-09, 0.99993, 1.17486385e-08, 5.6579474e-08]</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[([1.0724234e-06, 1.4683914e-07, 1.5451868e-06, 2.83348e-07, 6.203704e-05, 4.6814375e-06, 6.654035e-09, 0.99993, 1.17486385e-08, 5.6579474e-08],)]"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS cifar10_val_random_predict_array, cifar10_val_random_predict_array_summary;\n",
+ "SELECT madlib.cols2vec(\n",
+ " 'cifar10_val_random_predict',\n",
+ " 'cifar10_val_random_predict_array',\n",
+ " '*',\n",
+ " 'id'\n",
+ ");\n",
+ "select * from cifar10_val_random_predict_array;"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.image.AxesImage at 0x15724d350>"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n",
+ " \n",
+ "horse 0.99993\n",
+ "deer 6.203704e-05\n",
+ "dog 4.6814375e-06\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "x = %sql SELECT x FROM cifar10_val_random;\n",
+ "x = x.DataFrame().to_numpy()\n",
+ "import numpy as np\n",
+ "from matplotlib.pyplot import imshow\n",
+ "%matplotlib inline\n",
+ "x_np = np.array(x[0][0], dtype=np.uint8)\n",
+ "imshow(x_np)\n",
+ "\n",
+ "x = %sql SELECT * FROM cifar10_val_random_predict_array;\n",
+ "x = x.DataFrame().to_numpy()\n",
+ "x = np.array(x[0][0])\n",
+ "top_3_prob_label_indices = x.argsort()[-3:][::-1]\n",
+ "print (\" \");\n",
+ "for index in top_3_prob_label_indices:\n",
+ " print (label_names[index], x[index])"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.16"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/community-artifacts/Deep-learning/MADlib-Keras-model-selection-MLP-v1.ipynb b/community-artifacts/Deep-learning/MADlib-Keras-model-selection-MLP-v1.ipynb
index 9b91b31..cfe8c97 100644
--- a/community-artifacts/Deep-learning/MADlib-Keras-model-selection-MLP-v1.ipynb
+++ b/community-artifacts/Deep-learning/MADlib-Keras-model-selection-MLP-v1.ipynb
@@ -10,8 +10,7 @@
"\n",
"Deep learning works best on very large datasets, but that is not convenient for a quick introduction to the syntax. So in this workbook we use the well known iris data set from https://archive.ics.uci.edu/ml/datasets/iris to help get you started. It is similar to the example in user docs http://madlib.apache.org/docs/latest/index.html\n",
"\n",
- "For more realistic examples please refer to the deep learning notebooks at\n",
- "https://github.com/apache/madlib-site/tree/asf-site/community-artifacts\n",
+ "For more realistic examples please refer to the deep learning notebooks at https://github.com/apache/madlib-site/tree/asf-site/community-artifacts\n",
"\n",
"## Table of contents\n",
"\n",
@@ -5702,7 +5701,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
- "version": "2.7.10"
+ "version": "2.7.16"
}
},
"nbformat": 4,
diff --git a/community-artifacts/Deep-learning/Preprocessor-for-images-distribution-rules-v1.ipynb b/community-artifacts/Deep-learning/Preprocessor-for-images-distribution-rules-v1.ipynb
new file mode 100644
index 0000000..b457303
--- /dev/null
+++ b/community-artifacts/Deep-learning/Preprocessor-for-images-distribution-rules-v1.ipynb
@@ -0,0 +1,1966 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Preprocessor for image data - distribution rules\n",
+ "\n",
+ "This notebook shows how to set distribution rules parameter for moving training and validation datasets to certain segments where training will be done. How you distribute data may depend, for example, on how GPUs are attached to your database cluster.\n",
+ "\n",
+ "The distribution rules parameter is part of the mini-batch preprocessor utility for image data:\n",
+ "* `training_preprocessor_dl()` for training datasets\n",
+ "* `validation_preprocessor_dl()` for validation datasets\n",
+ "\n",
+ "\n",
+ "## Table of contents\n",
+ "\n",
+ "<a href=\"#distr\">1. Setup distribution rules</a>\n",
+ "\n",
+ "<a href=\"#pp_train\">2. Run preprocessor for training image data</a>\n",
+ "<ul>\n",
+ "<a href=\"#pp_train2a\">2a. Distribute to all segments</a>\n",
+ " \n",
+ "<a href=\"#pp_train2b\">2b. Distribute to segments on hosts with GPUs attached</a>\n",
+ "\n",
+ "<a href=\"#pp_train2c\">2c. Distribute to segments on a subset of hosts</a>\n",
+ "\n",
+ "<a href=\"#pp_train2d\">2d. Distribute to 1 segment only</a>\n",
+ "\n",
+ "</ul>\n",
+ "\n",
+ "<a href=\"#pp_val\">3. Run preprocessor for validation image data</a>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The sql extension is already loaded. To reload it, use:\n",
+ " %reload_ext sql\n"
+ ]
+ }
+ ],
+ "source": [
+ "%load_ext sql"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "u'Connected: gpadmin@cifar_places'"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Greenplum Database 5.x on GCP - via tunnel\n",
+ "%sql postgresql://gpadmin@localhost:8000/cifar_places\n",
+ " \n",
+ "# PostgreSQL local\n",
+ "#%sql postgresql://fmcquillan@localhost:5432/madlib"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>version</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>MADlib version: 1.17-dev, git revision: rc/1.16-rc1-95-gc62dfe7, cmake configuration time: Tue Mar 17 16:53:55 UTC 2020, build type: RelWithDebInfo, build system: Linux-2.6.32-754.6.3.el6.x86_64, C compiler: gcc 4.4.7, C++ compiler: g++ 4.4.7</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'MADlib version: 1.17-dev, git revision: rc/1.16-rc1-95-gc62dfe7, cmake configuration time: Tue Mar 17 16:53:55 UTC 2020, build type: RelWithDebInfo, build system: Linux-2.6.32-754.6.3.el6.x86_64, C compiler: gcc 4.4.7, C++ compiler: g++ 4.4.7',)]"
+ ]
+ },
+ "execution_count": 30,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%sql select madlib.version();\n",
+ "#%sql select version();"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"distr\"></a>\n",
+ "# 1. Setup distribution rules\n",
+ "\n",
+ "Here are different ways to set up distribution rules tables.\n",
+ "\n",
+ "First get the GPU configuration in the cluster using the MADlib helper function `gpu_configuration`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "Done.\n",
+ "1 rows affected.\n",
+ "20 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>hostname</th>\n",
+ " <th>gpu_descr</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix0</td>\n",
+ " <td>device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix0</td>\n",
+ " <td>device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix0</td>\n",
+ " <td>device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix0</td>\n",
+ " <td>device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix1</td>\n",
+ " <td>device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix1</td>\n",
+ " <td>device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix1</td>\n",
+ " <td>device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix1</td>\n",
+ " <td>device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix2</td>\n",
+ " <td>device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix2</td>\n",
+ " <td>device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix2</td>\n",
+ " <td>device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix2</td>\n",
+ " <td>device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix3</td>\n",
+ " <td>device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix3</td>\n",
+ " <td>device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix3</td>\n",
+ " <td>device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix3</td>\n",
+ " <td>device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix4</td>\n",
+ " <td>device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix4</td>\n",
+ " <td>device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix4</td>\n",
+ " <td>device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>gpsix4</td>\n",
+ " <td>device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'gpsix0', u'device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0'),\n",
+ " (u'gpsix0', u'device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0'),\n",
+ " (u'gpsix0', u'device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0'),\n",
+ " (u'gpsix0', u'device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0'),\n",
+ " (u'gpsix1', u'device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0'),\n",
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+ " (u'gpsix1', u'device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0'),\n",
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+ " (u'gpsix2', u'device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0'),\n",
+ " (u'gpsix2', u'device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0'),\n",
+ " (u'gpsix2', u'device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0'),\n",
+ " (u'gpsix2', u'device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0'),\n",
+ " (u'gpsix3', u'device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0'),\n",
+ " (u'gpsix3', u'device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0'),\n",
+ " (u'gpsix3', u'device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0'),\n",
+ " (u'gpsix3', u'device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0'),\n",
+ " (u'gpsix4', u'device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0'),\n",
+ " (u'gpsix4', u'device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0'),\n",
+ " (u'gpsix4', u'device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0'),\n",
+ " (u'gpsix4', u'device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0')]"
+ ]
+ },
+ "execution_count": 31,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS host_gpu_mapping_tf;\n",
+ "SELECT * FROM madlib.gpu_configuration('host_gpu_mapping_tf');\n",
+ "SELECT * FROM host_gpu_mapping_tf ORDER BY hostname, gpu_descr;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Review the Greenplum segments in the `gp_segment_configuration` table:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "21 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
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+ " <tr>\n",
+ " <th>dbid</th>\n",
+ " <th>content</th>\n",
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+ " <td>5</td>\n",
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+ " <td>gpsix1</td>\n",
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+ " <td>11</td>\n",
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+ " <td>gpsix2</td>\n",
+ " <td>/data/primary1/gpseg9</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>12</td>\n",
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+ " <td>/data/primary2/gpseg10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>13</td>\n",
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+ " </tr>\n",
+ " <tr>\n",
+ " <td>14</td>\n",
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+ " <td>gpsix3</td>\n",
+ " <td>gpsix3</td>\n",
+ " <td>/data/primary0/gpseg12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>15</td>\n",
+ " <td>13</td>\n",
+ " <td>p</td>\n",
+ " <td>p</td>\n",
+ " <td>n</td>\n",
+ " <td>u</td>\n",
+ " <td>40001</td>\n",
+ " <td>gpsix3</td>\n",
+ " <td>gpsix3</td>\n",
+ " <td>/data/primary1/gpseg13</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>16</td>\n",
+ " <td>14</td>\n",
+ " <td>p</td>\n",
+ " <td>p</td>\n",
+ " <td>n</td>\n",
+ " <td>u</td>\n",
+ " <td>40002</td>\n",
+ " <td>gpsix3</td>\n",
+ " <td>gpsix3</td>\n",
+ " <td>/data/primary2/gpseg14</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>17</td>\n",
+ " <td>15</td>\n",
+ " <td>p</td>\n",
+ " <td>p</td>\n",
+ " <td>n</td>\n",
+ " <td>u</td>\n",
+ " <td>40003</td>\n",
+ " <td>gpsix3</td>\n",
+ " <td>gpsix3</td>\n",
+ " <td>/data/primary3/gpseg15</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>18</td>\n",
+ " <td>16</td>\n",
+ " <td>p</td>\n",
+ " <td>p</td>\n",
+ " <td>n</td>\n",
+ " <td>u</td>\n",
+ " <td>40000</td>\n",
+ " <td>gpsix4</td>\n",
+ " <td>gpsix4</td>\n",
+ " <td>/data/primary0/gpseg16</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>19</td>\n",
+ " <td>17</td>\n",
+ " <td>p</td>\n",
+ " <td>p</td>\n",
+ " <td>n</td>\n",
+ " <td>u</td>\n",
+ " <td>40001</td>\n",
+ " <td>gpsix4</td>\n",
+ " <td>gpsix4</td>\n",
+ " <td>/data/primary1/gpseg17</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>20</td>\n",
+ " <td>18</td>\n",
+ " <td>p</td>\n",
+ " <td>p</td>\n",
+ " <td>n</td>\n",
+ " <td>u</td>\n",
+ " <td>40002</td>\n",
+ " <td>gpsix4</td>\n",
+ " <td>gpsix4</td>\n",
+ " <td>/data/primary2/gpseg18</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>21</td>\n",
+ " <td>19</td>\n",
+ " <td>p</td>\n",
+ " <td>p</td>\n",
+ " <td>n</td>\n",
+ " <td>u</td>\n",
+ " <td>40003</td>\n",
+ " <td>gpsix4</td>\n",
+ " <td>gpsix4</td>\n",
+ " <td>/data/primary3/gpseg19</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
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+ " (18, 16, u'p', u'p', u'n', u'u', 40000, u'gpsix4', u'gpsix4', u'/data/primary0/gpseg16'),\n",
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+ " (20, 18, u'p', u'p', u'n', u'u', 40002, u'gpsix4', u'gpsix4', u'/data/primary2/gpseg18'),\n",
+ " (21, 19, u'p', u'p', u'n', u'u', 40003, u'gpsix4', u'gpsix4', u'/data/primary3/gpseg19')]"
+ ]
+ },
+ "execution_count": 32,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT * FROM gp_segment_configuration ORDER BY dbid;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now JOIN the above 2 tables to build up various distribution rules, depending on your needs.\n",
+ "\n",
+ "Build distribution rules table for 4 VMs:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "Done.\n",
+ "16 rows affected.\n",
+ "16 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
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+ " <tr>\n",
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+ " (15, u'gpsix3'),\n",
+ " (16, u'gpsix3'),\n",
+ " (17, u'gpsix3')]"
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS segments_to_use_4VMs;\n",
+ "CREATE TABLE segments_to_use_4VMs AS\n",
+ " SELECT DISTINCT dbid, hostname FROM gp_segment_configuration JOIN host_gpu_mapping_tf USING (hostname)\n",
+ " WHERE role='p' AND content>=0 AND hostname!='gpsix4';\n",
+ "SELECT * FROM segments_to_use_4VMs ORDER BY hostname, dbid;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Build distribution rules table for 2 VMs:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "Done.\n",
+ "8 rows affected.\n",
+ "8 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
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+ " (7, u'gpsix1'),\n",
+ " (8, u'gpsix1'),\n",
+ " (9, u'gpsix1')]"
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS segments_to_use_2VMs;\n",
+ "CREATE TABLE segments_to_use_2VMs AS\n",
+ " SELECT DISTINCT dbid, hostname FROM gp_segment_configuration JOIN host_gpu_mapping_tf USING (hostname)\n",
+ " WHERE role='p' AND content>=0 AND (hostname='gpsix0' OR hostname='gpsix1');\n",
+ "SELECT * FROM segments_to_use_2VMs ORDER BY hostname, dbid;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Build distribution rules table for 1 VM:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "Done.\n",
+ "4 rows affected.\n",
+ "4 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>dbid</th>\n",
+ " <th>hostname</th>\n",
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+ " <td>2</td>\n",
+ " <td>gpsix0</td>\n",
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+ " <td>3</td>\n",
+ " <td>gpsix0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>4</td>\n",
+ " <td>gpsix0</td>\n",
+ " </tr>\n",
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+ " <td>5</td>\n",
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+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(2, u'gpsix0'), (3, u'gpsix0'), (4, u'gpsix0'), (5, u'gpsix0')]"
+ ]
+ },
+ "execution_count": 35,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS segments_to_use_1VM;\n",
+ "CREATE TABLE segments_to_use_1VM AS\n",
+ " SELECT DISTINCT dbid, hostname FROM gp_segment_configuration JOIN host_gpu_mapping_tf USING (hostname)\n",
+ " WHERE role='p' AND content>=0 AND hostname='gpsix0';\n",
+ "SELECT * FROM segments_to_use_1VM ORDER BY hostname, dbid;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Build distribution rules table for 1 segment:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "Done.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>dbid</th>\n",
+ " <th>hostname</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>gpsix0</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(2, u'gpsix0')]"
+ ]
+ },
+ "execution_count": 36,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS segments_to_use_1seg;\n",
+ "CREATE TABLE segments_to_use_1seg AS\n",
+ " SELECT DISTINCT dbid, hostname FROM gp_segment_configuration JOIN host_gpu_mapping_tf USING (hostname)\n",
+ " WHERE dbid=2;\n",
+ "SELECT * FROM segments_to_use_1seg ORDER BY hostname, dbid;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"pp_train\"></a>\n",
+ "# 2. Run preprocessor for training image data\n",
+ "\n",
+ "Run the preprocessor to generate the packed output table on the segments that you want to use for training and validation. The training data in our example is CIFAR-10 and is in table `image_data_train` and the validation data is in `image_data_val` .\n",
+ "\n",
+ "<a id=\"pp_train2a\"></a>\n",
+ "## 2a. All segments\n",
+ "\n",
+ "First distribute to all segments:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "Done.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>source_table</th>\n",
+ " <th>output_table</th>\n",
+ " <th>dependent_varname</th>\n",
+ " <th>independent_varname</th>\n",
+ " <th>dependent_vartype</th>\n",
+ " <th>class_values</th>\n",
+ " <th>buffer_size</th>\n",
+ " <th>normalizing_const</th>\n",
+ " <th>num_classes</th>\n",
+ " <th>distribution_rules</th>\n",
+ " <th>__internal_gpu_config__</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>image_data_train</td>\n",
+ " <td>image_data_train_packed</td>\n",
+ " <td>y</td>\n",
+ " <td>x</td>\n",
+ " <td>smallint</td>\n",
+ " <td>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]</td>\n",
+ " <td>2500</td>\n",
+ " <td>255.0</td>\n",
+ " <td>10</td>\n",
+ " <td>all_segments</td>\n",
+ " <td>all_segments</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'image_data_train', u'image_data_train_packed', u'y', u'x', u'smallint', [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], 2500, 255.0, 10, 'all_segments', 'all_segments')]"
+ ]
+ },
+ "execution_count": 37,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS image_data_train_packed, image_data_train_packed_summary;\n",
+ "\n",
+ "SELECT madlib.training_preprocessor_dl('image_data_train', -- Source table\n",
+ " 'image_data_train_packed', -- Output table\n",
+ " 'y', -- Dependent variable\n",
+ " 'x', -- Independent variable\n",
+ " NULL, -- Buffer size\n",
+ " 255, -- Normalizing constant\n",
+ " NULL, -- Number of classes\n",
+ " 'all_segments' -- Distribution rules\n",
+ " );\n",
+ "SELECT * FROM image_data_train_packed_summary;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Check distribution:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "20 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
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+ " <th>buffer_id</th>\n",
+ " </tr>\n",
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+ " <td>0</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
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+ " <td>[2500, 10]</td>\n",
+ " <td>14</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>5</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>17</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>6</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
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+ " </tr>\n",
+ " <tr>\n",
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+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>9</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>13</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>12</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>15</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>14</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>6</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>19</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>21</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>18</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>27</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>28</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>29</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>8</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>33</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>19</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>34</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>55</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>56</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(0, [2500, 32, 32, 3], [2500, 10], 1),\n",
+ " (1, [2500, 32, 32, 3], [2500, 10], 4),\n",
+ " (2, [2500, 32, 32, 3], [2500, 10], 9),\n",
+ " (3, [2500, 32, 32, 3], [2500, 10], 7),\n",
+ " (4, [2500, 32, 32, 3], [2500, 10], 14),\n",
+ " (5, [2500, 32, 32, 3], [2500, 10], 17),\n",
+ " (6, [2500, 32, 32, 3], [2500, 10], 16),\n",
+ " (7, [2500, 32, 32, 3], [2500, 10], 11),\n",
+ " (9, [2500, 32, 32, 3], [2500, 10], 13),\n",
+ " (12, [2500, 32, 32, 3], [2500, 10], 15),\n",
+ " (14, [2500, 32, 32, 3], [2500, 10], 6),\n",
+ " (19, [2500, 32, 32, 3], [2500, 10], 12),\n",
+ " (21, [2500, 32, 32, 3], [2500, 10], 18),\n",
+ " (27, [2500, 32, 32, 3], [2500, 10], 10),\n",
+ " (28, [2500, 32, 32, 3], [2500, 10], 5),\n",
+ " (29, [2500, 32, 32, 3], [2500, 10], 8),\n",
+ " (33, [2500, 32, 32, 3], [2500, 10], 19),\n",
+ " (34, [2500, 32, 32, 3], [2500, 10], 0),\n",
+ " (55, [2500, 32, 32, 3], [2500, 10], 3),\n",
+ " (56, [2500, 32, 32, 3], [2500, 10], 2)]"
+ ]
+ },
+ "execution_count": 38,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT __dist_key__, independent_var_shape, dependent_var_shape, buffer_id FROM image_data_train_packed ORDER BY __dist_key__;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"pp_train2b\"></a>\n",
+ "## 2b. All segments on hosts with GPUs\n",
+ "\n",
+ "Now distribute to all segments on hosts with GPUs attached:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "Done.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>source_table</th>\n",
+ " <th>output_table</th>\n",
+ " <th>dependent_varname</th>\n",
+ " <th>independent_varname</th>\n",
+ " <th>dependent_vartype</th>\n",
+ " <th>class_values</th>\n",
+ " <th>buffer_size</th>\n",
+ " <th>normalizing_const</th>\n",
+ " <th>num_classes</th>\n",
+ " <th>distribution_rules</th>\n",
+ " <th>__internal_gpu_config__</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>image_data_train</td>\n",
+ " <td>image_data_train_packed</td>\n",
+ " <td>y</td>\n",
+ " <td>x</td>\n",
+ " <td>smallint</td>\n",
+ " <td>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]</td>\n",
+ " <td>2500</td>\n",
+ " <td>255.0</td>\n",
+ " <td>10</td>\n",
+ " <td>[2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21]</td>\n",
+ " <td>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'image_data_train', u'image_data_train_packed', u'y', u'x', u'smallint', [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], 2500, 255.0, 10, [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19])]"
+ ]
+ },
+ "execution_count": 39,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS image_data_train_packed, image_data_train_packed_summary;\n",
+ "\n",
+ "SELECT madlib.training_preprocessor_dl('image_data_train', -- Source table\n",
+ " 'image_data_train_packed', -- Output table\n",
+ " 'y', -- Dependent variable\n",
+ " 'x', -- Independent variable\n",
+ " NULL, -- Buffer size\n",
+ " 255, -- Normalizing constant\n",
+ " NULL, -- Number of classes\n",
+ " 'gpu_segments' -- Distribution rules\n",
+ " );\n",
+ "SELECT * FROM image_data_train_packed_summary;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Check distribution:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "20 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>__dist_key__</th>\n",
+ " <th>independent_var_shape</th>\n",
+ " <th>dependent_var_shape</th>\n",
+ " <th>buffer_id</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>0</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>4</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>9</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>7</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>4</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>14</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>5</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>17</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>6</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>16</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>7</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>9</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>13</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>12</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>15</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>14</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>6</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>19</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>21</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>18</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>27</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>28</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>29</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>8</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>33</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>19</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>34</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>55</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>56</td>\n",
+ " <td>[2500, 32, 32, 3]</td>\n",
+ " <td>[2500, 10]</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(0, [2500, 32, 32, 3], [2500, 10], 1),\n",
+ " (1, [2500, 32, 32, 3], [2500, 10], 4),\n",
+ " (2, [2500, 32, 32, 3], [2500, 10], 9),\n",
+ " (3, [2500, 32, 32, 3], [2500, 10], 7),\n",
+ " (4, [2500, 32, 32, 3], [2500, 10], 14),\n",
+ " (5, [2500, 32, 32, 3], [2500, 10], 17),\n",
+ " (6, [2500, 32, 32, 3], [2500, 10], 16),\n",
+ " (7, [2500, 32, 32, 3], [2500, 10], 11),\n",
+ " (9, [2500, 32, 32, 3], [2500, 10], 13),\n",
+ " (12, [2500, 32, 32, 3], [2500, 10], 15),\n",
+ " (14, [2500, 32, 32, 3], [2500, 10], 6),\n",
+ " (19, [2500, 32, 32, 3], [2500, 10], 12),\n",
+ " (21, [2500, 32, 32, 3], [2500, 10], 18),\n",
+ " (27, [2500, 32, 32, 3], [2500, 10], 10),\n",
+ " (28, [2500, 32, 32, 3], [2500, 10], 5),\n",
+ " (29, [2500, 32, 32, 3], [2500, 10], 8),\n",
+ " (33, [2500, 32, 32, 3], [2500, 10], 19),\n",
+ " (34, [2500, 32, 32, 3], [2500, 10], 0),\n",
+ " (55, [2500, 32, 32, 3], [2500, 10], 3),\n",
+ " (56, [2500, 32, 32, 3], [2500, 10], 2)]"
+ ]
+ },
+ "execution_count": 40,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT __dist_key__, independent_var_shape, dependent_var_shape, buffer_id FROM image_data_train_packed ORDER BY __dist_key__;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"pp_train2c\"></a>\n",
+ "## 2c. Segments on 2 hosts with GPUs\n",
+ "\n",
+ "Now distribute to segments on 2 hosts with GPUs attached (if for some reason I need to do this):"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For small datasets like in this example, buffer size is mainly determined by the number of segments in the database. For a Greenplum database with 2 segments, there will be 2 rows with a buffer size of 26. For PostgresSQL, there would be only one row with a buffer size of 52 since it is a single node database. For larger data sets, other factors go into computing buffers size besides number of segments. \n",
+ "\n",
+ "Review the output summary table:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "Done.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>source_table</th>\n",
+ " <th>output_table</th>\n",
+ " <th>dependent_varname</th>\n",
+ " <th>independent_varname</th>\n",
+ " <th>dependent_vartype</th>\n",
+ " <th>class_values</th>\n",
+ " <th>buffer_size</th>\n",
+ " <th>normalizing_const</th>\n",
+ " <th>num_classes</th>\n",
+ " <th>distribution_rules</th>\n",
+ " <th>__internal_gpu_config__</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>image_data_train</td>\n",
+ " <td>image_data_train_packed</td>\n",
+ " <td>y</td>\n",
+ " <td>x</td>\n",
+ " <td>smallint</td>\n",
+ " <td>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]</td>\n",
+ " <td>6250</td>\n",
+ " <td>255.0</td>\n",
+ " <td>10</td>\n",
+ " <td>[2, 3, 4, 5, 6, 7, 8, 9]</td>\n",
+ " <td>[0, 1, 2, 3, 4, 5, 6, 7]</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'image_data_train', u'image_data_train_packed', u'y', u'x', u'smallint', [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], 6250, 255.0, 10, [2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7])]"
+ ]
+ },
+ "execution_count": 41,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS image_data_train_packed, image_data_train_packed_summary;\n",
+ "\n",
+ "SELECT madlib.training_preprocessor_dl('image_data_train', -- Source table\n",
+ " 'image_data_train_packed', -- Output table\n",
+ " 'y', -- Dependent variable\n",
+ " 'x', -- Independent variable\n",
+ " NULL, -- Buffer size\n",
+ " 255, -- Normalizing constant\n",
+ " NULL, -- Number of classes\n",
+ " 'segments_to_use_2VMs' -- Distribution rules\n",
+ " );\n",
+ "SELECT * FROM image_data_train_packed_summary;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Check distribution:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "8 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>__dist_key__</th>\n",
+ " <th>independent_var_shape</th>\n",
+ " <th>dependent_var_shape</th>\n",
+ " <th>buffer_id</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>0</td>\n",
+ " <td>[6250, 32, 32, 3]</td>\n",
+ " <td>[6250, 10]</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>[6250, 32, 32, 3]</td>\n",
+ " <td>[6250, 10]</td>\n",
+ " <td>4</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>[6250, 32, 32, 3]</td>\n",
+ " <td>[6250, 10]</td>\n",
+ " <td>7</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>14</td>\n",
+ " <td>[6250, 32, 32, 3]</td>\n",
+ " <td>[6250, 10]</td>\n",
+ " <td>6</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>28</td>\n",
+ " <td>[6250, 32, 32, 3]</td>\n",
+ " <td>[6250, 10]</td>\n",
+ " <td>5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>34</td>\n",
+ " <td>[6250, 32, 32, 3]</td>\n",
+ " <td>[6250, 10]</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>55</td>\n",
+ " <td>[6250, 32, 32, 3]</td>\n",
+ " <td>[6250, 10]</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>56</td>\n",
+ " <td>[6250, 32, 32, 3]</td>\n",
+ " <td>[6250, 10]</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(0, [6250, 32, 32, 3], [6250, 10], 1),\n",
+ " (1, [6250, 32, 32, 3], [6250, 10], 4),\n",
+ " (3, [6250, 32, 32, 3], [6250, 10], 7),\n",
+ " (14, [6250, 32, 32, 3], [6250, 10], 6),\n",
+ " (28, [6250, 32, 32, 3], [6250, 10], 5),\n",
+ " (34, [6250, 32, 32, 3], [6250, 10], 0),\n",
+ " (55, [6250, 32, 32, 3], [6250, 10], 3),\n",
+ " (56, [6250, 32, 32, 3], [6250, 10], 2)]"
+ ]
+ },
+ "execution_count": 42,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT __dist_key__, independent_var_shape, dependent_var_shape, buffer_id FROM image_data_train_packed ORDER BY __dist_key__;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"pp_train2d\"></a>\n",
+ "## 2d. Segments on 1 segment\n",
+ "\n",
+ "Now distribute 1 segment on a host with GPUs attached (if for some reason I need to do this):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "Done.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>source_table</th>\n",
+ " <th>output_table</th>\n",
+ " <th>dependent_varname</th>\n",
+ " <th>independent_varname</th>\n",
+ " <th>dependent_vartype</th>\n",
+ " <th>class_values</th>\n",
+ " <th>buffer_size</th>\n",
+ " <th>normalizing_const</th>\n",
+ " <th>num_classes</th>\n",
+ " <th>distribution_rules</th>\n",
+ " <th>__internal_gpu_config__</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>image_data_train</td>\n",
+ " <td>image_data_train_packed</td>\n",
+ " <td>y</td>\n",
+ " <td>x</td>\n",
+ " <td>smallint</td>\n",
+ " <td>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]</td>\n",
+ " <td>16667</td>\n",
+ " <td>255.0</td>\n",
+ " <td>10</td>\n",
+ " <td>[2]</td>\n",
+ " <td>[0]</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'image_data_train', u'image_data_train_packed', u'y', u'x', u'smallint', [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], 16667, 255.0, 10, [2], [0])]"
+ ]
+ },
+ "execution_count": 43,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS image_data_train_packed, image_data_train_packed_summary;\n",
+ "\n",
+ "SELECT madlib.training_preprocessor_dl('image_data_train', -- Source table\n",
+ " 'image_data_train_packed', -- Output table\n",
+ " 'y', -- Dependent variable\n",
+ " 'x', -- Independent variable\n",
+ " NULL, -- Buffer size\n",
+ " 255, -- Normalizing constant\n",
+ " NULL, -- Number of classes\n",
+ " 'segments_to_use_1seg' -- Distribution rules\n",
+ " );\n",
+ "SELECT * FROM image_data_train_packed_summary;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Check distribution:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "3 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>__dist_key__</th>\n",
+ " <th>independent_var_shape</th>\n",
+ " <th>dependent_var_shape</th>\n",
+ " <th>buffer_id</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>34</td>\n",
+ " <td>[16667, 32, 32, 3]</td>\n",
+ " <td>[16667, 10]</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>34</td>\n",
+ " <td>[16666, 32, 32, 3]</td>\n",
+ " <td>[16666, 10]</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>34</td>\n",
+ " <td>[16667, 32, 32, 3]</td>\n",
+ " <td>[16667, 10]</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(34, [16667, 32, 32, 3], [16667, 10], 1),\n",
+ " (34, [16666, 32, 32, 3], [16666, 10], 2),\n",
+ " (34, [16667, 32, 32, 3], [16667, 10], 0)]"
+ ]
+ },
+ "execution_count": 44,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT __dist_key__, independent_var_shape, dependent_var_shape, buffer_id FROM image_data_train_packed ORDER BY __dist_key__;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"pp_val\"></a>\n",
+ "# 3. Run preprocessor for validation image data\n",
+ "\n",
+ "The same idea applies to the validation data set for distribution rules. Continuing the example above with distribution to a single segment:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "Done.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>source_table</th>\n",
+ " <th>output_table</th>\n",
+ " <th>dependent_varname</th>\n",
+ " <th>independent_varname</th>\n",
+ " <th>dependent_vartype</th>\n",
+ " <th>class_values</th>\n",
+ " <th>buffer_size</th>\n",
+ " <th>normalizing_const</th>\n",
+ " <th>num_classes</th>\n",
+ " <th>distribution_rules</th>\n",
+ " <th>__internal_gpu_config__</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>image_data_val</td>\n",
+ " <td>image_data_val_packed</td>\n",
+ " <td>y</td>\n",
+ " <td>x</td>\n",
+ " <td>smallint</td>\n",
+ " <td>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]</td>\n",
+ " <td>10000</td>\n",
+ " <td>255.0</td>\n",
+ " <td>10</td>\n",
+ " <td>[2]</td>\n",
+ " <td>[0]</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'image_data_val', u'image_data_val_packed', u'y', u'x', u'smallint', [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], 10000, 255.0, 10, [2], [0])]"
+ ]
+ },
+ "execution_count": 45,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS image_data_val_packed, image_data_val_packed_summary;\n",
+ "\n",
+ "SELECT madlib.validation_preprocessor_dl('image_data_val', -- Source table\n",
+ " 'image_data_val_packed', -- Output table\n",
+ " 'y', -- Dependent variable\n",
+ " 'x', -- Independent variable\n",
+ " 'image_data_train_packed', -- Training preprocessor output table \n",
+ " NULL, -- Buffer size\n",
+ " 'segments_to_use_1seg' -- Distribution rules\n",
+ " );\n",
+ "SELECT * FROM image_data_val_packed_summary;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Check distribution:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/cifar_places\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>__dist_key__</th>\n",
+ " <th>independent_var_shape</th>\n",
+ " <th>dependent_var_shape</th>\n",
+ " <th>buffer_id</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>34</td>\n",
+ " <td>[10000, 32, 32, 3]</td>\n",
+ " <td>[10000, 10]</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(34, [10000, 32, 32, 3], [10000, 10], 0)]"
+ ]
+ },
+ "execution_count": 46,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT __dist_key__, independent_var_shape, dependent_var_shape, buffer_id FROM image_data_val_packed ORDER BY __dist_key__;"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.16"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/community-artifacts/Deep-learning/automl/.ipynb_checkpoints/hyperband-diag-cifar10-v1-checkpoint.ipynb b/community-artifacts/Deep-learning/automl/.ipynb_checkpoints/hyperband-diag-cifar10-v1-checkpoint.ipynb
new file mode 100644
index 0000000..c5e8919
--- /dev/null
+++ b/community-artifacts/Deep-learning/automl/.ipynb_checkpoints/hyperband-diag-cifar10-v1-checkpoint.ipynb
@@ -0,0 +1,5288 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Hyperband diagonal using CIFAR-10\n",
+ "\n",
+ "Implemention of Hyperband https://arxiv.org/pdf/1603.06560.pdf for MPP with a synchronous barrier. Uses the Hyperband schedule but runs it on a diagonal across brackets, instead of one bracket at a time, to be more efficient with cluster resources.\n",
+ "\n",
+ "The CIFAR-10 dataset consists of 60,000 32x32 colour images in 10 classes, with 6,000 images per class. There are 50,000 training images and 10,000 test images.\n",
+ "https://www.cs.toronto.edu/~kriz/cifar.html\n",
+ "\n",
+ "\n",
+ "## Table of contents \n",
+ "\n",
+ "<a href=\"#setup\">0. Setup</a>\n",
+ "\n",
+ "<a href=\"#load_dataset\">1. Load dataset into table</a>\n",
+ "\n",
+ "<a href=\"#distr\">2. Setup distribution rules and call preprocessor</a>\n",
+ "\n",
+ "<a href=\"#arch\">3. Define and load model architectures</a>\n",
+ "\n",
+ "<a href=\"#hyperband\">4. Hyperband diagonal</a>\n",
+ "\n",
+ "<a href=\"#plot\">5. Plot results</a>\n",
+ "\n",
+ "<a href=\"#print\">6. Pretty print schedules</a>\n",
+ "\n",
+ "<a href=\"#predict\">7. Inference</a>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"setup\"></a>\n",
+ "# 0. Setup"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [],
+ "source": [
+ "%load_ext sql"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "u'Connected: fmcquillan@madlib'"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Greenplum Database 5.x on GCP - via tunnel\n",
+ "#%sql postgresql://gpadmin@localhost:8000/madlib\n",
+ "#%sql postgresql://gpadmin@35.230.53.21:5432/cifar_demo\n",
+ "\n",
+ "# PostgreSQL local\n",
+ "%sql postgresql://fmcquillan@localhost:5432/madlib"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://fmcquillan@localhost:5432/madlib\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>version</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>MADlib version: 1.16, git revision: rc/1.16-rc1, cmake configuration time: Mon Jul 1 17:45:09 UTC 2019, build type: Release, build system: Darwin-16.7.0, C compiler: Clang, C++ compiler: Clang</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'MADlib version: 1.16, git revision: rc/1.16-rc1, cmake configuration time: Mon Jul 1 17:45:09 UTC 2019, build type: Release, build system: Darwin-16.7.0, C compiler: Clang, C++ compiler: Clang',)]"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%sql select madlib.version();\n",
+ "#%sql select version();"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Import libraries and define some params"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Using TensorFlow backend.\n"
+ ]
+ }
+ ],
+ "source": [
+ "from __future__ import print_function\n",
+ "import keras\n",
+ "from keras.datasets import cifar10\n",
+ "from keras.preprocessing.image import ImageDataGenerator\n",
+ "from keras.models import Sequential\n",
+ "from keras.layers import Dense, Dropout, Activation, Flatten, BatchNormalization\n",
+ "from keras.layers import Conv2D, MaxPooling2D\n",
+ "import os"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Others needed in this workbook"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import sys\n",
+ "import os\n",
+ "from matplotlib import pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"load_dataset\"></a>\n",
+ "# 1. Load dataset into table"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "PXF can be used to load image data. \n",
+ "\n",
+ "For this demo, we will get the dataset from Keras and use the script called madlib_image_loader.py located at https://github.com/apache/madlib-site/tree/asf-site/community-artifacts/Deep-learning .\n",
+ "\n",
+ "If the script is not in the same folder as the notebook, you can use the following lines to import it."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import sys\n",
+ "sys.path.insert(1, '/Users/fmcquillan/workspace/madlib-site/community-artifacts/Deep-learning')\n",
+ "from madlib_image_loader import ImageLoader, DbCredentials\n",
+ "\n",
+ "# Specify database credentials, for connecting to db\n",
+ "#db_creds = DbCredentials(user='gpadmin',\n",
+ "# host='localhost',\n",
+ "# port='8000',\n",
+ "# password='')\n",
+ "\n",
+ "db_creds = DbCredentials(user='fmcquillan',\n",
+ " host='localhost',\n",
+ " port='5432',\n",
+ " password='')\n",
+ "\n",
+ "# Initialize ImageLoader (increase num_workers to run faster)\n",
+ "iloader = ImageLoader(num_workers=5, db_creds=db_creds)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Load the training and test data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://fmcquillan@localhost:5432/madlib\n",
+ "Done.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "MainProcess: Connected to madlib db.\n",
+ "Executing: CREATE TABLE cifar10_train (id SERIAL, x REAL[], y TEXT)\n",
+ "CREATE TABLE\n",
+ "Created table cifar10_train in madlib db\n",
+ "Spawning 5 workers...\n",
+ "Initializing PoolWorker-1 [pid 10828]\n",
+ "Initializing PoolWorker-2 [pid 10829]\n",
+ "PoolWorker-1: Created temporary directory /tmp/madlib_DaP40IOgzi\n",
+ "Initializing PoolWorker-3 [pid 10830]\n",
+ "PoolWorker-2: Created temporary directory /tmp/madlib_n5XjJvXs5s\n",
+ "PoolWorker-3: Created temporary directory /tmp/madlib_99mTsCxOFF\n",
+ "Initializing PoolWorker-4 [pid 10831]\n",
+ "PoolWorker-5: Connected to madlib db.\n",
+ "PoolWorker-4: Created temporary directory /tmp/madlib_zGujxaoQIb\n",
+ "Initializing PoolWorker-5 [pid 10832]\n",
+ "PoolWorker-1: Connected to madlib db.\n",
+ "PoolWorker-5: Created temporary directory /tmp/madlib_D6q8olnown\n",
+ "PoolWorker-2: Connected to madlib db.\n",
+ "PoolWorker-3: Connected to madlib db.\n",
+ "PoolWorker-4: Connected to madlib db.\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_DaP40IOgzi/cifar10_train0000.tmp\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_99mTsCxOFF/cifar10_train0000.tmp\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_n5XjJvXs5s/cifar10_train0000.tmp\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_zGujxaoQIb/cifar10_train0000.tmp\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_D6q8olnown/cifar10_train0000.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-3: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-2: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-4: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-5: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_DaP40IOgzi/cifar10_train0001.tmp\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_99mTsCxOFF/cifar10_train0001.tmp\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_n5XjJvXs5s/cifar10_train0001.tmp\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_zGujxaoQIb/cifar10_train0001.tmp\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_D6q8olnown/cifar10_train0001.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-3: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-2: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-4: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-5: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_DaP40IOgzi/cifar10_train0002.tmp\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_99mTsCxOFF/cifar10_train0002.tmp\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_n5XjJvXs5s/cifar10_train0002.tmp\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_zGujxaoQIb/cifar10_train0002.tmp\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_D6q8olnown/cifar10_train0002.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-3: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-2: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-4: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-5: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_DaP40IOgzi/cifar10_train0003.tmp\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_99mTsCxOFF/cifar10_train0003.tmp\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_n5XjJvXs5s/cifar10_train0003.tmp\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_zGujxaoQIb/cifar10_train0003.tmp\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_D6q8olnown/cifar10_train0003.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-3: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-2: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-4: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-5: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_DaP40IOgzi/cifar10_train0004.tmp\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_99mTsCxOFF/cifar10_train0004.tmp\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_n5XjJvXs5s/cifar10_train0004.tmp\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_zGujxaoQIb/cifar10_train0004.tmp\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_D6q8olnown/cifar10_train0004.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-3: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-2: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-4: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-5: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_DaP40IOgzi/cifar10_train0005.tmp\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_99mTsCxOFF/cifar10_train0005.tmp\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_n5XjJvXs5s/cifar10_train0005.tmp\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_zGujxaoQIb/cifar10_train0005.tmp\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_D6q8olnown/cifar10_train0005.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-3: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-2: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-4: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-5: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_DaP40IOgzi/cifar10_train0006.tmp\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_99mTsCxOFF/cifar10_train0006.tmp\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_n5XjJvXs5s/cifar10_train0006.tmp\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_zGujxaoQIb/cifar10_train0006.tmp\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_D6q8olnown/cifar10_train0006.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-3: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-2: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-4: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-5: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_DaP40IOgzi/cifar10_train0007.tmp\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_99mTsCxOFF/cifar10_train0007.tmp\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_n5XjJvXs5s/cifar10_train0007.tmp\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_zGujxaoQIb/cifar10_train0007.tmp\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_D6q8olnown/cifar10_train0007.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-3: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-2: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-4: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-5: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_DaP40IOgzi/cifar10_train0008.tmp\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_99mTsCxOFF/cifar10_train0008.tmp\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_n5XjJvXs5s/cifar10_train0008.tmp\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_zGujxaoQIb/cifar10_train0008.tmp\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_D6q8olnown/cifar10_train0008.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-3: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-2: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-4: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-5: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_DaP40IOgzi/cifar10_train0009.tmp\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_99mTsCxOFF/cifar10_train0009.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-3: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_DaP40IOgzi/cifar10_train0010.tmp\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_99mTsCxOFF/cifar10_train0010.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-3: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_DaP40IOgzi/cifar10_train0011.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-4: Removed temporary directory /tmp/madlib_zGujxaoQIb\n",
+ "PoolWorker-5: Removed temporary directory /tmp/madlib_D6q8olnown\n",
+ "PoolWorker-2: Removed temporary directory /tmp/madlib_n5XjJvXs5s\n",
+ "PoolWorker-1: Removed temporary directory /tmp/madlib_DaP40IOgzi\n",
+ "PoolWorker-3: Removed temporary directory /tmp/madlib_99mTsCxOFF\n",
+ "Done! Loaded 50000 images in 19.7727279663s\n",
+ "5 workers terminated.\n",
+ "MainProcess: Connected to madlib db.\n",
+ "Executing: CREATE TABLE cifar10_val (id SERIAL, x REAL[], y TEXT)\n",
+ "CREATE TABLE\n",
+ "Created table cifar10_val in madlib db\n",
+ "Spawning 5 workers...\n",
+ "Initializing PoolWorker-6 [pid 10850]\n",
+ "PoolWorker-6: Created temporary directory /tmp/madlib_OqFarH4eVS\n",
+ "Initializing PoolWorker-7 [pid 10851]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "PoolWorker-7: Created temporary directory /tmp/madlib_BHhah9z53T\n",
+ "Initializing PoolWorker-8 [pid 10852]\n",
+ "PoolWorker-8: Created temporary directory /tmp/madlib_G5oLCmXwQN\n",
+ "Initializing PoolWorker-9 [pid 10853]\n",
+ "PoolWorker-6: Connected to madlib db.\n",
+ "PoolWorker-9: Created temporary directory /tmp/madlib_THDiiymnsM\n",
+ "Initializing PoolWorker-10 [pid 10854]\n",
+ "PoolWorker-7: Connected to madlib db.\n",
+ "PoolWorker-10: Created temporary directory /tmp/madlib_DLO1TEiyo6\n",
+ "PoolWorker-8: Connected to madlib db.\n",
+ "PoolWorker-9: Connected to madlib db.\n",
+ "PoolWorker-10: Connected to madlib db.\n",
+ "PoolWorker-6: Wrote 1000 images to /tmp/madlib_OqFarH4eVS/cifar10_val0000.tmp\n",
+ "PoolWorker-7: Wrote 1000 images to /tmp/madlib_BHhah9z53T/cifar10_val0000.tmp\n",
+ "PoolWorker-8: Wrote 1000 images to /tmp/madlib_G5oLCmXwQN/cifar10_val0000.tmp\n",
+ "PoolWorker-9: Wrote 1000 images to /tmp/madlib_THDiiymnsM/cifar10_val0000.tmp\n",
+ "PoolWorker-10: Wrote 1000 images to /tmp/madlib_DLO1TEiyo6/cifar10_val0000.tmp\n",
+ "PoolWorker-6: Loaded 1000 images into cifar10_val\n",
+ "PoolWorker-7: Loaded 1000 images into cifar10_val\n",
+ "PoolWorker-8: Loaded 1000 images into cifar10_val\n",
+ "PoolWorker-9: Loaded 1000 images into cifar10_val\n",
+ "PoolWorker-10: Loaded 1000 images into cifar10_val\n",
+ "PoolWorker-6: Wrote 1000 images to /tmp/madlib_OqFarH4eVS/cifar10_val0001.tmp\n",
+ "PoolWorker-7: Wrote 1000 images to /tmp/madlib_BHhah9z53T/cifar10_val0001.tmp\n",
+ "PoolWorker-8: Wrote 1000 images to /tmp/madlib_G5oLCmXwQN/cifar10_val0001.tmp\n",
+ "PoolWorker-9: Wrote 1000 images to /tmp/madlib_THDiiymnsM/cifar10_val0001.tmp\n",
+ "PoolWorker-10: Wrote 1000 images to /tmp/madlib_DLO1TEiyo6/cifar10_val0001.tmp\n",
+ "PoolWorker-6: Loaded 1000 images into cifar10_val\n",
+ "PoolWorker-7: Loaded 1000 images into cifar10_val\n",
+ "PoolWorker-8: Loaded 1000 images into cifar10_val\n",
+ "PoolWorker-9: Loaded 1000 images into cifar10_val\n",
+ "PoolWorker-10: Loaded 1000 images into cifar10_val\n",
+ "PoolWorker-8: Removed temporary directory /tmp/madlib_G5oLCmXwQN\n",
+ "PoolWorker-7: Removed temporary directory /tmp/madlib_BHhah9z53T\n",
+ "PoolWorker-10: Removed temporary directory /tmp/madlib_DLO1TEiyo6\n",
+ "PoolWorker-6: Removed temporary directory /tmp/madlib_OqFarH4eVS\n",
+ "PoolWorker-9: Removed temporary directory /tmp/madlib_THDiiymnsM\n",
+ "Done! Loaded 10000 images in 4.03977298737s\n",
+ "5 workers terminated.\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Load dataset into np array\n",
+ "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
+ "\n",
+ "%sql DROP TABLE IF EXISTS cifar10_train, cifar10_val;\n",
+ "\n",
+ "# Save images to temporary directories and load into database\n",
+ "iloader.load_dataset_from_np(x_train, y_train, 'cifar10_train', append=False)\n",
+ "iloader.load_dataset_from_np(x_test, y_test, 'cifar10_val', append=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/madlib\n",
+ "(psycopg2.errors.UndefinedTable) relation \"cifar_10_train_data\" does not exist\n",
+ "LINE 1: SELECT COUNT(*) FROM cifar_10_train_data;\n",
+ " ^\n",
+ "\n",
+ "[SQL: SELECT COUNT(*) FROM cifar_10_train_data;]\n",
+ "(Background on this error at: http://sqlalche.me/e/f405)\n"
+ ]
+ }
+ ],
+ "source": [
+ "%sql SELECT COUNT(*) FROM cifar10_train;"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>10000</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(10000L,)]"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%sql SELECT COUNT(*) FROM cifar10_val;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"distr\"></a>\n",
+ "# 2. Setup distribution rules and call preprocessor\n",
+ "\n",
+ "Get cluster configuration\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "20 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>hostname</th>\n",
+ " <th>gpu_descr</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>phoenix0</td>\n",
+ " <td>device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>phoenix0</td>\n",
+ " <td>device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>phoenix0</td>\n",
+ " <td>device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>phoenix0</td>\n",
+ " <td>device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>phoenix1</td>\n",
+ " <td>device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>phoenix1</td>\n",
+ " <td>device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>phoenix1</td>\n",
+ " <td>device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>phoenix1</td>\n",
+ " <td>device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>phoenix2</td>\n",
+ " <td>device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>phoenix2</td>\n",
+ " <td>device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>phoenix2</td>\n",
+ " <td>device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>phoenix2</td>\n",
+ " <td>device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>phoenix3</td>\n",
+ " <td>device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>phoenix3</td>\n",
+ " <td>device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>phoenix3</td>\n",
+ " <td>device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>phoenix3</td>\n",
+ " <td>device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>phoenix4</td>\n",
+ " <td>device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>phoenix4</td>\n",
+ " <td>device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>phoenix4</td>\n",
+ " <td>device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>phoenix4</td>\n",
+ " <td>device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'phoenix0', u'device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0'),\n",
+ " (u'phoenix0', u'device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0'),\n",
+ " (u'phoenix0', u'device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0'),\n",
+ " (u'phoenix0', u'device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0'),\n",
+ " (u'phoenix1', u'device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0'),\n",
+ " (u'phoenix1', u'device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0'),\n",
+ " (u'phoenix1', u'device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0'),\n",
+ " (u'phoenix1', u'device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0'),\n",
+ " (u'phoenix2', u'device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0'),\n",
+ " (u'phoenix2', u'device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0'),\n",
+ " (u'phoenix2', u'device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0'),\n",
+ " (u'phoenix2', u'device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0'),\n",
+ " (u'phoenix3', u'device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0'),\n",
+ " (u'phoenix3', u'device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0'),\n",
+ " (u'phoenix3', u'device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0'),\n",
+ " (u'phoenix3', u'device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0'),\n",
+ " (u'phoenix4', u'device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0'),\n",
+ " (u'phoenix4', u'device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0'),\n",
+ " (u'phoenix4', u'device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0'),\n",
+ " (u'phoenix4', u'device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0')]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS host_gpu_mapping_tf;\n",
+ "SELECT * FROM madlib.gpu_configuration('host_gpu_mapping_tf');\n",
+ "SELECT * FROM host_gpu_mapping_tf ORDER BY hostname, gpu_descr;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Below are examples of setting up different distribution rules tables. You can customize this to your needs.\n",
+ "\n",
+ "Build distribution rules table for 4 VMs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS segments_to_use_4VMs;\n",
+ "CREATE TABLE segments_to_use_4VMs AS\n",
+ " SELECT DISTINCT dbid, hostname FROM gp_segment_configuration JOIN host_gpu_mapping_tf USING (hostname)\n",
+ " WHERE role='p' AND content>=0 AND hostname!='phoenix4';\n",
+ "SELECT * FROM segments_to_use_4VMs ORDER BY hostname, dbid;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Build distribution rules table for 2 VMs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS segments_to_use_2VMs;\n",
+ "CREATE TABLE segments_to_use_2VMs AS\n",
+ " SELECT DISTINCT dbid, hostname FROM gp_segment_configuration JOIN host_gpu_mapping_tf USING (hostname)\n",
+ " WHERE role='p' AND content>=0 AND (hostname='phoenix0' OR hostname='phoenix1');\n",
+ "SELECT * FROM segments_to_use_2VMs ORDER BY hostname, dbid;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Build distribution rules table for 1 VMs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS segments_to_use_1VM;\n",
+ "CREATE TABLE segments_to_use_1VM AS\n",
+ " SELECT DISTINCT dbid, hostname FROM gp_segment_configuration JOIN host_gpu_mapping_tf USING (hostname)\n",
+ " WHERE role='p' AND content>=0 AND hostname='phoenix0';\n",
+ "SELECT * FROM segments_to_use_1VM ORDER BY hostname, dbid;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Build distribution rules table for 1 segment"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "5 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>dbid</th>\n",
+ " <th>content</th>\n",
+ " <th>role</th>\n",
+ " <th>preferred_role</th>\n",
+ " <th>mode</th>\n",
+ " <th>status</th>\n",
+ " <th>port</th>\n",
+ " <th>hostname</th>\n",
+ " <th>address</th>\n",
+ " <th>replication_port</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>-1</td>\n",
+ " <td>p</td>\n",
+ " <td>p</td>\n",
+ " <td>s</td>\n",
+ " <td>u</td>\n",
+ " <td>5432</td>\n",
+ " <td>phoenix0</td>\n",
+ " <td>phoenix0</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>0</td>\n",
+ " <td>p</td>\n",
+ " <td>p</td>\n",
+ " <td>c</td>\n",
+ " <td>u</td>\n",
+ " <td>40000</td>\n",
+ " <td>phoenix0</td>\n",
+ " <td>phoenix0</td>\n",
+ " <td>70000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>1</td>\n",
+ " <td>p</td>\n",
+ " <td>p</td>\n",
+ " <td>c</td>\n",
+ " <td>u</td>\n",
+ " <td>40001</td>\n",
+ " <td>phoenix0</td>\n",
+ " <td>phoenix0</td>\n",
+ " <td>70001</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>4</td>\n",
+ " <td>2</td>\n",
+ " <td>p</td>\n",
+ " <td>p</td>\n",
+ " <td>c</td>\n",
+ " <td>u</td>\n",
+ " <td>40002</td>\n",
+ " <td>phoenix0</td>\n",
+ " <td>phoenix0</td>\n",
+ " <td>70002</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>5</td>\n",
+ " <td>3</td>\n",
+ " <td>p</td>\n",
+ " <td>p</td>\n",
+ " <td>c</td>\n",
+ " <td>u</td>\n",
+ " <td>40003</td>\n",
+ " <td>phoenix0</td>\n",
+ " <td>phoenix0</td>\n",
+ " <td>70003</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(1, -1, u'p', u'p', u's', u'u', 5432, u'phoenix0', u'phoenix0', None),\n",
+ " (2, 0, u'p', u'p', u'c', u'u', 40000, u'phoenix0', u'phoenix0', 70000),\n",
+ " (3, 1, u'p', u'p', u'c', u'u', 40001, u'phoenix0', u'phoenix0', 70001),\n",
+ " (4, 2, u'p', u'p', u'c', u'u', 40002, u'phoenix0', u'phoenix0', 70002),\n",
+ " (5, 3, u'p', u'p', u'c', u'u', 40003, u'phoenix0', u'phoenix0', 70003)]"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT * FROM gp_segment_configuration WHERE role='p' AND hostname='phoenix0' ORDER BY dbid;"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>dbid</th>\n",
+ " <th>hostname</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>phoenix0</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(2, u'phoenix0')]"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS segments_to_use_1seg;\n",
+ "CREATE TABLE segments_to_use_1seg AS\n",
+ " SELECT DISTINCT dbid, hostname FROM gp_segment_configuration JOIN host_gpu_mapping_tf USING (hostname)\n",
+ " WHERE dbid=2;\n",
+ "SELECT * FROM segments_to_use_1seg ORDER BY hostname, dbid;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Training dataset (uses training preprocessor):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "16 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
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+ " <tr>\n",
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+ " </tr>\n",
+ " <tr>\n",
+ " <td>[3125, 32, 32, 3]</td>\n",
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+ " <tr>\n",
+ " <td>[3125, 32, 32, 3]</td>\n",
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+ " ([3125, 32, 32, 3], [3125, 10], 12),\n",
+ " ([3125, 32, 32, 3], [3125, 10], 13),\n",
+ " ([3125, 32, 32, 3], [3125, 10], 14),\n",
+ " ([3125, 32, 32, 3], [3125, 10], 15)]"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS cifar10_train_packed, cifar10_train_packed_summary;\n",
+ "\n",
+ "SELECT madlib.training_preprocessor_dl('cifar10_train', -- Source table\n",
+ " 'cifar10_train_packed', -- Output table\n",
+ " 'y', -- Dependent variable\n",
+ " 'x', -- Independent variable\n",
+ " NULL, -- Buffer size\n",
+ " 256.0, -- Normalizing constant\n",
+ " NULL, -- Number of classes\n",
+ " 'gpu_segments' -- Distribution rules\n",
+ " );\n",
+ "\n",
+ "SELECT independent_var_shape, dependent_var_shape, buffer_id FROM cifar10_train_packed ORDER BY buffer_id;"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>source_table</th>\n",
+ " <th>output_table</th>\n",
+ " <th>dependent_varname</th>\n",
+ " <th>independent_varname</th>\n",
+ " <th>dependent_vartype</th>\n",
+ " <th>class_values</th>\n",
+ " <th>buffer_size</th>\n",
+ " <th>normalizing_const</th>\n",
+ " <th>num_classes</th>\n",
+ " <th>distribution_rules</th>\n",
+ " <th>__internal_gpu_config__</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>cifar10_train</td>\n",
+ " <td>cifar10_train_packed</td>\n",
+ " <td>y</td>\n",
+ " <td>x</td>\n",
+ " <td>smallint</td>\n",
+ " <td>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]</td>\n",
+ " <td>3125</td>\n",
+ " <td>256.0</td>\n",
+ " <td>10</td>\n",
+ " <td>[2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]</td>\n",
+ " <td>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'cifar10_train', u'cifar10_train_packed', u'y', u'x', u'smallint', [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], 3125, 256.0, 10, [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15])]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT * FROM cifar10_train_packed_summary;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Validation dataset (uses validation preprocessor):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "16 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
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+ " </tr>\n",
+ " <tr>\n",
+ " <td>[625, 32, 32, 3]</td>\n",
+ " <td>[625, 10]</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[625, 32, 32, 3]</td>\n",
+ " <td>[625, 10]</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[625, 32, 32, 3]</td>\n",
+ " <td>[625, 10]</td>\n",
+ " <td>2</td>\n",
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+ " <td>[625, 32, 32, 3]</td>\n",
+ " <td>[625, 10]</td>\n",
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+ " <td>[625, 32, 32, 3]</td>\n",
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+ " </tr>\n",
+ " <tr>\n",
+ " <td>[625, 32, 32, 3]</td>\n",
+ " <td>[625, 10]</td>\n",
+ " <td>5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[625, 32, 32, 3]</td>\n",
+ " <td>[625, 10]</td>\n",
+ " <td>6</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[625, 32, 32, 3]</td>\n",
+ " <td>[625, 10]</td>\n",
+ " <td>7</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[625, 32, 32, 3]</td>\n",
+ " <td>[625, 10]</td>\n",
+ " <td>8</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[625, 32, 32, 3]</td>\n",
+ " <td>[625, 10]</td>\n",
+ " <td>9</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[625, 32, 32, 3]</td>\n",
+ " <td>[625, 10]</td>\n",
+ " <td>10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[625, 32, 32, 3]</td>\n",
+ " <td>[625, 10]</td>\n",
+ " <td>11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[625, 32, 32, 3]</td>\n",
+ " <td>[625, 10]</td>\n",
+ " <td>12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[625, 32, 32, 3]</td>\n",
+ " <td>[625, 10]</td>\n",
+ " <td>13</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[625, 32, 32, 3]</td>\n",
+ " <td>[625, 10]</td>\n",
+ " <td>14</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[625, 32, 32, 3]</td>\n",
+ " <td>[625, 10]</td>\n",
+ " <td>15</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[([625, 32, 32, 3], [625, 10], 0),\n",
+ " ([625, 32, 32, 3], [625, 10], 1),\n",
+ " ([625, 32, 32, 3], [625, 10], 2),\n",
+ " ([625, 32, 32, 3], [625, 10], 3),\n",
+ " ([625, 32, 32, 3], [625, 10], 4),\n",
+ " ([625, 32, 32, 3], [625, 10], 5),\n",
+ " ([625, 32, 32, 3], [625, 10], 6),\n",
+ " ([625, 32, 32, 3], [625, 10], 7),\n",
+ " ([625, 32, 32, 3], [625, 10], 8),\n",
+ " ([625, 32, 32, 3], [625, 10], 9),\n",
+ " ([625, 32, 32, 3], [625, 10], 10),\n",
+ " ([625, 32, 32, 3], [625, 10], 11),\n",
+ " ([625, 32, 32, 3], [625, 10], 12),\n",
+ " ([625, 32, 32, 3], [625, 10], 13),\n",
+ " ([625, 32, 32, 3], [625, 10], 14),\n",
+ " ([625, 32, 32, 3], [625, 10], 15)]"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS cifar10_val_packed, cifar10_val_packed_summary;\n",
+ "\n",
+ "SELECT madlib.validation_preprocessor_dl('cifar10_val', -- Source table\n",
+ " 'cifar10_val_packed', -- Output table\n",
+ " 'y', -- Dependent variable\n",
+ " 'x', -- Independent variable\n",
+ " 'cifar10_train_packed', -- From training preprocessor step\n",
+ " NULL, -- Buffer size\n",
+ " 'gpu_segments' -- Distribution rules\n",
+ " ); \n",
+ "\n",
+ "SELECT independent_var_shape, dependent_var_shape, buffer_id FROM cifar10_val_packed ORDER BY buffer_id;"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>source_table</th>\n",
+ " <th>output_table</th>\n",
+ " <th>dependent_varname</th>\n",
+ " <th>independent_varname</th>\n",
+ " <th>dependent_vartype</th>\n",
+ " <th>class_values</th>\n",
+ " <th>buffer_size</th>\n",
+ " <th>normalizing_const</th>\n",
+ " <th>num_classes</th>\n",
+ " <th>distribution_rules</th>\n",
+ " <th>__internal_gpu_config__</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>cifar10_val</td>\n",
+ " <td>cifar10_val_packed</td>\n",
+ " <td>y</td>\n",
+ " <td>x</td>\n",
+ " <td>smallint</td>\n",
+ " <td>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]</td>\n",
+ " <td>625</td>\n",
+ " <td>256.0</td>\n",
+ " <td>10</td>\n",
+ " <td>[2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]</td>\n",
+ " <td>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'cifar10_val', u'cifar10_val_packed', u'y', u'x', u'smallint', [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], 625, 256.0, 10, [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15])]"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT * FROM cifar10_val_packed_summary;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"arch\"></a>\n",
+ "# 3. Define and load model architectures\n",
+ "\n",
+ "Here we load some example model architectures from published sources.\n",
+ "\n",
+ "a. Model architecture from https://keras.io/examples/cifar10_cnn/"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "num_classes = 10\n",
+ "\n",
+ "#to be removed\n",
+ "#do this just to get shape for model architecture \n",
+ "(x_train, y_train), (x_test, y_test) = cifar10.load_data()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "_________________________________________________________________\n",
+ "Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ "conv2d_1 (Conv2D) (None, 32, 32, 32) 896 \n",
+ "_________________________________________________________________\n",
+ "activation_1 (Activation) (None, 32, 32, 32) 0 \n",
+ "_________________________________________________________________\n",
+ "conv2d_2 (Conv2D) (None, 30, 30, 32) 9248 \n",
+ "_________________________________________________________________\n",
+ "activation_2 (Activation) (None, 30, 30, 32) 0 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_1 (MaxPooling2 (None, 15, 15, 32) 0 \n",
+ "_________________________________________________________________\n",
+ "dropout_1 (Dropout) (None, 15, 15, 32) 0 \n",
+ "_________________________________________________________________\n",
+ "conv2d_3 (Conv2D) (None, 15, 15, 64) 18496 \n",
+ "_________________________________________________________________\n",
+ "activation_3 (Activation) (None, 15, 15, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "conv2d_4 (Conv2D) (None, 13, 13, 64) 36928 \n",
+ "_________________________________________________________________\n",
+ "activation_4 (Activation) (None, 13, 13, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_2 (MaxPooling2 (None, 6, 6, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "dropout_2 (Dropout) (None, 6, 6, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "flatten_1 (Flatten) (None, 2304) 0 \n",
+ "_________________________________________________________________\n",
+ "dense_1 (Dense) (None, 512) 1180160 \n",
+ "_________________________________________________________________\n",
+ "activation_5 (Activation) (None, 512) 0 \n",
+ "_________________________________________________________________\n",
+ "dropout_3 (Dropout) (None, 512) 0 \n",
+ "_________________________________________________________________\n",
+ "dense_2 (Dense) (None, 10) 5130 \n",
+ "_________________________________________________________________\n",
+ "activation_6 (Activation) (None, 10) 0 \n",
+ "=================================================================\n",
+ "Total params: 1,250,858\n",
+ "Trainable params: 1,250,858\n",
+ "Non-trainable params: 0\n",
+ "_________________________________________________________________\n"
+ ]
+ }
+ ],
+ "source": [
+ "model1 = Sequential()\n",
+ "\n",
+ "model1.add(Conv2D(32, (3, 3), padding='same',\n",
+ " input_shape=x_train.shape[1:]))\n",
+ "model1.add(Activation('relu'))\n",
+ "model1.add(Conv2D(32, (3, 3)))\n",
+ "model1.add(Activation('relu'))\n",
+ "model1.add(MaxPooling2D(pool_size=(2, 2)))\n",
+ "model1.add(Dropout(0.25))\n",
+ "\n",
+ "model1.add(Conv2D(64, (3, 3), padding='same'))\n",
+ "model1.add(Activation('relu'))\n",
+ "model1.add(Conv2D(64, (3, 3)))\n",
+ "model1.add(Activation('relu'))\n",
+ "model1.add(MaxPooling2D(pool_size=(2, 2)))\n",
+ "model1.add(Dropout(0.25))\n",
+ "\n",
+ "model1.add(Flatten())\n",
+ "model1.add(Dense(512))\n",
+ "model1.add(Activation('relu'))\n",
+ "model1.add(Dropout(0.5))\n",
+ "model1.add(Dense(num_classes))\n",
+ "model1.add(Activation('softmax'))\n",
+ "\n",
+ "model1.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'{\"class_name\": \"Sequential\", \"keras_version\": \"2.1.6\", \"config\": [{\"class_name\": \"Conv2D\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"conv2d_1\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"linear\", \"trainable\": true, \"data_format\": \"channels_last\", \"filters\": 32, \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"batch_input_shape\": [null, 32, 32, 3], \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"Activation\", \"config\": {\"activation\": \"relu\", \"trainable\": true, \"name\": \"activation_1\"}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"conv2d_2\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"linear\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"valid\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 32, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"Activation\", \"config\": {\"activation\": \"relu\", \"trainable\": true, \"name\": \"activation_2\"}}, {\"class_name\": \"MaxPooling2D\", \"config\": {\"name\": \"max_pooling2d_1\", \"trainable\": true, \"data_format\": \"channels_last\", \"pool_size\": [2, 2], \"padding\": \"valid\", \"strides\": [2, 2]}}, {\"class_name\": \"Dropout\", \"config\": {\"rate\": 0.25, \"noise_shape\": null, \"trainable\": true, \"seed\": null, \"name\": \"dropout_1\"}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"conv2d_3\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"linear\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 64, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"Activation\", \"config\": {\"activation\": \"relu\", \"trainable\": true, \"name\": \"activation_3\"}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"conv2d_4\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"linear\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"valid\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 64, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"Activation\", \"config\": {\"activation\": \"relu\", \"trainable\": true, \"name\": \"activation_4\"}}, {\"class_name\": \"MaxPooling2D\", \"config\": {\"name\": \"max_pooling2d_2\", \"trainable\": true, \"data_format\": \"channels_last\", \"pool_size\": [2, 2], \"padding\": \"valid\", \"strides\": [2, 2]}}, {\"class_name\": \"Dropout\", \"config\": {\"rate\": 0.25, \"noise_shape\": null, \"trainable\": true, \"seed\": null, \"name\": \"dropout_2\"}}, {\"class_name\": \"Flatten\", \"config\": {\"trainable\": true, \"name\": \"flatten_1\", \"data_format\": \"channels_last\"}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_1\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"linear\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 512, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Activation\", \"config\": {\"activation\": \"relu\", \"trainable\": true, \"name\": \"activation_5\"}}, {\"class_name\": \"Dropout\", \"config\": {\"rate\": 0.5, \"noise_shape\": null, \"trainable\": true, \"seed\": null, \"name\": \"dropout_3\"}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_2\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"linear\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Activation\", \"config\": {\"activation\": \"softmax\", \"trainable\": true, \"name\": \"activation_6\"}}], \"backend\": \"tensorflow\"}'"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model1.to_json()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "b. Model architecture from https://machinelearningmastery.com/how-to-develop-a-cnn-from-scratch-for-cifar-10-photo-classification/"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "_________________________________________________________________\n",
+ "Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ "conv2d_5 (Conv2D) (None, 32, 32, 32) 896 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_1 (Batch (None, 32, 32, 32) 128 \n",
+ "_________________________________________________________________\n",
+ "conv2d_6 (Conv2D) (None, 32, 32, 32) 9248 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_2 (Batch (None, 32, 32, 32) 128 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_3 (MaxPooling2 (None, 16, 16, 32) 0 \n",
+ "_________________________________________________________________\n",
+ "dropout_4 (Dropout) (None, 16, 16, 32) 0 \n",
+ "_________________________________________________________________\n",
+ "conv2d_7 (Conv2D) (None, 16, 16, 64) 18496 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_3 (Batch (None, 16, 16, 64) 256 \n",
+ "_________________________________________________________________\n",
+ "conv2d_8 (Conv2D) (None, 16, 16, 64) 36928 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_4 (Batch (None, 16, 16, 64) 256 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_4 (MaxPooling2 (None, 8, 8, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "dropout_5 (Dropout) (None, 8, 8, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "conv2d_9 (Conv2D) (None, 8, 8, 128) 73856 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_5 (Batch (None, 8, 8, 128) 512 \n",
+ "_________________________________________________________________\n",
+ "conv2d_10 (Conv2D) (None, 8, 8, 128) 147584 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_6 (Batch (None, 8, 8, 128) 512 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_5 (MaxPooling2 (None, 4, 4, 128) 0 \n",
+ "_________________________________________________________________\n",
+ "dropout_6 (Dropout) (None, 4, 4, 128) 0 \n",
+ "_________________________________________________________________\n",
+ "flatten_2 (Flatten) (None, 2048) 0 \n",
+ "_________________________________________________________________\n",
+ "dense_3 (Dense) (None, 128) 262272 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_7 (Batch (None, 128) 512 \n",
+ "_________________________________________________________________\n",
+ "dropout_7 (Dropout) (None, 128) 0 \n",
+ "_________________________________________________________________\n",
+ "dense_4 (Dense) (None, 10) 1290 \n",
+ "=================================================================\n",
+ "Total params: 552,874\n",
+ "Trainable params: 551,722\n",
+ "Non-trainable params: 1,152\n",
+ "_________________________________________________________________\n"
+ ]
+ }
+ ],
+ "source": [
+ "model2 = Sequential()\n",
+ "\n",
+ "model2.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same', input_shape=(32, 32, 3)))\n",
+ "model2.add(BatchNormalization())\n",
+ "model2.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
+ "model2.add(BatchNormalization())\n",
+ "model2.add(MaxPooling2D((2, 2)))\n",
+ "model2.add(Dropout(0.2))\n",
+ "\n",
+ "model2.add(Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
+ "model2.add(BatchNormalization())\n",
+ "model2.add(Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
+ "model2.add(BatchNormalization())\n",
+ "model2.add(MaxPooling2D((2, 2)))\n",
+ "model2.add(Dropout(0.3))\n",
+ "\n",
+ "model2.add(Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
+ "model2.add(BatchNormalization())\n",
+ "model2.add(Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
+ "model2.add(BatchNormalization())\n",
+ "model2.add(MaxPooling2D((2, 2)))\n",
+ "model2.add(Dropout(0.4))\n",
+ "\n",
+ "model2.add(Flatten())\n",
+ "model2.add(Dense(128, activation='relu', kernel_initializer='he_uniform'))\n",
+ "model2.add(BatchNormalization())\n",
+ "model2.add(Dropout(0.5))\n",
+ "model2.add(Dense(10, activation='softmax'))\n",
+ "\n",
+ "model2.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'{\"class_name\": \"Sequential\", \"keras_version\": \"2.1.6\", \"config\": [{\"class_name\": \"Conv2D\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"conv2d_5\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"data_format\": \"channels_last\", \"filters\": 32, \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"batch_input_shape\": [null, 32, 32, 3], \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"BatchNormalization\", \"config\": {\"beta_constraint\": null, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"name\": \"batch_normalization_1\", \"epsilon\": 0.001, \"trainable\": true, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"scale\": true, \"axis\": -1, \"gamma_constraint\": null, \"gamma_regularizer\": null, \"beta_regularizer\": null, \"momentum\": 0.99, \"center\": true}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"conv2d_6\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 32, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"BatchNormalization\", \"config\": {\"beta_constraint\": null, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"name\": \"batch_normalization_2\", \"epsilon\": 0.001, \"trainable\": true, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"scale\": true, \"axis\": -1, \"gamma_constraint\": null, \"gamma_regularizer\": null, \"beta_regularizer\": null, \"momentum\": 0.99, \"center\": true}}, {\"class_name\": \"MaxPooling2D\", \"config\": {\"name\": \"max_pooling2d_3\", \"trainable\": true, \"data_format\": \"channels_last\", \"pool_size\": [2, 2], \"padding\": \"valid\", \"strides\": [2, 2]}}, {\"class_name\": \"Dropout\", \"config\": {\"rate\": 0.2, \"noise_shape\": null, \"trainable\": true, \"seed\": null, \"name\": \"dropout_4\"}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"conv2d_7\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 64, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"BatchNormalization\", \"config\": {\"beta_constraint\": null, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"name\": \"batch_normalization_3\", \"epsilon\": 0.001, \"trainable\": true, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"scale\": true, \"axis\": -1, \"gamma_constraint\": null, \"gamma_regularizer\": null, \"beta_regularizer\": null, \"momentum\": 0.99, \"center\": true}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"conv2d_8\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 64, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"BatchNormalization\", \"config\": {\"beta_constraint\": null, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"name\": \"batch_normalization_4\", \"epsilon\": 0.001, \"trainable\": true, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"scale\": true, \"axis\": -1, \"gamma_constraint\": null, \"gamma_regularizer\": null, \"beta_regularizer\": null, \"momentum\": 0.99, \"center\": true}}, {\"class_name\": \"MaxPooling2D\", \"config\": {\"name\": \"max_pooling2d_4\", \"trainable\": true, \"data_format\": \"channels_last\", \"pool_size\": [2, 2], \"padding\": \"valid\", \"strides\": [2, 2]}}, {\"class_name\": \"Dropout\", \"config\": {\"rate\": 0.3, \"noise_shape\": null, \"trainable\": true, \"seed\": null, \"name\": \"dropout_5\"}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"conv2d_9\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 128, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"BatchNormalization\", \"config\": {\"beta_constraint\": null, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"name\": \"batch_normalization_5\", \"epsilon\": 0.001, \"trainable\": true, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"scale\": true, \"axis\": -1, \"gamma_constraint\": null, \"gamma_regularizer\": null, \"beta_regularizer\": null, \"momentum\": 0.99, \"center\": true}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"conv2d_10\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 128, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"BatchNormalization\", \"config\": {\"beta_constraint\": null, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"name\": \"batch_normalization_6\", \"epsilon\": 0.001, \"trainable\": true, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"scale\": true, \"axis\": -1, \"gamma_constraint\": null, \"gamma_regularizer\": null, \"beta_regularizer\": null, \"momentum\": 0.99, \"center\": true}}, {\"class_name\": \"MaxPooling2D\", \"config\": {\"name\": \"max_pooling2d_5\", \"trainable\": true, \"data_format\": \"channels_last\", \"pool_size\": [2, 2], \"padding\": \"valid\", \"strides\": [2, 2]}}, {\"class_name\": \"Dropout\", \"config\": {\"rate\": 0.4, \"noise_shape\": null, \"trainable\": true, \"seed\": null, \"name\": \"dropout_6\"}}, {\"class_name\": \"Flatten\", \"config\": {\"trainable\": true, \"name\": \"flatten_2\", \"data_format\": \"channels_last\"}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"dense_3\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 128, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"BatchNormalization\", \"config\": {\"beta_constraint\": null, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"name\": \"batch_normalization_7\", \"epsilon\": 0.001, \"trainable\": true, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"scale\": true, \"axis\": -1, \"gamma_constraint\": null, \"gamma_regularizer\": null, \"beta_regularizer\": null, \"momentum\": 0.99, \"center\": true}}, {\"class_name\": \"Dropout\", \"config\": {\"rate\": 0.5, \"noise_shape\": null, \"trainable\": true, \"seed\": null, \"name\": \"dropout_7\"}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_4\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"softmax\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}], \"backend\": \"tensorflow\"}'"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model2.to_json()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "c. Another model architecture from https://machinelearningmastery.com/how-to-develop-a-cnn-from-scratch-for-cifar-10-photo-classification/"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "_________________________________________________________________\n",
+ "Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ "conv2d_11 (Conv2D) (None, 32, 32, 32) 896 \n",
+ "_________________________________________________________________\n",
+ "conv2d_12 (Conv2D) (None, 32, 32, 32) 9248 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_6 (MaxPooling2 (None, 16, 16, 32) 0 \n",
+ "_________________________________________________________________\n",
+ "dropout_8 (Dropout) (None, 16, 16, 32) 0 \n",
+ "_________________________________________________________________\n",
+ "conv2d_13 (Conv2D) (None, 16, 16, 64) 18496 \n",
+ "_________________________________________________________________\n",
+ "conv2d_14 (Conv2D) (None, 16, 16, 64) 36928 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_7 (MaxPooling2 (None, 8, 8, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "dropout_9 (Dropout) (None, 8, 8, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "conv2d_15 (Conv2D) (None, 8, 8, 128) 73856 \n",
+ "_________________________________________________________________\n",
+ "conv2d_16 (Conv2D) (None, 8, 8, 128) 147584 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_8 (MaxPooling2 (None, 4, 4, 128) 0 \n",
+ "_________________________________________________________________\n",
+ "dropout_10 (Dropout) (None, 4, 4, 128) 0 \n",
+ "_________________________________________________________________\n",
+ "flatten_3 (Flatten) (None, 2048) 0 \n",
+ "_________________________________________________________________\n",
+ "dense_5 (Dense) (None, 128) 262272 \n",
+ "_________________________________________________________________\n",
+ "dropout_11 (Dropout) (None, 128) 0 \n",
+ "_________________________________________________________________\n",
+ "dense_6 (Dense) (None, 10) 1290 \n",
+ "=================================================================\n",
+ "Total params: 550,570\n",
+ "Trainable params: 550,570\n",
+ "Non-trainable params: 0\n",
+ "_________________________________________________________________\n"
+ ]
+ }
+ ],
+ "source": [
+ "model3 = Sequential()\n",
+ "\n",
+ "model3.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same', input_shape=(32, 32, 3)))\n",
+ "model3.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
+ "model3.add(MaxPooling2D((2, 2)))\n",
+ "model3.add(Dropout(0.2))\n",
+ "model3.add(Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
+ "model3.add(Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
+ "model3.add(MaxPooling2D((2, 2)))\n",
+ "model3.add(Dropout(0.3))\n",
+ "model3.add(Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
+ "model3.add(Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
+ "model3.add(MaxPooling2D((2, 2)))\n",
+ "model3.add(Dropout(0.4))\n",
+ "model3.add(Flatten())\n",
+ "model3.add(Dense(128, activation='relu', kernel_initializer='he_uniform'))\n",
+ "model3.add(Dropout(0.5))\n",
+ "model3.add(Dense(10, activation='softmax'))\n",
+ "\n",
+ "model3.summary()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Load into model architecture table using psycopg2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "3 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>model_id</th>\n",
+ " <th>name</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>CNN from Keras docs for CIFAR-10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>CNN from Jason Brownlee blog post</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>CNN from Jason Brownlee blog post - no batch normalization</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(1, u'CNN from Keras docs for CIFAR-10'),\n",
+ " (2, u'CNN from Jason Brownlee blog post'),\n",
+ " (3, u'CNN from Jason Brownlee blog post - no batch normalization')]"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import psycopg2 as p2\n",
+ "#conn = p2.connect('postgresql://gpadmin@35.239.240.26:5432/madlib')\n",
+ "#conn = p2.connect('postgresql://fmcquillan@localhost:5432/madlib')\n",
+ "conn = p2.connect('postgresql://gpadmin@localhost:8000/cifar_demo')\n",
+ "cur = conn.cursor()\n",
+ "\n",
+ "%sql DROP TABLE IF EXISTS model_arch_table_cifar10;\n",
+ "query = \"SELECT madlib.load_keras_model('model_arch_table_cifar10', %s, NULL, %s)\"\n",
+ "cur.execute(query,[model1.to_json(), \"CNN from Keras docs for CIFAR-10\"])\n",
+ "conn.commit()\n",
+ "\n",
+ "query = \"SELECT madlib.load_keras_model('model_arch_table_cifar10', %s, NULL, %s)\"\n",
+ "cur.execute(query,[model2.to_json(), \"CNN from Jason Brownlee blog post\"])\n",
+ "conn.commit()\n",
+ "\n",
+ "query = \"SELECT madlib.load_keras_model('model_arch_table_cifar10', %s, NULL, %s)\"\n",
+ "cur.execute(query,[model3.to_json(), \"CNN from Jason Brownlee blog post - no batch normalization\"])\n",
+ "conn.commit()\n",
+ "\n",
+ "# check model loaded OK\n",
+ "%sql SELECT model_id, name FROM model_arch_table_cifar10 ORDER BY model_id;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"hyperband\"></a>\n",
+ "# 4. Hyperband diagonal\n",
+ "\n",
+ "Create tables for intermediate and overall results from Hyperband, which is running on top of MADlib model selection methods."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "Done.\n",
+ "Done.\n",
+ "Done.\n",
+ "Done.\n",
+ "Done.\n",
+ "1 rows affected.\n",
+ "Done.\n",
+ "Done.\n",
+ "Done.\n",
+ "Done.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "-- overall results table\n",
+ "DROP TABLE IF EXISTS results_cifar10;\n",
+ "CREATE TABLE results_cifar10 ( \n",
+ " mst_key INTEGER, -- note not SERIAL\n",
+ " model_id INTEGER, \n",
+ " compile_params TEXT,\n",
+ " fit_params TEXT, \n",
+ " model_type TEXT, \n",
+ " model_size DOUBLE PRECISION, \n",
+ " metrics_elapsed_time DOUBLE PRECISION[], \n",
+ " metrics_type TEXT[], \n",
+ " training_metrics_final DOUBLE PRECISION, \n",
+ " training_loss_final DOUBLE PRECISION, \n",
+ " training_metrics DOUBLE PRECISION[], \n",
+ " training_loss DOUBLE PRECISION[], \n",
+ " validation_metrics_final DOUBLE PRECISION, \n",
+ " validation_loss_final DOUBLE PRECISION, \n",
+ " validation_metrics DOUBLE PRECISION[], \n",
+ " validation_loss DOUBLE PRECISION[], \n",
+ " model_arch_table TEXT, \n",
+ " num_iterations INTEGER, \n",
+ " start_training_time TIMESTAMP, \n",
+ " end_training_time TIMESTAMP,\n",
+ " s INTEGER, -- bracket number from Hyperband\n",
+ " i INTEGER, -- iteration corresponding to successive having within a bracket\n",
+ " run_id SERIAL -- global counter for the training runs\n",
+ " );\n",
+ "\n",
+ "-- all model selections:\n",
+ "-- model selection table containing all model configs (all brackets)\n",
+ "DROP TABLE IF EXISTS mst_table_hb_cifar10;\n",
+ "CREATE TABLE mst_table_hb_cifar10 (\n",
+ " mst_key SERIAL, \n",
+ " s INTEGER, -- bracket\n",
+ " model_id INTEGER, \n",
+ " compile_params VARCHAR, \n",
+ " fit_params VARCHAR\n",
+ " );\n",
+ "\n",
+ "-- model selection summary table\n",
+ "DROP TABLE IF EXISTS mst_table_hb_cifar10_summary;\n",
+ "CREATE TABLE mst_table_hb_cifar10_summary (model_arch_table VARCHAR);\n",
+ "INSERT INTO mst_table_hb_cifar10_summary VALUES ('model_arch_table_cifar10');\n",
+ "\n",
+ "-- diagonal model selections:\n",
+ "-- model selection table for diagonal: fit() will be called on a per diagonal basis\n",
+ "DROP TABLE IF EXISTS mst_diag_table_hb_cifar10;\n",
+ "CREATE TABLE mst_diag_table_hb_cifar10 (\n",
+ " mst_key INTEGER, -- note not SERIAL since this table derived from main model selection table\n",
+ " s INTEGER, -- bracket\n",
+ " model_id INTEGER, \n",
+ " compile_params VARCHAR, \n",
+ " fit_params VARCHAR\n",
+ " );\n",
+ "\n",
+ "-- model selection summary table for diagonal table\n",
+ "DROP TABLE IF EXISTS mst_diag_table_hb_cifar10_summary;\n",
+ "CREATE TABLE mst_diag_table_hb_cifar10_summary (model_arch_table VARCHAR);\n",
+ "INSERT INTO mst_diag_table_hb_cifar10_summary VALUES ('model_arch_table_cifar10');"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Generalize table names"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "results_table = 'results_cifar10'\n",
+ "\n",
+ "output_table = 'cifar10_multi_model'\n",
+ "output_table_info = '_'.join([output_table, 'info'])\n",
+ "output_table_summary = '_'.join([output_table, 'summary'])\n",
+ "\n",
+ "best_model = 'cifar10_best_model'\n",
+ "best_model_info = '_'.join([best_model, 'info'])\n",
+ "best_model_summary = '_'.join([best_model, 'summary'])\n",
+ "\n",
+ "\n",
+ "mst_table = 'mst_table_hb_cifar10'\n",
+ "mst_table_summary = '_'.join([mst_table, 'summary'])\n",
+ "\n",
+ "mst_diag_table = 'mst_diag_table_hb_cifar10'\n",
+ "mst_diag_table_summary = '_'.join([mst_diag_table, 'summary'])\n",
+ "\n",
+ "model_arch_table = 'model_arch_table_cifar10'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Hyperband diagonal logic"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Define variables for Hyperband"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "max_iter = 27 # maximum iterations per configuration\n",
+ "eta = 3 # defines downsampling rate (default = 3)\n",
+ "skip_last = 0 # 1 means skip last run in each bracket, 0 means run full bracket"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "from random import random\n",
+ "from math import log, ceil\n",
+ "from time import time, ctime\n",
+ "\n",
+ "class Hyperband_diagonal:\n",
+ " \n",
+ " def __init__( self, get_params_function, try_params_function ):\n",
+ " self.get_params = get_params_function #\n",
+ " self.try_params = try_params_function\n",
+ "\n",
+ " self.max_iter = max_iter \n",
+ " self.eta = eta \n",
+ " self.skip_last = skip_last \n",
+ "\n",
+ " self.logeta = lambda x: log( x ) / log( self.eta )\n",
+ " self.s_max = int( self.logeta( self.max_iter ))\n",
+ " self.B = ( self.s_max + 1 ) * self.max_iter\n",
+ " \n",
+ " #echo output\n",
+ " print (\"max_iter = \" + str(self.max_iter))\n",
+ " print (\"eta = \" + str(self.eta))\n",
+ " print (\"B = \" + str(self.s_max+1) + \"*max_iter = \" + str(self.B))\n",
+ " print (\"skip_last = \" + str(self.skip_last))\n",
+ " \n",
+ " self.setup_full_schedule()\n",
+ " self.create_mst_superset()\n",
+ " \n",
+ " self.best_loss = np.inf\n",
+ " self.best_accuracy = 0.0\n",
+ "\n",
+ " # create full Hyperband schedule for all brackets ahead of time\n",
+ " def setup_full_schedule(self):\n",
+ " self.n_vals = np.zeros((self.s_max+1, self.s_max+1), dtype=int)\n",
+ " self.r_vals = np.zeros((self.s_max+1, self.s_max+1), dtype=int)\n",
+ " \n",
+ " print (\" \")\n",
+ " print (\"Hyperband brackets\")\n",
+ "\n",
+ " # loop through each bracket in reverse order\n",
+ " for s in reversed(range(self.s_max+1)):\n",
+ " \n",
+ " print (\" \")\n",
+ " print (\"s=\" + str(s))\n",
+ " print (\"n_i r_i\")\n",
+ " print (\"------------\")\n",
+ "\n",
+ " for i in range(s+1):\n",
+ " # n_i configs for r_i iterations\n",
+ " n_i = n*self.eta**(-i)\n",
+ " r_i = r*self.eta**(i)\n",
+ "\n",
+ " self.n_vals[s][i] = n_i\n",
+ " self.r_vals[s][i] = r_i\n",
+ "\n",
+ " print (str(n_i) + \" \" + str (r_i))\n",
+ " \n",
+ " \n",
+ " # generate model selection tuples for all brackets\n",
+ " def create_mst_superset(self):\n",
+ " \n",
+ " print (\" \")\n",
+ " print (\"Create superset of MSTs for each bracket s\")\n",
+ " \n",
+ " # get hyper parameter configs for each bracket s\n",
+ " for s in reversed(range(self.s_max+1)):\n",
+ " n = int(ceil(int(self.B/self.max_iter/(s+1))*self.eta**s)) # initial number of configurations\n",
+ " r = self.max_iter*self.eta**(-s) # initial number of iterations to run configurations for\n",
+ "\n",
+ " print (\" \")\n",
+ " print (\"s=\" + str(s))\n",
+ " print (\"n=\" + str(n))\n",
+ " print (\"r=\" + str(r))\n",
+ " print (\" \")\n",
+ " \n",
+ " # n random configurations for each bracket s\n",
+ " self.get_params(n, s)\n",
+ " \n",
+ " \n",
+ " # Hyperband diagonal logic\n",
+ " def run(self): \n",
+ " \n",
+ " print (\" \")\n",
+ " print (\"Hyperband diagonal\")\n",
+ " print (\"Outer loop on diagonal:\")\n",
+ " \n",
+ " # outer loop on diagonal\n",
+ " #for i in range(self.s_max+1):\n",
+ " for i in range((self.s_max+1) - int(self.skip_last)):\n",
+ " print (\" \")\n",
+ " print (\"i=\" + str(i))\n",
+ " \n",
+ " # zero out diagonal table\n",
+ " %sql TRUNCATE TABLE $mst_diag_table\n",
+ " \n",
+ " # loop on brackets s desc to create diagonal table\n",
+ " print (\"Loop on s desc to create diagonal table:\")\n",
+ " for s in range(self.s_max, self.s_max-i-1, -1):\n",
+ "\n",
+ " # build up mst table for diagonal\n",
+ " %sql INSERT INTO $mst_diag_table (SELECT * FROM $mst_table WHERE s=$s);\n",
+ " \n",
+ " # first pass\n",
+ " if i == 0:\n",
+ " first_pass = True\n",
+ " else:\n",
+ " first_pass = False\n",
+ " \n",
+ " # multi-model training\n",
+ " print (\" \")\n",
+ " print (\"Try params for i = \" + str(i))\n",
+ " U = self.try_params(i, self.r_vals[self.s_max][i], first_pass) # r_i is the same for all diagonal elements\n",
+ " \n",
+ " # loop on brackets s desc to prune model selection table\n",
+ " # don't need to prune if finished last diagonal\n",
+ " #if i < (self.s_max):\n",
+ " if i < (self.s_max - int(self.skip_last)):\n",
+ " print (\"Loop on s desc to prune mst table:\")\n",
+ " for s in range(self.s_max, self.s_max-i-1, -1):\n",
+ " \n",
+ " # compute number of configs to keep\n",
+ " # remember i value is different for each bracket s on the diagonal\n",
+ " k = int( self.n_vals[s][s-self.s_max+i] / self.eta)\n",
+ " print (\"Pruning s = {} with k = {}\".format(s, k))\n",
+ "\n",
+ " # temporarily re-define table names due to weird Python scope issues\n",
+ " results_table = 'results_cifar10'\n",
+ "\n",
+ " output_table = 'cifar10_multi_model'\n",
+ " output_table_info = '_'.join([output_table, 'info'])\n",
+ " output_table_summary = '_'.join([output_table, 'summary'])\n",
+ "\n",
+ " mst_table = 'mst_table_hb_cifar10'\n",
+ " mst_table_summary = '_'.join([mst_table, 'summary'])\n",
+ "\n",
+ " mst_diag_table = 'mst_diag_table_hb_cifar10'\n",
+ " mst_diag_table_summary = '_'.join([mst_diag_table, 'summary'])\n",
+ "\n",
+ " model_arch_table = 'model_arch_table_cifar10'\n",
+ " \n",
+ " query = \"\"\"\n",
+ " DELETE FROM {mst_table} WHERE s={s} AND mst_key NOT IN (SELECT {output_table_info}.mst_key FROM {output_table_info} JOIN {mst_table} ON {output_table_info}.mst_key={mst_table}.mst_key WHERE s={s} ORDER BY validation_loss_final ASC LIMIT {k}::INT);\n",
+ " \"\"\".format(**locals())\n",
+ " cur.execute(query)\n",
+ " conn.commit()\n",
+ " \n",
+ " # these were not working so used cursor instead\n",
+ " #%sql DELETE FROM $mst_table WHERE s=$s AND mst_key NOT IN (SELECT $output_table_info.mst_key FROM $output_table_info JOIN $mst_table ON $output_table_info.mst_key=$mst_table.mst_key WHERE s=$s ORDER BY validation_loss_final ASC LIMIT $k::INT);\n",
+ " #%sql DELETE FROM mst_table_hb_cifar10 WHERE s=1 AND mst_key NOT IN (SELECT cifar10_multi_model_info.mst_key FROM cifar10_multi_model_info JOIN mst_table_hb_cifar10 ON cifar10_multi_model_info.mst_key=mst_table_hb_cifar10.mst_key WHERE s=1 ORDER BY validation_loss_final ASC LIMIT 1);\n",
+ " \n",
+ " # keep track of best loss so far and save the model for inference\n",
+ " # get best loss and accuracy from this diagonal run\n",
+ " # (need to check if this will work OK if don't evaluate metrics every iteration)\n",
+ " loss = %sql SELECT validation_loss_final FROM $output_table_info ORDER BY validation_loss_final ASC LIMIT 1;\n",
+ " accuracy = %sql SELECT validation_metrics_final FROM $output_table_info ORDER BY validation_metrics_final DESC LIMIT 1;\n",
+ " \n",
+ " # save best model based on accuracy (could do loss if you wanted)\n",
+ " if accuracy > self.best_accuracy:\n",
+ " \n",
+ " self.best_accuracy = accuracy\n",
+ " \n",
+ " # get best mst_key\n",
+ " best_mst_key = %sql SELECT mst_key FROM $output_table_info ORDER BY validation_metrics_final DESC LIMIT 1; \n",
+ " best_mst_key = best_mst_key.DataFrame().to_numpy()[0][0]\n",
+ "\n",
+ " # save model table (1 row for best model)\n",
+ " %sql DROP TABLE IF EXISTS $best_model;\n",
+ " %sql CREATE TABLE $best_model AS SELECT * FROM $output_table WHERE mst_key = $best_mst_key;\n",
+ "\n",
+ " # save info table (1 row for best model)\n",
+ " %sql DROP TABLE IF EXISTS $best_model_info;\n",
+ " %sql CREATE TABLE $best_model_info AS SELECT * FROM $output_table_info WHERE mst_key = $best_mst_key;\n",
+ " \n",
+ " # save summary table\n",
+ " %sql DROP TABLE IF EXISTS $best_model_summary;\n",
+ " %sql CREATE TABLE $best_model_summary AS SELECT * FROM $output_table_summary;\n",
+ " \n",
+ " if loss < self.best_loss:\n",
+ " self.best_loss = loss\n",
+ " \n",
+ " print (\" \")\n",
+ " print (\"Best validation loss so far = \")\n",
+ " print (str(loss))\n",
+ " print (\"Best validation accuracy so far = \")\n",
+ " print (str(accuracy))\n",
+ " \n",
+ "\n",
+ " \n",
+ " return"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Generate params and insert into MST table. This version of get_params uses the same compile parameters for all optimizers, and the same compile/fit parameters for all model architectures. (This may be too restrictive in some cases.) -- Note 3/13: check SIGMOID paper runs which I think I may have addressed this to some extent"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_params(n, s):\n",
+ " \n",
+ " from sklearn.model_selection import ParameterSampler\n",
+ " from scipy.stats.distributions import uniform\n",
+ " import numpy as np\n",
+ " \n",
+ " # model architecture\n",
+ " model_id = [1,2]\n",
+ "\n",
+ " # compile params\n",
+ " # loss function\n",
+ " loss = ['categorical_crossentropy']\n",
+ " # optimizer\n",
+ " optimizer = ['sgd', 'adam', 'rmsprop']\n",
+ " # learning rate (sample on log scale here not in ParameterSampler)\n",
+ " lr_range = [0.0001, 0.01]\n",
+ " lr = 10**np.random.uniform(np.log10(lr_range[0]), np.log10(lr_range[1]), n)\n",
+ " # metrics\n",
+ " metrics = ['accuracy']\n",
+ "\n",
+ " # fit params\n",
+ " # batch size\n",
+ " batch_size = [32, 64, 128, 256]\n",
+ " # epochs\n",
+ " epochs = [5]\n",
+ "\n",
+ " # create random param list\n",
+ " param_grid = {\n",
+ " 'model_id': model_id,\n",
+ " 'loss': loss,\n",
+ " 'optimizer': optimizer,\n",
+ " 'lr': lr,\n",
+ " 'metrics': metrics,\n",
+ " 'batch_size': batch_size,\n",
+ " 'epochs': epochs\n",
+ " }\n",
+ " param_list = list(ParameterSampler(param_grid, n_iter=n))\n",
+ " \n",
+ " for params in param_list:\n",
+ "\n",
+ " model_id = str(params.get(\"model_id\"))\n",
+ " compile_params = \"$$loss='\" + str(params.get(\"loss\")) + \"',optimizer='\" + str(params.get(\"optimizer\")) + \"(lr=\" + str(params.get(\"lr\")) + \")',metrics=['\" + str(params.get(\"metrics\")) + \"']$$\" \n",
+ " fit_params = \"$$batch_size=\" + str(params.get(\"batch_size\")) + \",epochs=\" + str(params.get(\"epochs\")) + \"$$\" \n",
+ " row_content = \"(\" + str(s) + \", \" + model_id + \", \" + compile_params + \", \" + fit_params + \");\"\n",
+ " \n",
+ " %sql INSERT INTO $mst_table (s, model_id, compile_params, fit_params) VALUES $row_content\n",
+ " \n",
+ " return"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Generate params and insert into MST table. This version of get_params allows for more customization by optimizer and model architecture. This is sort of brute force and can be improved."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_params(n, s):\n",
+ " \n",
+ " from sklearn.model_selection import ParameterSampler\n",
+ " from scipy.stats.distributions import uniform\n",
+ " import numpy as np\n",
+ " \n",
+ " # number of samples by optimizer\n",
+ " #n_adam = int(n/3)\n",
+ " n_adam = int(n/2)\n",
+ " #n_rmsprop = int(n/3)\n",
+ " n_rmsprop = 0\n",
+ " n_sgd = int(n - n_adam - n_rmsprop)\n",
+ "\n",
+ " # 1) adam\n",
+ " \n",
+ " # model architecture\n",
+ " model_id = [2,3]\n",
+ "\n",
+ " # compile params\n",
+ " # loss function\n",
+ " loss = ['categorical_crossentropy']\n",
+ " # optimizer\n",
+ " optimizer = ['adam']\n",
+ " # learning rate (sample on log scale here not in ParameterSampler)\n",
+ " lr_range = [0.0001, 0.001]\n",
+ " lr = 10**np.random.uniform(np.log10(lr_range[0]), np.log10(lr_range[1]), n_adam)\n",
+ " # metrics\n",
+ " metrics = ['accuracy']\n",
+ "\n",
+ " # fit params\n",
+ " # batch size\n",
+ " batch_size = [128, 256]\n",
+ " # epochs\n",
+ " epochs = [5]\n",
+ "\n",
+ " # create random param list\n",
+ " param_grid = {\n",
+ " 'model_id': model_id,\n",
+ " 'loss': loss,\n",
+ " 'optimizer': optimizer,\n",
+ " 'lr': lr,\n",
+ " 'metrics': metrics,\n",
+ " 'batch_size': batch_size,\n",
+ " 'epochs': epochs\n",
+ " }\n",
+ " param_list_adam = list(ParameterSampler(param_grid, n_iter=n_adam))\n",
+ "\n",
+ " # iterate over params\n",
+ " for params in param_list_adam:\n",
+ "\n",
+ " model_id = str(params.get(\"model_id\"))\n",
+ " compile_params = \"$$loss='\" + str(params.get(\"loss\")) + \"',optimizer='\" + str(params.get(\"optimizer\")) + \"(lr=\" + str(params.get(\"lr\")) + \")',metrics=['\" + str(params.get(\"metrics\")) + \"']$$\" \n",
+ " fit_params = \"$$batch_size=\" + str(params.get(\"batch_size\")) + \",epochs=\" + str(params.get(\"epochs\")) + \"$$\" \n",
+ " row_content = \"(\" + str(s) + \", \" + model_id + \", \" + compile_params + \", \" + fit_params + \");\"\n",
+ " \n",
+ " # populate mst table\n",
+ " %sql INSERT INTO $mst_table (s, model_id, compile_params, fit_params) VALUES $row_content\n",
+ " \n",
+ " \n",
+ " # 2) rmsprop\n",
+ " \n",
+ " # model architecture\n",
+ " model_id = [1,2,3]\n",
+ "\n",
+ " # compile params\n",
+ " # loss function\n",
+ " loss = ['categorical_crossentropy']\n",
+ " # optimizer\n",
+ " optimizer = ['rmsprop']\n",
+ " # learning rate (sample on log scale here not in ParameterSampler)\n",
+ " lr_range = [0.0001, 0.001]\n",
+ " lr = 10**np.random.uniform(np.log10(lr_range[0]), np.log10(lr_range[1]), n_rmsprop)\n",
+ " # decay (sample on log scale here not in ParameterSampler if want multiple values)\n",
+ " decay = [1e-6]\n",
+ "\n",
+ " # metrics\n",
+ " metrics = ['accuracy']\n",
+ "\n",
+ " # fit params\n",
+ " # batch size\n",
+ " batch_size = [32, 64, 128, 256]\n",
+ " # epochs\n",
+ " epochs = [5]\n",
+ "\n",
+ " # create random param list\n",
+ " param_grid = {\n",
+ " 'model_id': model_id,\n",
+ " 'loss': loss,\n",
+ " 'optimizer': optimizer,\n",
+ " 'lr': lr,\n",
+ " 'decay': decay,\n",
+ " 'metrics': metrics,\n",
+ " 'batch_size': batch_size,\n",
+ " 'epochs': epochs\n",
+ " }\n",
+ " param_list_rmsprop = list(ParameterSampler(param_grid, n_iter=n_rmsprop))\n",
+ "\n",
+ " # iterate over params\n",
+ " for params in param_list_rmsprop:\n",
+ "\n",
+ " model_id = str(params.get(\"model_id\"))\n",
+ " compile_params = \"$$loss='\" + str(params.get(\"loss\")) + \"',optimizer='\" + str(params.get(\"optimizer\")) + \"(lr=\" + str(params.get(\"lr\")) + \",decay=\" + str(params.get(\"decay\")) + \")',metrics=['\" + str(params.get(\"metrics\")) + \"']$$\" \n",
+ " fit_params = \"$$batch_size=\" + str(params.get(\"batch_size\")) + \",epochs=\" + str(params.get(\"epochs\")) + \"$$\" \n",
+ " row_content = \"(\" + str(s) + \", \" + model_id + \", \" + compile_params + \", \" + fit_params + \");\"\n",
+ " \n",
+ " # populate mst table\n",
+ " %sql INSERT INTO $mst_table (s, model_id, compile_params, fit_params) VALUES $row_content\n",
+ "\n",
+ "\n",
+ " # 3) sgd\n",
+ " \n",
+ " # model architecture\n",
+ " model_id = [2,3]\n",
+ "\n",
+ " # compile params\n",
+ " # loss function\n",
+ " loss = ['categorical_crossentropy']\n",
+ " # optimizer\n",
+ " optimizer = ['sgd']\n",
+ " # learning rate (sample on log scale here not in ParameterSampler)\n",
+ " lr_range = [0.001, 0.005]\n",
+ " lr = 10**np.random.uniform(np.log10(lr_range[0]), np.log10(lr_range[1]), n_sgd)\n",
+ " # momentum (sample on log scale here not in ParameterSampler)\n",
+ " # recall momentum is an exponentially weighted array\n",
+ " beta_range = [0.9, 0.95]\n",
+ " beta = 1.0 - 10**np.random.uniform(np.log10(1.0-beta_range[0]), np.log10(1.0-beta_range[1]), n_sgd)\n",
+ " # metrics\n",
+ " metrics = ['accuracy']\n",
+ "\n",
+ " # fit params\n",
+ " # batch size\n",
+ " batch_size = [128, 256]\n",
+ " # epochs\n",
+ " epochs = [5]\n",
+ "\n",
+ " # create random param list\n",
+ " param_grid = {\n",
+ " 'model_id': model_id,\n",
+ " 'loss': loss,\n",
+ " 'optimizer': optimizer,\n",
+ " 'lr': lr,\n",
+ " 'beta': beta,\n",
+ " 'metrics': metrics,\n",
+ " 'batch_size': batch_size,\n",
+ " 'epochs': epochs\n",
+ " }\n",
+ " param_list_sgd = list(ParameterSampler(param_grid, n_iter=n_sgd))\n",
+ "\n",
+ " # iterate over params\n",
+ " for params in param_list_sgd:\n",
+ "\n",
+ " model_id = str(params.get(\"model_id\"))\n",
+ " compile_params = \"$$loss='\" + str(params.get(\"loss\")) + \"',optimizer='\" + str(params.get(\"optimizer\")) + \"(lr=\" + str(params.get(\"lr\")) + \",momentum=\" + str(params.get(\"beta\")) + \")',metrics=['\" + str(params.get(\"metrics\")) + \"']$$\" \n",
+ " fit_params = \"$$batch_size=\" + str(params.get(\"batch_size\")) + \",epochs=\" + str(params.get(\"epochs\")) + \"$$\" \n",
+ " row_content = \"(\" + str(s) + \", \" + model_id + \", \" + compile_params + \", \" + fit_params + \");\"\n",
+ " \n",
+ " # populate mst table\n",
+ " %sql INSERT INTO $mst_table (s, model_id, compile_params, fit_params) VALUES $row_content\n",
+ "\n",
+ " \n",
+ " #4) organize mst table\n",
+ "\n",
+ " #down sample\n",
+ " #%sql DELETE from $mst_table WHERE mst_key NOT IN (SELECT mst_key FROM $mst_table ORDER BY random() LIMIT $n);\n",
+ "\n",
+ " # make mst_keys contiguous\n",
+ " #%sql ALTER TABLE $mst_table DROP COLUMN mst_key;\n",
+ " #%sql ALTER TABLE $mst_table ADD COLUMN mst_key SERIAL;\n",
+ " \n",
+ " return"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Run model hopper for candidates in MST table"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def try_params(i, r, first_pass):\n",
+ " \n",
+ " # multi-model fit\n",
+ " if first_pass:\n",
+ " # cold start\n",
+ " %sql DROP TABLE IF EXISTS $output_table, $output_table_summary, $output_table_info;\n",
+ " # passing vars as madlib args does not seem to work\n",
+ " #%sql SELECT madlib.madlib_keras_fit_multiple_model('cifar10_train_packed', $output_table, $mst_diag_table, $r_i::INT, 0);\n",
+ " %sql SELECT madlib.madlib_keras_fit_multiple_model('cifar10_train_packed', 'cifar10_multi_model', 'mst_diag_table_hb_cifar10', $r::INT, True, 'cifar10_val_packed',1);\n",
+ "\n",
+ " else:\n",
+ " # warm start to continue from previous run\n",
+ " %sql SELECT madlib.madlib_keras_fit_multiple_model('cifar10_train_packed', 'cifar10_multi_model', 'mst_diag_table_hb_cifar10', $r::INT, True, 'cifar10_val_packed', 1, True);\n",
+ "\n",
+ " # save results via temp table\n",
+ " # add everything from info table\n",
+ " %sql DROP TABLE IF EXISTS temp_results;\n",
+ " %sql CREATE TABLE temp_results AS (SELECT * FROM $output_table_info);\n",
+ " \n",
+ " # add summary table info and i value (same for each row)\n",
+ " %sql ALTER TABLE temp_results ADD COLUMN model_arch_table TEXT, ADD COLUMN num_iterations INTEGER, ADD COLUMN start_training_time TIMESTAMP, ADD COLUMN end_training_time TIMESTAMP, ADD COLUMN s INTEGER, ADD COLUMN i INTEGER;\n",
+ " %sql UPDATE temp_results SET model_arch_table = (SELECT model_arch_table FROM $output_table_summary), num_iterations = (SELECT num_iterations FROM $output_table_summary), start_training_time = (SELECT start_training_time FROM $output_table_summary), end_training_time = (SELECT end_training_time FROM $output_table_summary), i = $i;\n",
+ " \n",
+ " # get the s value for each run (not the same for each row since diagonal table crosses multiple brackets)\n",
+ " %sql UPDATE temp_results SET s = m.s FROM mst_diag_table_hb_cifar10 AS m WHERE m.mst_key = temp_results.mst_key;\n",
+ " \n",
+ " # copy temp table into results table\n",
+ " %sql INSERT INTO $results_table (SELECT * FROM temp_results);\n",
+ "\n",
+ " return"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Call Hyperband diagonal"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "max_iter = 27\n",
+ "eta = 3\n",
+ "B = 4*max_iter = 108\n",
+ "skip_last = 0\n",
+ " \n",
+ "Hyperband brackets\n",
+ " \n",
+ "s=3\n",
+ "n_i r_i\n",
+ "------------\n",
+ "27 1.0\n",
+ "9.0 3.0\n",
+ "3.0 9.0\n",
+ "1.0 27.0\n",
+ " \n",
+ "s=2\n",
+ "n_i r_i\n",
+ "------------\n",
+ "9 3.0\n",
+ "3.0 9.0\n",
+ "1.0 27.0\n",
+ " \n",
+ "s=1\n",
+ "n_i r_i\n",
+ "------------\n",
+ "6 9.0\n",
+ "2.0 27.0\n",
+ " \n",
+ "s=0\n",
+ "n_i r_i\n",
+ "------------\n",
+ "4 27\n",
+ " \n",
+ "Create superset of MSTs for each bracket s\n",
+ " \n",
+ "s=3\n",
+ "n=27\n",
+ "r=1.0\n",
+ " \n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ " \n",
+ "s=2\n",
+ "n=9\n",
+ "r=3.0\n",
+ " \n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ " \n",
+ "s=1\n",
+ "n=6\n",
+ "r=9.0\n",
+ " \n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ " \n",
+ "s=0\n",
+ "n=4\n",
+ "r=27\n",
+ " \n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ " \n",
+ "Hyperband diagonal\n",
+ "Outer loop on diagonal:\n",
+ " \n",
+ "i=0\n",
+ "Done.\n",
+ "Loop on s desc to create diagonal table:\n",
+ "27 rows affected.\n",
+ " \n",
+ "Try params for i = 0\n",
+ "Done.\n",
+ "1 rows affected.\n",
+ "Done.\n",
+ "27 rows affected.\n",
+ "Done.\n",
+ "27 rows affected.\n",
+ "27 rows affected.\n",
+ "27 rows affected.\n",
+ "Loop on s desc to prune mst table:\n",
+ "Pruning s = 3 with k = 9\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "Done.\n",
+ "1 rows affected.\n",
+ "Done.\n",
+ "1 rows affected.\n",
+ "Done.\n",
+ "1 rows affected.\n",
+ " \n",
+ "Best validation loss so far = \n",
+ "+-----------------------+\n",
+ "| validation_loss_final |\n",
+ "+-----------------------+\n",
+ "| 0.782763898373 |\n",
+ "+-----------------------+\n",
+ "Best validation accuracy so far = \n",
+ "+--------------------------+\n",
+ "| validation_metrics_final |\n",
+ "+--------------------------+\n",
+ "| 0.72729998827 |\n",
+ "+--------------------------+\n",
+ " \n",
+ "i=1\n",
+ "Done.\n",
+ "Loop on s desc to create diagonal table:\n",
+ "9 rows affected.\n",
+ "9 rows affected.\n",
+ " \n",
+ "Try params for i = 1\n",
+ "1 rows affected.\n",
+ "Done.\n",
+ "18 rows affected.\n",
+ "Done.\n",
+ "18 rows affected.\n",
+ "18 rows affected.\n",
+ "18 rows affected.\n",
+ "Loop on s desc to prune mst table:\n",
+ "Pruning s = 3 with k = 3\n",
+ "Pruning s = 2 with k = 3\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "Done.\n",
+ "1 rows affected.\n",
+ "Done.\n",
+ "1 rows affected.\n",
+ "Done.\n",
+ "1 rows affected.\n",
+ " \n",
+ "Best validation loss so far = \n",
+ "+-----------------------+\n",
+ "| validation_loss_final |\n",
+ "+-----------------------+\n",
+ "| 0.602479159832 |\n",
+ "+-----------------------+\n",
+ "Best validation accuracy so far = \n",
+ "+--------------------------+\n",
+ "| validation_metrics_final |\n",
+ "+--------------------------+\n",
+ "| 0.805599987507 |\n",
+ "+--------------------------+\n",
+ " \n",
+ "i=2\n",
+ "Done.\n",
+ "Loop on s desc to create diagonal table:\n",
+ "3 rows affected.\n",
+ "3 rows affected.\n",
+ "6 rows affected.\n",
+ " \n",
+ "Try params for i = 2\n",
+ "1 rows affected.\n",
+ "Done.\n",
+ "12 rows affected.\n",
+ "Done.\n",
+ "12 rows affected.\n",
+ "12 rows affected.\n",
+ "12 rows affected.\n",
+ "Loop on s desc to prune mst table:\n",
+ "Pruning s = 3 with k = 1\n",
+ "Pruning s = 2 with k = 1\n",
+ "Pruning s = 1 with k = 2\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "Done.\n",
+ "1 rows affected.\n",
+ "Done.\n",
+ "1 rows affected.\n",
+ "Done.\n",
+ "1 rows affected.\n",
+ " \n",
+ "Best validation loss so far = \n",
+ "+-----------------------+\n",
+ "| validation_loss_final |\n",
+ "+-----------------------+\n",
+ "| 0.595765888691 |\n",
+ "+-----------------------+\n",
+ "Best validation accuracy so far = \n",
+ "+--------------------------+\n",
+ "| validation_metrics_final |\n",
+ "+--------------------------+\n",
+ "| 0.824999988079 |\n",
+ "+--------------------------+\n",
+ " \n",
+ "i=3\n",
+ "Done.\n",
+ "Loop on s desc to create diagonal table:\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "2 rows affected.\n",
+ "4 rows affected.\n",
+ " \n",
+ "Try params for i = 3\n",
+ "1 rows affected.\n",
+ "Done.\n",
+ "8 rows affected.\n",
+ "Done.\n",
+ "8 rows affected.\n",
+ "8 rows affected.\n",
+ "8 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "Done.\n",
+ "1 rows affected.\n",
+ "Done.\n",
+ "1 rows affected.\n",
+ "Done.\n",
+ "1 rows affected.\n",
+ " \n",
+ "Best validation loss so far = \n",
+ "+-----------------------+\n",
+ "| validation_loss_final |\n",
+ "+-----------------------+\n",
+ "| 0.580716967583 |\n",
+ "+-----------------------+\n",
+ "Best validation accuracy so far = \n",
+ "+--------------------------+\n",
+ "| validation_metrics_final |\n",
+ "+--------------------------+\n",
+ "| 0.834100008011 |\n",
+ "+--------------------------+\n"
+ ]
+ }
+ ],
+ "source": [
+ "hp = Hyperband_diagonal(get_params, try_params )\n",
+ "results = hp.run()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"plot\"></a>\n",
+ "# 5. Review and plot results"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>mst_key</th>\n",
+ " <th>model_id</th>\n",
+ " <th>compile_params</th>\n",
+ " <th>fit_params</th>\n",
+ " <th>model_type</th>\n",
+ " <th>model_size</th>\n",
+ " <th>metrics_elapsed_time</th>\n",
+ " <th>metrics_type</th>\n",
+ " <th>training_metrics_final</th>\n",
+ " <th>training_loss_final</th>\n",
+ " <th>training_metrics</th>\n",
+ " <th>training_loss</th>\n",
+ " <th>validation_metrics_final</th>\n",
+ " <th>validation_loss_final</th>\n",
+ " <th>validation_metrics</th>\n",
+ " <th>validation_loss</th>\n",
+ " <th>model_arch_table</th>\n",
+ " <th>num_iterations</th>\n",
+ " <th>start_training_time</th>\n",
+ " <th>end_training_time</th>\n",
+ " <th>s</th>\n",
+ " <th>i</th>\n",
+ " <th>run_id</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>45</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='sgd(lr=0.004501919010538727,momentum=0.9002808952996391)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=256,epochs=5</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>2159.70019531</td>\n",
+ " <td>[121.955986022949, 245.619317054749, 368.365077972412, 490.415205955505, 614.768485069275, 737.048167943954, 860.508330106735, 984.307431936264, 1106.31793498993, 1229.54079914093, 1352.66811394691, 1477.57317709923, 1599.99458003044, 1723.35215711594, 1847.86346912384, 1971.57312297821, 2096.37913298607, 2221.54790210724, 2346.08665895462, 2470.83494997025, 2595.6411960125, 2722.25887513161, 2846.48335313797, 2971.13271403313, 3097.49445009232, 3222.44972395897, 3348.5662779808]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.941940009594</td>\n",
+ " <td>0.169452220201</td>\n",
+ " <td>[0.574479997158051, 0.658760011196136, 0.695840001106262, 0.72733998298645, 0.733219981193542, 0.771200001239777, 0.778680026531219, 0.808700025081635, 0.809000015258789, 0.818579971790314, 0.835739970207214, 0.84799998998642, 0.853200018405914, 0.858900010585785, 0.872919976711273, 0.878780007362366, 0.88808000087738, 0.880240023136139, 0.894320011138916, 0.903779983520508, 0.912299990653992, 0.908439993858337, 0.919539988040924, 0.924639999866486, 0.929180026054382, 0.9375, 0.941940009593964]</td>\n",
+ " <td>[1.19219434261322, 0.959131419658661, 0.861107409000397, 0.770956337451935, 0.747268915176392, 0.64410811662674, 0.628470838069916, 0.539423823356628, 0.541868448257446, 0.514527797698975, 0.469026476144791, 0.432008743286133, 0.416983753442764, 0.402583330869675, 0.363078087568283, 0.346161216497421, 0.317243546247482, 0.340911239385605, 0.304346263408661, 0.274338334798813, 0.253901869058609, 0.262585163116455, 0.231020957231522, 0.218931555747986, 0.206650838255882, 0.184870630502701, 0.169452220201492]</td>\n",
+ " <td>0.816399991512</td>\n",
+ " <td>0.580716967583</td>\n",
+ " <td>[0.565699994564056, 0.641200006008148, 0.674899995326996, 0.704500019550323, 0.708000004291534, 0.740499973297119, 0.739799976348877, 0.766499996185303, 0.762099981307983, 0.76690000295639, 0.780900001525879, 0.785000026226044, 0.785300016403198, 0.79009997844696, 0.79449999332428, 0.795799970626831, 0.802600026130676, 0.792599976062775, 0.798399984836578, 0.807299971580505, 0.810500025749207, 0.801699995994568, 0.805400013923645, 0.811600029468536, 0.810100018978119, 0.813899993896484, 0.816399991512299]</td>\n",
+ " <td>[1.20952260494232, 1.00138294696808, 0.919946014881134, 0.846988558769226, 0.835236310958862, 0.748137712478638, 0.745132148265839, 0.670836567878723, 0.688502311706543, 0.673530399799347, 0.646275579929352, 0.626095473766327, 0.629233837127686, 0.623023450374603, 0.601795375347137, 0.603216171264648, 0.587353229522705, 0.635767936706543, 0.61867493391037, 0.594616591930389, 0.586753845214844, 0.60888147354126, 0.601007521152496, 0.593143999576569, 0.601291477680206, 0.583372294902802, 0.580716967582703]</td>\n",
+ " <td>model_arch_table_cifar10</td>\n",
+ " <td>27</td>\n",
+ " <td>2020-01-23 21:12:04.749779</td>\n",
+ " <td>2020-01-23 22:07:53.819497</td>\n",
+ " <td>0</td>\n",
+ " <td>3</td>\n",
+ " <td>65</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(45, 2, u\"loss='categorical_crossentropy',optimizer='sgd(lr=0.004501919010538727,momentum=0.9002808952996391)',metrics=['accuracy']\", u'batch_size=256,epochs=5', u'madlib_keras', 2159.70019531, [121.955986022949, 245.619317054749, 368.365077972412, 490.415205955505, 614.768485069275, 737.048167943954, 860.508330106735, 984.307431936264, 1106.31793498993, 1229.54079914093, 1352.66811394691, 1477.57317709923, 1599.99458003044, 1723.35215711594, 1847.86346912384, 1971.57312297821, 2096.37913298607, 2221.54790210724, 2346.08665895462, 2470.83494997025, 2595.6411960125, 2722.25887513161, 2846.48335313797, 2971.13271403313, 3097.49445009232, 3222.44972395897, 3348.5662779808], [u'accuracy'], 0.941940009594, 0.169452220201, [0.574479997158051, 0.658760011196136, 0.695840001106262, 0.72733998298645, 0.733219981193542, 0.771200001239777, 0.778680026531219, 0.808700025081635, 0.809000015258789, 0.818579971790314, 0.835739970207214, 0.84799998998642, 0.853200018405914, 0.858900010585785, 0.872919976711273, 0.878780007362366, 0.88808000087738, 0.880240023136139, 0.894320011138916, 0.903779983520508, 0.912299990653992, 0.908439993858337, 0.919539988040924, 0.924639999866486, 0.929180026054382, 0.9375, 0.941940009593964], [1.19219434261322, 0.959131419658661, 0.861107409000397, 0.770956337451935, 0.747268915176392, 0.64410811662674, 0.628470838069916, 0.539423823356628, 0.541868448257446, 0.514527797698975, 0.469026476144791, 0.432008743286133, 0.416983753442764, 0.402583330869675, 0.363078087568283, 0.346161216497421, 0.317243546247482, 0.340911239385605, 0.304346263408661, 0.274338334798813, 0.253901869058609, 0.262585163116455, 0.231020957231522, 0.218931555747986, 0.206650838255882, 0.184870630502701, 0.169452220201492], 0.816399991512, 0.580716967583, [0.565699994564056, 0.641200006008148, 0.674899995326996, 0.704500019550323, 0.708000004291534, 0.740499973297119, 0.739799976348877, 0.766499996185303, 0.762099981307983, 0.76690000295639, 0.780900001525879, 0.785000026226044, 0.785300016403198, 0.79009997844696, 0.79449999332428, 0.795799970626831, 0.802600026130676, 0.792599976062775, 0.798399984836578, 0.807299971580505, 0.810500025749207, 0.801699995994568, 0.805400013923645, 0.811600029468536, 0.810100018978119, 0.813899993896484, 0.816399991512299], [1.20952260494232, 1.00138294696808, 0.919946014881134, 0.846988558769226, 0.835236310958862, 0.748137712478638, 0.745132148265839, 0.670836567878723, 0.688502311706543, 0.673530399799347, 0.646275579929352, 0.626095473766327, 0.629233837127686, 0.623023450374603, 0.601795375347137, 0.603216171264648, 0.587353229522705, 0.635767936706543, 0.61867493391037, 0.594616591930389, 0.586753845214844, 0.60888147354126, 0.601007521152496, 0.593143999576569, 0.601291477680206, 0.583372294902802, 0.580716967582703], u'model_arch_table_cifar10', 27, datetime.datetime(2020, 1, 23, 21, 12, 4, 749779), datetime.datetime(2020, 1, 23, 22, 7, 53, 819497), 0, 3, 65)]"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%sql SELECT * FROM $results_table ORDER BY validation_loss_final ASC LIMIT 1;"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib notebook\n",
+ "import matplotlib.pyplot as plt\n",
+ "from matplotlib.ticker import MaxNLocator\n",
+ "from collections import defaultdict\n",
+ "import pandas as pd\n",
+ "import seaborn as sns\n",
+ "sns.set_palette(sns.color_palette(\"hls\", 20))\n",
+ "plt.rcParams.update({'font.size': 12})\n",
+ "pd.set_option('display.max_colwidth', -1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Training dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "65 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
+ " if (typeof(WebSocket) !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support.' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
+ "\n",
+ " this.ws = websocket;\n",
+ "\n",
+ " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ "\n",
+ " if (!this.supports_binary) {\n",
+ " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
+ "\n",
+ " this.context = undefined;\n",
+ " this.message = undefined;\n",
+ " this.canvas = undefined;\n",
+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
+ " if (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+ " }\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " }\n",
+ "\n",
+ " this.imageObj.onload = function() {\n",
+ " if (fig.image_mode == 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onunload = function() {\n",
+ " fig.ws.close();\n",
+ " }\n",
+ "\n",
+ " this.ws.onmessage = this._make_on_message_function(this);\n",
+ "\n",
+ " this.ondownload = ondownload;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option)\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width, fig.canvas.height);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var width = this.canvas.width/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " event.shiftKey = false;\n",
+ " // Send a \"J\" for go to next cell\n",
+ " event.which = 74;\n",
+ " event.keyCode = 74;\n",
+ " manager.command_mode();\n",
+ " manager.handle_keydown(event);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i<ncells; i++) {\n",
+ " var cell = cells[i];\n",
+ " if (cell.cell_type === 'code'){\n",
+ " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " var data = cell.output_area.outputs[j];\n",
+ " if (data.data) {\n",
+ " // IPython >= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
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+ ]
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\" width=\"1000\">"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ }
+ ],
+ "source": [
+ "#df_results = %sql SELECT * FROM $results_table ORDER BY run_id;\n",
+ "df_results = %sql SELECT * FROM $results_table ORDER BY training_loss_final ASC LIMIT 100;\n",
+ "df_results = df_results.DataFrame()\n",
+ "\n",
+ "#set up plots\n",
+ "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(10,5))\n",
+ "fig.legend(ncol=4)\n",
+ "fig.tight_layout()\n",
+ "\n",
+ "ax_metric = axs[0]\n",
+ "ax_loss = axs[1]\n",
+ "\n",
+ "ax_metric.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
+ "ax_metric.set_xlabel('Iteration')\n",
+ "ax_metric.set_ylabel('Metric')\n",
+ "ax_metric.set_title('Training metric curve')\n",
+ "\n",
+ "ax_loss.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
+ "ax_loss.set_xlabel('Iteration')\n",
+ "ax_loss.set_ylabel('Loss')\n",
+ "ax_loss.set_title('Training loss curve')\n",
+ "\n",
+ "for run_id in df_results['run_id']:\n",
+ " df_output_info = %sql SELECT training_metrics,training_loss FROM $results_table WHERE run_id = $run_id\n",
+ " df_output_info = df_output_info.DataFrame()\n",
+ " training_metrics = df_output_info['training_metrics'][0]\n",
+ " training_loss = df_output_info['training_loss'][0]\n",
+ " X = range(len(training_metrics))\n",
+ " \n",
+ " ax_metric.plot(X, training_metrics, label=run_id, marker='o')\n",
+ " ax_loss.plot(X, training_loss, label=run_id, marker='o')\n",
+ "\n",
+ "# fig.savefig('./lc_keras_fit.png', dpi = 300)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Validation dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "65 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
+ " if (typeof(WebSocket) !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support.' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
+ "\n",
+ " this.ws = websocket;\n",
+ "\n",
+ " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ "\n",
+ " if (!this.supports_binary) {\n",
+ " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
+ "\n",
+ " this.context = undefined;\n",
+ " this.message = undefined;\n",
+ " this.canvas = undefined;\n",
+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
+ " if (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+ " }\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " }\n",
+ "\n",
+ " this.imageObj.onload = function() {\n",
+ " if (fig.image_mode == 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onunload = function() {\n",
+ " fig.ws.close();\n",
+ " }\n",
+ "\n",
+ " this.ws.onmessage = this._make_on_message_function(this);\n",
+ "\n",
+ " this.ondownload = ondownload;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option)\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width, fig.canvas.height);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var width = this.canvas.width/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " event.shiftKey = false;\n",
+ " // Send a \"J\" for go to next cell\n",
+ " event.which = 74;\n",
+ " event.keyCode = 74;\n",
+ " manager.command_mode();\n",
+ " manager.handle_keydown(event);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i<ncells; i++) {\n",
+ " var cell = cells[i];\n",
+ " if (cell.cell_type === 'code'){\n",
+ " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " var data = cell.output_area.outputs[j];\n",
+ " if (data.data) {\n",
+ " // IPython >= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
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+ },
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+ "output_type": "display_data"
+ },
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\" width=\"1000\">"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n",
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+ "1 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ }
+ ],
+ "source": [
+ "#df_results = %sql SELECT * FROM $results_table ORDER BY run_id;\n",
+ "df_results = %sql SELECT * FROM $results_table ORDER BY validation_metrics_final DESC LIMIT 100;\n",
+ "df_results = df_results.DataFrame()\n",
+ "\n",
+ "#set up plots\n",
+ "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(10,5))\n",
+ "fig.legend(ncol=4)\n",
+ "fig.tight_layout()\n",
+ "\n",
+ "ax_metric = axs[0]\n",
+ "ax_loss = axs[1]\n",
+ "\n",
+ "ax_metric.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
+ "ax_metric.set_xlabel('Iteration')\n",
+ "ax_metric.set_ylabel('Metric')\n",
+ "ax_metric.set_title('Validation metric curve')\n",
+ "\n",
+ "ax_loss.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
+ "ax_loss.set_xlabel('Iteration')\n",
+ "ax_loss.set_ylabel('Loss')\n",
+ "ax_loss.set_title('Validation loss curve')\n",
+ "\n",
+ "for run_id in df_results['run_id']:\n",
+ " df_output_info = %sql SELECT validation_metrics,validation_loss FROM $results_table WHERE run_id = $run_id\n",
+ " df_output_info = df_output_info.DataFrame()\n",
+ " validation_metrics = df_output_info['validation_metrics'][0]\n",
+ " validation_loss = df_output_info['validation_loss'][0]\n",
+ " X = range(len(validation_metrics))\n",
+ " \n",
+ " ax_metric.plot(X, validation_metrics, label=run_id, marker='o')\n",
+ " ax_loss.plot(X, validation_loss, label=run_id, marker='o')\n",
+ "\n",
+ "# fig.savefig('./lc_keras_fit.png', dpi = 300)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"print\"></a>\n",
+ "# 6. Print run schedules (display only)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Pretty print reg Hyperband run schedule"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "max_iter = 27\n",
+ "eta = 3\n",
+ "B = 4*max_iter = 108\n",
+ "skip_last = 0\n",
+ " \n",
+ "s=3\n",
+ "n_i r_i\n",
+ "------------\n",
+ "27 1.0\n",
+ "9.0 3.0\n",
+ "3.0 9.0\n",
+ "1.0 27.0\n",
+ " \n",
+ "s=2\n",
+ "n_i r_i\n",
+ "------------\n",
+ "9 3.0\n",
+ "3.0 9.0\n",
+ "1.0 27.0\n",
+ " \n",
+ "s=1\n",
+ "n_i r_i\n",
+ "------------\n",
+ "6 9.0\n",
+ "2.0 27.0\n",
+ " \n",
+ "s=0\n",
+ "n_i r_i\n",
+ "------------\n",
+ "4 27\n",
+ " \n",
+ "sum of configurations at leaf nodes across all s = 8.0\n",
+ "(if have more workers than this, they may not be 100% busy)\n"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "from math import log, ceil\n",
+ "\n",
+ "#input\n",
+ "max_iter = 27 # maximum iterations/epochs per configuration\n",
+ "eta = 3 # defines downsampling rate (default=3)\n",
+ "skip_last = 0 # 1 means skip last run in each bracket, 0 means run full bracket\n",
+ "\n",
+ "logeta = lambda x: log(x)/log(eta)\n",
+ "s_max = int(logeta(max_iter)) # number of unique executions of Successive Halving (minus one)\n",
+ "B = (s_max+1)*max_iter # total number of iterations (without reuse) per execution of Succesive Halving (n,r)\n",
+ "\n",
+ "#echo output\n",
+ "print (\"max_iter = \" + str(max_iter))\n",
+ "print (\"eta = \" + str(eta))\n",
+ "print (\"B = \" + str(s_max+1) + \"*max_iter = \" + str(B))\n",
+ "print (\"skip_last = \" + str(skip_last))\n",
+ "\n",
+ "sum_leaf_n_i = 0 # count configurations at leaf nodes across all s\n",
+ "\n",
+ "#### Begin Finite Horizon Hyperband outlerloop. Repeat indefinitely.\n",
+ "for s in reversed(range(s_max+1)):\n",
+ " \n",
+ " print (\" \")\n",
+ " print (\"s=\" + str(s))\n",
+ " print (\"n_i r_i\")\n",
+ " print (\"------------\")\n",
+ " counter = 0\n",
+ " \n",
+ " n = int(ceil(int(B/max_iter/(s+1))*eta**s)) # initial number of configurations\n",
+ " r = max_iter*eta**(-s) # initial number of iterations to run configurations for\n",
+ "\n",
+ " #### Begin Finite Horizon Successive Halving with (n,r)\n",
+ " #T = [ get_random_hyperparameter_configuration() for i in range(n) ] \n",
+ " for i in range((s+1) - int(skip_last)):\n",
+ " # Run each of the n_i configs for r_i iterations and keep best n_i/eta\n",
+ " n_i = n*eta**(-i)\n",
+ " r_i = r*eta**(i)\n",
+ " \n",
+ " print (str(n_i) + \" \" + str (r_i))\n",
+ " \n",
+ " # check if leaf node for this s\n",
+ " if counter == (s-skip_last):\n",
+ " sum_leaf_n_i += n_i\n",
+ " counter += 1\n",
+ " \n",
+ " #val_losses = [ run_then_return_val_loss(num_iters=r_i,hyperparameters=t) for t in T ]\n",
+ " #T = [ T[i] for i in argsort(val_losses)[0:int( n_i/eta )] ]\n",
+ " #### End Finite Horizon Successive Halving with (n,r)\n",
+ "\n",
+ "print (\" \")\n",
+ "print (\"sum of configurations at leaf nodes across all s = \" + str(sum_leaf_n_i))\n",
+ "print (\"(if have more workers than this, they may not be 100% busy)\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Pretty print Hyperband diagonal run schedule"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "from math import log, ceil\n",
+ "\n",
+ "#input\n",
+ "max_iter = 27 # maximum iterations/epochs per configuration\n",
+ "eta = 3 # defines downsampling rate (default=3)\n",
+ "skip_last = 1 # 1 means skip last run in each bracket, 0 means run full bracket\n",
+ "\n",
+ "logeta = lambda x: log(x)/log(eta)\n",
+ "s_max = int(logeta(max_iter)) # number of unique executions of Successive Halving (minus one)\n",
+ "B = (s_max+1)*max_iter # total number of iterations (without reuse) per execution of Succesive Halving (n,r)\n",
+ "\n",
+ "#echo output\n",
+ "print (\"echo input:\")\n",
+ "print (\"max_iter = \" + str(max_iter))\n",
+ "print (\"eta = \" + str(eta))\n",
+ "print (\"s_max = \" + str(s_max))\n",
+ "print (\"B = \" + str(s_max+1) + \"*max_iter = \" + str(B))\n",
+ "\n",
+ "print (\" \")\n",
+ "print (\"initial n, r values for each s:\")\n",
+ "initial_n_vals = {}\n",
+ "initial_r_vals = {}\n",
+ "# get hyper parameter configs for each s\n",
+ "for s in reversed(range(s_max+1)):\n",
+ " \n",
+ " n = int(ceil(int(B/max_iter/(s+1))*eta**s)) # initial number of configurations\n",
+ " r = max_iter*eta**(-s) # initial number of iterations to run configurations for\n",
+ " \n",
+ " initial_n_vals[s] = n \n",
+ " initial_r_vals[s] = r \n",
+ " \n",
+ " print (\"s=\" + str(s))\n",
+ " print (\"n=\" + str(n))\n",
+ " print (\"r=\" + str(r))\n",
+ " print (\" \")\n",
+ " \n",
+ "print (\"outer loop on diagonal:\")\n",
+ "# outer loop on diagonal\n",
+ "for i in range((s_max+1) - int(skip_last)):\n",
+ " print (\" \")\n",
+ " print (\"i=\" + str(i))\n",
+ " \n",
+ " print (\"inner loop on s desc:\")\n",
+ " # inner loop on s desc\n",
+ " for s in range(s_max, s_max-i-1, -1):\n",
+ " n_i = initial_n_vals[s]*eta**(-i+s_max-s)\n",
+ " r_i = initial_r_vals[s]*eta**(i-s_max+s)\n",
+ " \n",
+ " print (\"s=\" + str(s))\n",
+ " print (\"n_i=\" + str(n_i))\n",
+ " print (\"r_i=\" + str(r_i))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"predict\"></a>\n",
+ "# 7. Inference\n",
+ "\n",
+ "Use the best model from the last run.\n",
+ "\n",
+ "## 7a. Run predict on the whole validation dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 93,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>mst_key</th>\n",
+ " <th>model_id</th>\n",
+ " <th>compile_params</th>\n",
+ " <th>fit_params</th>\n",
+ " <th>model_type</th>\n",
+ " <th>model_size</th>\n",
+ " <th>metrics_elapsed_time</th>\n",
+ " <th>metrics_type</th>\n",
+ " <th>training_metrics_final</th>\n",
+ " <th>training_loss_final</th>\n",
+ " <th>training_metrics</th>\n",
+ " <th>training_loss</th>\n",
+ " <th>validation_metrics_final</th>\n",
+ " <th>validation_loss_final</th>\n",
+ " <th>validation_metrics</th>\n",
+ " <th>validation_loss</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>6</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='adam(lr=0.002826545217978097)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=128,epochs=5</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>2159.70019531</td>\n",
+ " <td>[156.498700857162, 314.38369679451, 471.076618909836]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>0.89631998539</td>\n",
+ " <td>0.301868826151</td>\n",
+ " <td>[0.817480027675629, 0.862479984760284, 0.896319985389709]</td>\n",
+ " <td>[0.536632478237152, 0.400230169296265, 0.301868826150894]</td>\n",
+ " <td>0.805899977684</td>\n",
+ " <td>0.613121390343</td>\n",
+ " <td>[0.764500021934509, 0.788500010967255, 0.805899977684021]</td>\n",
+ " <td>[0.717438697814941, 0.662977695465088, 0.613121390342712]</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(6, 2, u\"loss='categorical_crossentropy',optimizer='adam(lr=0.002826545217978097)',metrics=['accuracy']\", u'batch_size=128,epochs=5', u'madlib_keras', 2159.70019531, [156.498700857162, 314.38369679451, 471.076618909836], [u'accuracy'], 0.89631998539, 0.301868826151, [0.817480027675629, 0.862479984760284, 0.896319985389709], [0.536632478237152, 0.400230169296265, 0.301868826150894], 0.805899977684, 0.613121390343, [0.764500021934509, 0.788500010967255, 0.805899977684021], [0.717438697814941, 0.662977695465088, 0.613121390342712])]"
+ ]
+ },
+ "execution_count": 93,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%sql SELECT * FROM $best_model_info;"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 94,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ }
+ ],
+ "source": [
+ "best_mst_key = %sql SELECT mst_key FROM $best_model_info; \n",
+ "best_mst_key = best_mst_key.DataFrame().to_numpy()[0][0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 95,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "5 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>id</th>\n",
+ " <th>estimated_y</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>4</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>5</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(1, 0), (2, 0), (3, 0), (4, 0), (5, 0)]"
+ ]
+ },
+ "execution_count": 95,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%sql DROP TABLE IF EXISTS cifar10_val_predict;\n",
+ "%sql SELECT madlib.madlib_keras_predict('cifar10_best_model', 'cifar10_val', 'id', 'x', 'cifar10_val_predict', 'response', True, $best_mst_key);\n",
+ "%sql SELECT * FROM cifar10_val_predict ORDER BY id LIMIT 5;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Count missclassifications"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 96,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1941</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(1941L,)]"
+ ]
+ },
+ "execution_count": 96,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT COUNT(*) FROM cifar10_val_predict JOIN cifar10_val USING (id) \n",
+ "WHERE cifar10_val_predict.estimated_y != cifar10_val.y;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Accuracy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 97,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>test_accuracy_percent</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>80.59</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(Decimal('80.59'),)]"
+ ]
+ },
+ "execution_count": 97,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT round(count(*)*100.0/10000.0,2) as test_accuracy_percent from\n",
+ " (select cifar10_val.y as actual, cifar10_val_predict.estimated_y as predicted\n",
+ " from cifar10_val_predict inner join cifar10_val\n",
+ " on cifar10_val.id=cifar10_val_predict.id) q\n",
+ "WHERE q.actual=q.predicted;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 7b. Select a random image from the validation dataset and run predict\n",
+ "\n",
+ "Label map"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 98,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "label_names = {\n",
+ " 0 :\"airplane\",\n",
+ " 1 :\"automobile\",\n",
+ " 2 :\"bird\",\n",
+ " 3:\"cat\",\n",
+ " 4 :\"deer\",\n",
+ " 5 :\"dog\",\n",
+ " 6 :\"frog\",\n",
+ " 7 :\"horse\",\n",
+ " 8 :\"ship\",\n",
+ " 9 :\"truck\"\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Pick a random image"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 99,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 99,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS cifar10_val_random;\n",
+ "CREATE TABLE cifar10_val_random AS\n",
+ " SELECT * FROM cifar10_val ORDER BY random() LIMIT 1;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Predict"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 100,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>id</th>\n",
+ " <th>prob_0</th>\n",
+ " <th>prob_1</th>\n",
+ " <th>prob_2</th>\n",
+ " <th>prob_3</th>\n",
+ " <th>prob_4</th>\n",
+ " <th>prob_5</th>\n",
+ " <th>prob_6</th>\n",
+ " <th>prob_7</th>\n",
+ " <th>prob_8</th>\n",
+ " <th>prob_9</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>9813</td>\n",
+ " <td>7.9166554e-08</td>\n",
+ " <td>0.00038159246</td>\n",
+ " <td>8.776156e-11</td>\n",
+ " <td>1.7702625e-08</td>\n",
+ " <td>1.2219187e-10</td>\n",
+ " <td>8.096258e-10</td>\n",
+ " <td>5.192042e-10</td>\n",
+ " <td>1.5758073e-09</td>\n",
+ " <td>4.106987e-07</td>\n",
+ " <td>0.99961793</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(9813, 7.9166554e-08, 0.00038159246, 8.776156e-11, 1.7702625e-08, 1.2219187e-10, 8.096258e-10, 5.192042e-10, 1.5758073e-09, 4.106987e-07, 0.99961793)]"
+ ]
+ },
+ "execution_count": 100,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%sql DROP TABLE IF EXISTS cifar10_val_random_predict;\n",
+ "%sql SELECT madlib.madlib_keras_predict('cifar10_best_model', 'cifar10_val_random', 'id', 'x', 'cifar10_val_random_predict', 'prob', True, $best_mst_key);\n",
+ "%sql SELECT * FROM cifar10_val_random_predict ;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Format output and display"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 101,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>feature_vector</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[7.9166554e-08, 0.00038159246, 8.776156e-11, 1.7702625e-08, 1.2219187e-10, 8.096258e-10, 5.192042e-10, 1.5758073e-09, 4.106987e-07, 0.99961793]</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[([7.9166554e-08, 0.00038159246, 8.776156e-11, 1.7702625e-08, 1.2219187e-10, 8.096258e-10, 5.192042e-10, 1.5758073e-09, 4.106987e-07, 0.99961793],)]"
+ ]
+ },
+ "execution_count": 101,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS cifar10_val_random_predict_array, cifar10_val_random_predict_array_summary;\n",
+ "SELECT madlib.cols2vec(\n",
+ " 'cifar10_val_random_predict',\n",
+ " 'cifar10_val_random_predict_array',\n",
+ " '*',\n",
+ " 'id'\n",
+ ");\n",
+ "select * from cifar10_val_random_predict_array;"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 102,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ " \n",
+ "truck 0.99961793\n",
+ "automobile 0.00038159246\n",
+ "ship 4.106987e-07\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "x = %sql SELECT x FROM cifar10_val_random;\n",
+ "x = x.DataFrame().to_numpy()\n",
+ "import numpy as np\n",
+ "from matplotlib.pyplot import imshow\n",
+ "%matplotlib inline\n",
+ "x_np = np.array(x[0][0], dtype=np.uint8)\n",
+ "imshow(x_np)\n",
+ "\n",
+ "x = %sql SELECT * FROM cifar10_val_random_predict_array;\n",
+ "x = x.DataFrame().to_numpy()\n",
+ "x = np.array(x[0][0])\n",
+ "top_3_prob_label_indices = x.argsort()[-3:][::-1]\n",
+ "print (\" \");\n",
+ "for index in top_3_prob_label_indices:\n",
+ " print (label_names[index], x[index])"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.16"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/community-artifacts/Deep-learning/automl/hyperband-diag-cifar10-v1.ipynb b/community-artifacts/Deep-learning/automl/hyperband-diag-cifar10-v1.ipynb
index 9f1a558..05a7143 100644
--- a/community-artifacts/Deep-learning/automl/hyperband-diag-cifar10-v1.ipynb
+++ b/community-artifacts/Deep-learning/automl/hyperband-diag-cifar10-v1.ipynb
@@ -6,9 +6,10 @@
"source": [
"# Hyperband diagonal using CIFAR-10\n",
"\n",
- "Implemention of Hyperband https://arxiv.org/pdf/1603.06560.pdf for MPP with a synchronous barrier. Uses the Hyperband schedule but runs it on a diagonal across brackets, instead of one bracket at a time, to be more efficient with cluster resources. \n",
+ "Implemention of Hyperband https://arxiv.org/pdf/1603.06560.pdf for MPP with a synchronous barrier. Uses the Hyperband schedule but runs it on a diagonal across brackets, instead of one bracket at a time, to be more efficient with cluster resources.\n",
"\n",
- "This is an initial implementation to show functionality; there is still work to do to improve usability.\n",
+ "The CIFAR-10 dataset consists of 60,000 32x32 colour images in 10 classes, with 6,000 images per class. There are 50,000 training images and 10,000 test images.\n",
+ "https://www.cs.toronto.edu/~kriz/cifar.html\n",
"\n",
"\n",
"## Table of contents \n",
@@ -34,36 +35,6 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "The CIFAR-10 dataset consists of 60,000 32x32 colour images in 10 classes, with 6,000 images per class. There are 50,000 training images and 10,000 test images.\n",
- "https://www.cs.toronto.edu/~kriz/cifar.html"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [
- {
- "data": {
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\n",
- "text/plain": [
- "<IPython.core.display.Image object>"
- ]
- },
- "execution_count": 2,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "from IPython.display import Image\n",
- "Image(\"../images/cifar10.jpg\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
"<a id=\"setup\"></a>\n",
"# 0. Setup"
]
@@ -74,18 +45,7 @@
"metadata": {
"scrolled": false
},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/fmcquillan/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated since IPython 4.0. You should import from traitlets.config instead.\n",
- " \"You should import from traitlets.config instead.\", ShimWarning)\n",
- "/Users/fmcquillan/anaconda/lib/python2.7/site-packages/IPython/utils/traitlets.py:5: UserWarning: IPython.utils.traitlets has moved to a top-level traitlets package.\n",
- " warn(\"IPython.utils.traitlets has moved to a top-level traitlets package.\")\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"%load_ext sql"
]
@@ -98,7 +58,7 @@
{
"data": {
"text/plain": [
- "u'Connected: gpadmin@cifar_demo'"
+ "u'Connected: fmcquillan@madlib'"
]
},
"execution_count": 2,
@@ -108,11 +68,11 @@
],
"source": [
"# Greenplum Database 5.x on GCP - via tunnel\n",
- "%sql postgresql://gpadmin@localhost:8000/cifar_demo\n",
+ "#%sql postgresql://gpadmin@localhost:8000/madlib\n",
"#%sql postgresql://gpadmin@35.230.53.21:5432/cifar_demo\n",
"\n",
"# PostgreSQL local\n",
- "#%sql postgresql://fmcquillan@localhost:5432/madlib"
+ "%sql postgresql://fmcquillan@localhost:5432/madlib"
]
},
{
@@ -124,6 +84,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
+ " * postgresql://fmcquillan@localhost:5432/madlib\n",
"1 rows affected.\n"
]
},
@@ -135,12 +96,12 @@
" <th>version</th>\n",
" </tr>\n",
" <tr>\n",
- " <td>MADlib version: 1.17-dev, git revision: rc/1.16-rc1-63-g7625ae0, cmake configuration time: Tue Jan 14 23:42:21 UTC 2020, build type: RelWithDebInfo, build system: Linux-2.6.32-754.6.3.el6.x86_64, C compiler: gcc 4.4.7, C++ compiler: g++ 4.4.7</td>\n",
+ " <td>MADlib version: 1.16, git revision: rc/1.16-rc1, cmake configuration time: Mon Jul 1 17:45:09 UTC 2019, build type: Release, build system: Darwin-16.7.0, C compiler: Clang, C++ compiler: Clang</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(u'MADlib version: 1.17-dev, git revision: rc/1.16-rc1-63-g7625ae0, cmake configuration time: Tue Jan 14 23:42:21 UTC 2020, build type: RelWithDebInfo, build system: Linux-2.6.32-754.6.3.el6.x86_64, C compiler: gcc 4.4.7, C++ compiler: g++ 4.4.7',)]"
+ "[(u'MADlib version: 1.16, git revision: rc/1.16-rc1, cmake configuration time: Mon Jul 1 17:45:09 UTC 2019, build type: Release, build system: Darwin-16.7.0, C compiler: Clang, C++ compiler: Clang',)]"
]
},
"execution_count": 3,
@@ -171,13 +132,6 @@
"text": [
"Using TensorFlow backend.\n"
]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Couldn't import dot_parser, loading of dot files will not be possible.\n"
- ]
}
],
"source": [
@@ -223,9 +177,11 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "PXF to load iage data\n",
+ "PXF can be used to load image data. \n",
"\n",
- "Alternatively just get the dataset from Keras in the usual way"
+ "For this demo, we will get the dataset from Keras and use the script called madlib_image_loader.py located at https://github.com/apache/madlib-site/tree/asf-site/community-artifacts/Deep-learning .\n",
+ "\n",
+ "If the script is not in the same folder as the notebook, you can use the following lines to import it."
]
},
{
@@ -234,50 +190,270 @@
"metadata": {},
"outputs": [],
"source": [
- "%%sql\n",
- "CREATE EXTERNAL TABLE cifar_external_batchsize_500 (\n",
- " fullpaths TEXT[], \n",
- " y TEXT[], \n",
- " names TEXT[], \n",
- " x INT[]\n",
- ") \n",
- "LOCATION ('pxf://madlib-datasets/cifar10/?\n",
- "PROFILE=gs:image&SERVER=gs-aa&BATCH_SIZE=500&STREAM_FRAGMENTS=true') FORMAT 'csv';\n",
+ "import sys\n",
+ "sys.path.insert(1, '/Users/fmcquillan/workspace/madlib-site/community-artifacts/Deep-learning')\n",
+ "from madlib_image_loader import ImageLoader, DbCredentials\n",
"\n",
- "CREATE TABLE cifar10_train AS SELECT * FROM cifar_external_batchsize_500;"
+ "# Specify database credentials, for connecting to db\n",
+ "db_creds = DbCredentials(user='gpadmin',\n",
+ " host='localhost',\n",
+ " port='8000',\n",
+ " password='')\n",
+ "\n",
+ "#db_creds = DbCredentials(user='fmcquillan',\n",
+ "# host='localhost',\n",
+ "# port='5432',\n",
+ "# password='')\n",
+ "\n",
+ "# Initialize ImageLoader (increase num_workers to run faster)\n",
+ "iloader = ImageLoader(num_workers=5, db_creds=db_creds)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Load the training and test data"
]
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "1 rows affected.\n"
+ " * postgresql://fmcquillan@localhost:5432/madlib\n",
+ "Done.\n"
]
},
{
"data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>count</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>50000</td>\n",
- " </tr>\n",
- "</table>"
- ],
"text/plain": [
- "[(50000L,)]"
+ "[]"
]
},
- "execution_count": 6,
+ "execution_count": 7,
"metadata": {},
"output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "MainProcess: Connected to madlib db.\n",
+ "Executing: CREATE TABLE cifar10_train (id SERIAL, x REAL[], y TEXT)\n",
+ "CREATE TABLE\n",
+ "Created table cifar10_train in madlib db\n",
+ "Spawning 5 workers...\n",
+ "Initializing PoolWorker-1 [pid 10828]\n",
+ "Initializing PoolWorker-2 [pid 10829]\n",
+ "PoolWorker-1: Created temporary directory /tmp/madlib_DaP40IOgzi\n",
+ "Initializing PoolWorker-3 [pid 10830]\n",
+ "PoolWorker-2: Created temporary directory /tmp/madlib_n5XjJvXs5s\n",
+ "PoolWorker-3: Created temporary directory /tmp/madlib_99mTsCxOFF\n",
+ "Initializing PoolWorker-4 [pid 10831]\n",
+ "PoolWorker-5: Connected to madlib db.\n",
+ "PoolWorker-4: Created temporary directory /tmp/madlib_zGujxaoQIb\n",
+ "Initializing PoolWorker-5 [pid 10832]\n",
+ "PoolWorker-1: Connected to madlib db.\n",
+ "PoolWorker-5: Created temporary directory /tmp/madlib_D6q8olnown\n",
+ "PoolWorker-2: Connected to madlib db.\n",
+ "PoolWorker-3: Connected to madlib db.\n",
+ "PoolWorker-4: Connected to madlib db.\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_DaP40IOgzi/cifar10_train0000.tmp\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_99mTsCxOFF/cifar10_train0000.tmp\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_n5XjJvXs5s/cifar10_train0000.tmp\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_zGujxaoQIb/cifar10_train0000.tmp\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_D6q8olnown/cifar10_train0000.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-3: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-2: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-4: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-5: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_DaP40IOgzi/cifar10_train0001.tmp\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_99mTsCxOFF/cifar10_train0001.tmp\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_n5XjJvXs5s/cifar10_train0001.tmp\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_zGujxaoQIb/cifar10_train0001.tmp\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_D6q8olnown/cifar10_train0001.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-3: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-2: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-4: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-5: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_DaP40IOgzi/cifar10_train0002.tmp\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_99mTsCxOFF/cifar10_train0002.tmp\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_n5XjJvXs5s/cifar10_train0002.tmp\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_zGujxaoQIb/cifar10_train0002.tmp\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_D6q8olnown/cifar10_train0002.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-3: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-2: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-4: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-5: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_DaP40IOgzi/cifar10_train0003.tmp\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_99mTsCxOFF/cifar10_train0003.tmp\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_n5XjJvXs5s/cifar10_train0003.tmp\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_zGujxaoQIb/cifar10_train0003.tmp\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_D6q8olnown/cifar10_train0003.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-3: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-2: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-4: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-5: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_DaP40IOgzi/cifar10_train0004.tmp\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_99mTsCxOFF/cifar10_train0004.tmp\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_n5XjJvXs5s/cifar10_train0004.tmp\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_zGujxaoQIb/cifar10_train0004.tmp\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_D6q8olnown/cifar10_train0004.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-3: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-2: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-4: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-5: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_DaP40IOgzi/cifar10_train0005.tmp\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_99mTsCxOFF/cifar10_train0005.tmp\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_n5XjJvXs5s/cifar10_train0005.tmp\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_zGujxaoQIb/cifar10_train0005.tmp\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_D6q8olnown/cifar10_train0005.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-3: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-2: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-4: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-5: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_DaP40IOgzi/cifar10_train0006.tmp\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_99mTsCxOFF/cifar10_train0006.tmp\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_n5XjJvXs5s/cifar10_train0006.tmp\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_zGujxaoQIb/cifar10_train0006.tmp\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_D6q8olnown/cifar10_train0006.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-3: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-2: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-4: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-5: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_DaP40IOgzi/cifar10_train0007.tmp\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_99mTsCxOFF/cifar10_train0007.tmp\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_n5XjJvXs5s/cifar10_train0007.tmp\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_zGujxaoQIb/cifar10_train0007.tmp\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_D6q8olnown/cifar10_train0007.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-3: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-2: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-4: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-5: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_DaP40IOgzi/cifar10_train0008.tmp\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_99mTsCxOFF/cifar10_train0008.tmp\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_n5XjJvXs5s/cifar10_train0008.tmp\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_zGujxaoQIb/cifar10_train0008.tmp\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_D6q8olnown/cifar10_train0008.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-3: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-2: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-4: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-5: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_DaP40IOgzi/cifar10_train0009.tmp\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_99mTsCxOFF/cifar10_train0009.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-3: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_DaP40IOgzi/cifar10_train0010.tmp\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_99mTsCxOFF/cifar10_train0010.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-3: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_DaP40IOgzi/cifar10_train0011.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar10_train\n",
+ "PoolWorker-4: Removed temporary directory /tmp/madlib_zGujxaoQIb\n",
+ "PoolWorker-5: Removed temporary directory /tmp/madlib_D6q8olnown\n",
+ "PoolWorker-2: Removed temporary directory /tmp/madlib_n5XjJvXs5s\n",
+ "PoolWorker-1: Removed temporary directory /tmp/madlib_DaP40IOgzi\n",
+ "PoolWorker-3: Removed temporary directory /tmp/madlib_99mTsCxOFF\n",
+ "Done! Loaded 50000 images in 19.7727279663s\n",
+ "5 workers terminated.\n",
+ "MainProcess: Connected to madlib db.\n",
+ "Executing: CREATE TABLE cifar10_val (id SERIAL, x REAL[], y TEXT)\n",
+ "CREATE TABLE\n",
+ "Created table cifar10_val in madlib db\n",
+ "Spawning 5 workers...\n",
+ "Initializing PoolWorker-6 [pid 10850]\n",
+ "PoolWorker-6: Created temporary directory /tmp/madlib_OqFarH4eVS\n",
+ "Initializing PoolWorker-7 [pid 10851]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "PoolWorker-7: Created temporary directory /tmp/madlib_BHhah9z53T\n",
+ "Initializing PoolWorker-8 [pid 10852]\n",
+ "PoolWorker-8: Created temporary directory /tmp/madlib_G5oLCmXwQN\n",
+ "Initializing PoolWorker-9 [pid 10853]\n",
+ "PoolWorker-6: Connected to madlib db.\n",
+ "PoolWorker-9: Created temporary directory /tmp/madlib_THDiiymnsM\n",
+ "Initializing PoolWorker-10 [pid 10854]\n",
+ "PoolWorker-7: Connected to madlib db.\n",
+ "PoolWorker-10: Created temporary directory /tmp/madlib_DLO1TEiyo6\n",
+ "PoolWorker-8: Connected to madlib db.\n",
+ "PoolWorker-9: Connected to madlib db.\n",
+ "PoolWorker-10: Connected to madlib db.\n",
+ "PoolWorker-6: Wrote 1000 images to /tmp/madlib_OqFarH4eVS/cifar10_val0000.tmp\n",
+ "PoolWorker-7: Wrote 1000 images to /tmp/madlib_BHhah9z53T/cifar10_val0000.tmp\n",
+ "PoolWorker-8: Wrote 1000 images to /tmp/madlib_G5oLCmXwQN/cifar10_val0000.tmp\n",
+ "PoolWorker-9: Wrote 1000 images to /tmp/madlib_THDiiymnsM/cifar10_val0000.tmp\n",
+ "PoolWorker-10: Wrote 1000 images to /tmp/madlib_DLO1TEiyo6/cifar10_val0000.tmp\n",
+ "PoolWorker-6: Loaded 1000 images into cifar10_val\n",
+ "PoolWorker-7: Loaded 1000 images into cifar10_val\n",
+ "PoolWorker-8: Loaded 1000 images into cifar10_val\n",
+ "PoolWorker-9: Loaded 1000 images into cifar10_val\n",
+ "PoolWorker-10: Loaded 1000 images into cifar10_val\n",
+ "PoolWorker-6: Wrote 1000 images to /tmp/madlib_OqFarH4eVS/cifar10_val0001.tmp\n",
+ "PoolWorker-7: Wrote 1000 images to /tmp/madlib_BHhah9z53T/cifar10_val0001.tmp\n",
+ "PoolWorker-8: Wrote 1000 images to /tmp/madlib_G5oLCmXwQN/cifar10_val0001.tmp\n",
+ "PoolWorker-9: Wrote 1000 images to /tmp/madlib_THDiiymnsM/cifar10_val0001.tmp\n",
+ "PoolWorker-10: Wrote 1000 images to /tmp/madlib_DLO1TEiyo6/cifar10_val0001.tmp\n",
+ "PoolWorker-6: Loaded 1000 images into cifar10_val\n",
+ "PoolWorker-7: Loaded 1000 images into cifar10_val\n",
+ "PoolWorker-8: Loaded 1000 images into cifar10_val\n",
+ "PoolWorker-9: Loaded 1000 images into cifar10_val\n",
+ "PoolWorker-10: Loaded 1000 images into cifar10_val\n",
+ "PoolWorker-8: Removed temporary directory /tmp/madlib_G5oLCmXwQN\n",
+ "PoolWorker-7: Removed temporary directory /tmp/madlib_BHhah9z53T\n",
+ "PoolWorker-10: Removed temporary directory /tmp/madlib_DLO1TEiyo6\n",
+ "PoolWorker-6: Removed temporary directory /tmp/madlib_OqFarH4eVS\n",
+ "PoolWorker-9: Removed temporary directory /tmp/madlib_THDiiymnsM\n",
+ "Done! Loaded 10000 images in 4.03977298737s\n",
+ "5 workers terminated.\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Load dataset into np array\n",
+ "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
+ "\n",
+ "%sql DROP TABLE IF EXISTS cifar10_train, cifar10_val;\n",
+ "\n",
+ "# Save images to temporary directories and load into database\n",
+ "iloader.load_dataset_from_np(x_train, y_train, 'cifar10_train', append=False)\n",
+ "iloader.load_dataset_from_np(x_test, y_test, 'cifar10_val', append=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * postgresql://gpadmin@localhost:8000/madlib\n",
+ "(psycopg2.errors.UndefinedTable) relation \"cifar_10_train_data\" does not exist\n",
+ "LINE 1: SELECT COUNT(*) FROM cifar_10_train_data;\n",
+ " ^\n",
+ "\n",
+ "[SQL: SELECT COUNT(*) FROM cifar_10_train_data;]\n",
+ "(Background on this error at: http://sqlalche.me/e/f405)\n"
+ ]
}
],
"source": [
@@ -474,6 +650,8 @@
"cell_type": "markdown",
"metadata": {},
"source": [
+ "Below are examples of setting up different distribution rules tables. You can customize this to your needs.\n",
+ "\n",
"Build distribution rules table for 4 VMs"
]
},
@@ -841,7 +1019,7 @@
" NULL, -- Buffer size\n",
" 256.0, -- Normalizing constant\n",
" NULL, -- Number of classes\n",
- " 'segments_to_use_4VMs' -- Distribution rules\n",
+ " 'gpu_segments' -- Distribution rules\n",
" );\n",
"\n",
"SELECT independent_var_shape, dependent_var_shape, buffer_id FROM cifar10_train_packed ORDER BY buffer_id;"
@@ -1051,7 +1229,7 @@
" 'x', -- Independent variable\n",
" 'cifar10_train_packed', -- From training preprocessor step\n",
" NULL, -- Buffer size\n",
- " 'segments_to_use_4VMs' -- Distribution rules\n",
+ " 'gpu_segments' -- Distribution rules\n",
" ); \n",
"\n",
"SELECT independent_var_shape, dependent_var_shape, buffer_id FROM cifar10_val_packed ORDER BY buffer_id;"
@@ -1122,7 +1300,9 @@
"<a id=\"arch\"></a>\n",
"# 3. Define and load model architectures\n",
"\n",
- "Model architecture from https://keras.io/examples/cifar10_cnn/"
+ "Here we load some example model architectures from published sources.\n",
+ "\n",
+ "a. Model architecture from https://keras.io/examples/cifar10_cnn/"
]
},
{
@@ -1245,7 +1425,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Model architecture from https://machinelearningmastery.com/how-to-develop-a-cnn-from-scratch-for-cifar-10-photo-classification/"
+ "b. Model architecture from https://machinelearningmastery.com/how-to-develop-a-cnn-from-scratch-for-cifar-10-photo-classification/"
]
},
{
@@ -1370,7 +1550,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Another model architecture from https://machinelearningmastery.com/how-to-develop-a-cnn-from-scratch-for-cifar-10-photo-classification/"
+ "c. Another model architecture from https://machinelearningmastery.com/how-to-develop-a-cnn-from-scratch-for-cifar-10-photo-classification/"
]
},
{
@@ -1531,7 +1711,7 @@
"<a id=\"hyperband\"></a>\n",
"# 4. Hyperband diagonal\n",
"\n",
- "Create tables"
+ "Create tables for intermediate and overall results from Hyperband, which is running on top of MADlib model selection methods."
]
},
{
@@ -1593,16 +1773,17 @@
" num_iterations INTEGER, \n",
" start_training_time TIMESTAMP, \n",
" end_training_time TIMESTAMP,\n",
- " s INTEGER, \n",
- " i INTEGER,\n",
- " run_id SERIAL\n",
+ " s INTEGER, -- bracket number from Hyperband\n",
+ " i INTEGER, -- iteration corresponding to successive having within a bracket\n",
+ " run_id SERIAL -- global counter for the training runs\n",
" );\n",
"\n",
- "-- model selection table\n",
+ "-- all model selections:\n",
+ "-- model selection table containing all model configs (all brackets)\n",
"DROP TABLE IF EXISTS mst_table_hb_cifar10;\n",
"CREATE TABLE mst_table_hb_cifar10 (\n",
" mst_key SERIAL, \n",
- " s INTEGER, -- bracket\n",
+ " s INTEGER, -- bracket\n",
" model_id INTEGER, \n",
" compile_params VARCHAR, \n",
" fit_params VARCHAR\n",
@@ -1613,17 +1794,18 @@
"CREATE TABLE mst_table_hb_cifar10_summary (model_arch_table VARCHAR);\n",
"INSERT INTO mst_table_hb_cifar10_summary VALUES ('model_arch_table_cifar10');\n",
"\n",
- "-- model selection table for diagonal\n",
+ "-- diagonal model selections:\n",
+ "-- model selection table for diagonal: fit() will be called on a per diagonal basis\n",
"DROP TABLE IF EXISTS mst_diag_table_hb_cifar10;\n",
"CREATE TABLE mst_diag_table_hb_cifar10 (\n",
" mst_key INTEGER, -- note not SERIAL since this table derived from main model selection table\n",
- " s INTEGER, -- bracket\n",
+ " s INTEGER, -- bracket\n",
" model_id INTEGER, \n",
" compile_params VARCHAR, \n",
" fit_params VARCHAR\n",
" );\n",
"\n",
- "-- model selection summary table for diagonal\n",
+ "-- model selection summary table for diagonal table\n",
"DROP TABLE IF EXISTS mst_diag_table_hb_cifar10_summary;\n",
"CREATE TABLE mst_diag_table_hb_cifar10_summary (model_arch_table VARCHAR);\n",
"INSERT INTO mst_diag_table_hb_cifar10_summary VALUES ('model_arch_table_cifar10');"
@@ -1670,8 +1852,26 @@
]
},
{
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Define variables for Hyperband"
+ ]
+ },
+ {
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "max_iter = 27 # maximum iterations per configuration\n",
+ "eta = 3 # defines downsampling rate (default = 3)\n",
+ "skip_last = 0 # 1 means skip last run in each bracket, 0 means run full bracket"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
@@ -1686,9 +1886,9 @@
" self.get_params = get_params_function #\n",
" self.try_params = try_params_function\n",
"\n",
- " self.max_iter = 27 # maximum iterations per configuration\n",
- " self.eta = 3 # defines downsampling rate (default = 3)\n",
- " self.skip_last = 0 # 1 means skip last run in each bracket, 0 means run full bracket\n",
+ " self.max_iter = max_iter \n",
+ " self.eta = eta \n",
+ " self.skip_last = skip_last \n",
"\n",
" self.logeta = lambda x: log( x ) / log( self.eta )\n",
" self.s_max = int( self.logeta( self.max_iter ))\n",
@@ -1710,7 +1910,6 @@
" def setup_full_schedule(self):\n",
" self.n_vals = np.zeros((self.s_max+1, self.s_max+1), dtype=int)\n",
" self.r_vals = np.zeros((self.s_max+1, self.s_max+1), dtype=int)\n",
- " sum_leaf_n_i = 0 # count configurations at leaf nodes across all s\n",
" \n",
" print (\" \")\n",
" print (\"Hyperband brackets\")\n",
@@ -1723,10 +1922,6 @@
" print (\"n_i r_i\")\n",
" print (\"------------\")\n",
"\n",
- " n = int(ceil(int(self.B/self.max_iter/(s+1))*self.eta**s)) # initial number of configurations\n",
- " r = self.max_iter*self.eta**(-s) # initial number of iterations to run configurations for\n",
- "\n",
- " #### Begin Finite Horizon Successive Halving with (n,r)\n",
" for i in range(s+1):\n",
" # n_i configs for r_i iterations\n",
" n_i = n*self.eta**(-i)\n",
@@ -1737,7 +1932,6 @@
"\n",
" print (str(n_i) + \" \" + str (r_i))\n",
" \n",
- " #### End Finite Horizon Successive Halving with (n,r)\n",
" \n",
" # generate model selection tuples for all brackets\n",
" def create_mst_superset(self):\n",
@@ -1876,7 +2070,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Generate params and insert into MST table. This version of get_params uses the same compile parameters for all optimizers, and the same compile/fit parameters for all model architectures. (This may be too restrictive in some cases.)"
+ "Generate params and insert into MST table. This version of get_params uses the same compile parameters for all optimizers, and the same compile/fit parameters for all model architectures. (This may be too restrictive in some cases.) -- Note 3/13: check SIGMOID paper runs which I think I may have addressed this to some extent"
]
},
{
@@ -5086,7 +5280,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
- "version": "2.7.10"
+ "version": "2.7.16"
}
},
"nbformat": 4,
