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apache / madlib-site / ac2c01e2a555798c3b4d7ed919e5438fca652c16 / . / community-artifacts / Deep-learning / MADlib-Keras-model-selection-CNN-cifar10-v1.ipynb
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{
"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"
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],
"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",
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" <td>10000</td>\n",
" </tr>\n",
"</table>"
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"text/plain": [
"[(10000L,)]"
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},
"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",
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" <td>device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0</td>\n",
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" <tr>\n",
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" <tr>\n",
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" <td>device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0</td>\n",
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" <tr>\n",
" <td>gpsix4</td>\n",
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" </tr>\n",
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" <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>"
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" (u'gpsix3', u'device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0'),\n",
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" (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": {
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" <tr>\n",
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" <th>content</th>\n",
" <th>role</th>\n",
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" <th>mode</th>\n",
" <th>status</th>\n",
" <th>port</th>\n",
" <th>hostname</th>\n",
" <th>address</th>\n",
" <th>datadir</th>\n",
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" <tr>\n",
" <td>1</td>\n",
" <td>-1</td>\n",
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" <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",
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" <tr>\n",
" <td>3</td>\n",
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" <td>10</td>\n",
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" <td>40002</td>\n",
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" <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",
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" (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",
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" <tr>\n",
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" <td>gpsix0</td>\n",
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" <td>gpsix2</td>\n",
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"</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",
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"</table>"
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"text/plain": [
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" (55, [3125, 32, 32, 3], [3125, 10], 3),\n",
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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": {
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"<table>\n",
" <tr>\n",
" <th>source_table</th>\n",
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" <th>dependent_vartype</th>\n",
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" <th>distribution_rules</th>\n",
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" </tr>\n",
" <tr>\n",
" <td>cifar10_train</td>\n",
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" <td>y</td>\n",
" <td>x</td>\n",
" <td>smallint</td>\n",
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"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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" <td>29</td>\n",
" <td>[625, 32, 32, 3]</td>\n",
" <td>[625, 10]</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <td>34</td>\n",
" <td>[625, 32, 32, 3]</td>\n",
" <td>[625, 10]</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>55</td>\n",
" <td>[625, 32, 32, 3]</td>\n",
" <td>[625, 10]</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>56</td>\n",
" <td>[625, 32, 32, 3]</td>\n",
" <td>[625, 10]</td>\n",
" <td>2</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[(0, [625, 32, 32, 3], [625, 10], 1),\n",
" (1, [625, 32, 32, 3], [625, 10], 4),\n",
" (2, [625, 32, 32, 3], [625, 10], 9),\n",
" (3, [625, 32, 32, 3], [625, 10], 7),\n",
" (4, [625, 32, 32, 3], [625, 10], 14),\n",
" (7, [625, 32, 32, 3], [625, 10], 11),\n",
" (9, [625, 32, 32, 3], [625, 10], 13),\n",
" (12, [625, 32, 32, 3], [625, 10], 15),\n",
" (14, [625, 32, 32, 3], [625, 10], 6),\n",
" (19, [625, 32, 32, 3], [625, 10], 12),\n",
" (27, [625, 32, 32, 3], [625, 10], 10),\n",
" (28, [625, 32, 32, 3], [625, 10], 5),\n",
" (29, [625, 32, 32, 3], [625, 10], 8),\n",
" (34, [625, 32, 32, 3], [625, 10], 0),\n",
" (55, [625, 32, 32, 3], [625, 10], 3),\n",
" (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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" (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",
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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",
" <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",
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" <td>[u'accuracy']</td>\n",
" <td>0.585380017757</td>\n",
" <td>1.2495957613</td>\n",
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" <td>0.591700017452</td>\n",
" <td>1.32029783726</td>\n",
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" </tr>\n",
"</table>"
],
"text/plain": [
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" (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": {
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" alert('Your browser does not have WebSocket support.' +\n",
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" 'Firefox 4 and 5 are also supported but you ' +\n",
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" 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>"
]
},
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"output_type": "display_data"
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\" 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"<IPython.core.display.HTML object>"
]
},
"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')"
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},
"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"
],
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"execution_count": 8,
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{
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"text": [
" * postgresql://gpadmin@localhost:8000/cifar_places\n",
"1 rows affected.\n"
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{
"data": {
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{
"name": "stdout",
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"text": [
" * postgresql://gpadmin@localhost:8000/cifar_places\n",
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{
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{
"name": "stdout",
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"text": [
" * postgresql://gpadmin@localhost:8000/cifar_places\n",
"1 rows affected.\n"
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{
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{
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"text": [
" * postgresql://gpadmin@localhost:8000/cifar_places\n",
"1 rows affected.\n"
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{
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{
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"text": [
" * postgresql://gpadmin@localhost:8000/cifar_places\n",
"1 rows affected.\n"
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{
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{
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"text": [
" * postgresql://gpadmin@localhost:8000/cifar_places\n",
"1 rows affected.\n"
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{
"data": {
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{
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"text": [
" * postgresql://gpadmin@localhost:8000/cifar_places\n",
"1 rows affected.\n"
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},
{
"data": {
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" * postgresql://gpadmin@localhost:8000/cifar_places\n",
"1 rows affected.\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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" * postgresql://gpadmin@localhost:8000/cifar_places\n",
"1 rows affected.\n"
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"1 rows affected.\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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" * postgresql://gpadmin@localhost:8000/cifar_places\n",
"1 rows affected.\n"
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"1 rows affected.\n"
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"[<matplotlib.lines.Line2D at 0x156baca50>]"
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"data": {
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],
"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": {
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"\n",
"\n",
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" 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",
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"\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",
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" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
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"\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>"
]
},
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"output_type": "display_data"
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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",
"1 rows affected.\n"
]
},
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x156ccc1d0>]"
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"1 rows affected.\n"
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],
"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": {
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" } 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",
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" 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",
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"\n",
" this.image_mode = 'full';\n",
"\n",
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" 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",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
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"\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",
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"\n",
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"\n",
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" },\n",
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" },\n",
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" pass_mouse_events = true;\n",
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" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
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"\n",
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"\n",
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"\n",
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" } else {\n",
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" mouse_event_fn(event);\n",
" });\n",
"\n",
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"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
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" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
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" // 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",
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"\n",
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" }\n",
"\n",
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"\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",
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"\n",
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" // 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",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
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"\n",
" for (var ind in mpl.extensions) {\n",
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" }\n",
"\n",
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" 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>"
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},
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CUTDP/HmUVyBg21Ew1cJNlYlFTjCfba7FTh8fRfvPpS2wMF70zej/0Dhn3k3GCnKiFH6if5ysARiXhoCB+/t1o9lXSZ7pyjE5z6jROkjMbqInBm1wugOFREISGDWrFkvZGVlXdaiRYuwtlivXLmy4Z49ez7t0qULkwYHLBI4NPlMIsA8Dc7eQO+cGG5t9JeDgxEEFDb85Vdgu1Sd+YfZTTQAVwa4JYICCldauJc2UGGUABV9CiFU0ymisERS4Ah3iwojEbg6wQgTijW+IYVOzoRICRzOakWw/BXBtqiE6wQFGgtux+BJInx4hyrMaUGuTkJMhhLTGeHLesg9f3bocTj1naNWA53iQmGCzk24Agf331K4oirO/cG+K088JpgOiK/AEW5/HZ5Oe9zOxaRrFBGZO4VRTeEkgg01PvpcBERABMqCQDT8E+9+OFtUmCuCq+u+xd8WlUAcIuGrBGMciwKHE/3inSvDu4+htqhwKye3dPoWZ4uKb+4O1nN8KiYo58IMr6cdnEuuVqztm4UjcDiRKPStGF3pr/jbouKvXiA/1l9dJjBl7rx77ASqTkRzWfyu6p4xQGDWrFlvVq1a9cKmTZs6C8CurF61alWjnTt3jujatSsTMQcsEjhc4VSlUiTg7FvkSzZXg4PlleAfcu+jKf0JHE4CSSYLZdJQ78IXfuYJoOPgRuDgtffZew59j1P1h8h5eeWqPpNiskRS4GB7fGDy9I+BALhqHqw49gTaNuAcoeorcDjXMVkVmfsrwZKMUkhhhIRvklEnA3ygJKOREjjoLDIkk0eaemc69+0HV1Q456gK8/QWFuaWoDgWLI+Idzvh1nfmA0/iYT4U3/nuJMENV+Cgc8H9vTzmmPt9fYsT0eIrcIRrv9MuV3O4zYcOkXPyDROMMju9igiIgAjEA4FI+yfeTEIlGWUicb4kB8ut4N1eSX2VYONV2gKH82z2zZ/l2BgqEpL1QiUZdU4GCZRklBGvfHn3Llw0oV/DJJ30Z+hDeRcurvA5zIUTbg1mRKZvrio3vxfhCByhkoxS9OBCIp/ZgY6x9bbJnx8byGa+UzJZPI/KZUQsk52qiIBfAhI4NDESjYBz5Cb3SfKlkufFM7TPu/BFkEoxX+q5Ou0UfwKH88LGo1L5AOLLNAvPVx/llaDRW+Dg+eBMBsVjTL23ofCPNrdw8GHKBx0feHyhYwJN5kLwzXr+hG0nV8UZxscSaYHDyaLOF2Qex8qHtHehrXyx5wo+H8KMZGD+CfbRyfjNhxJ5OgmyfAUO9pFsGfnBcfF3rFmgEFLHISQ3bv1w+HMMyZfiTKBjYiMlcDhhoTw2jSspgYrjQHpHU3BVgs4AozpoJ52fYMfEhluftjD5KefFYFtM4c84JnRQnWzu4QocPMKOx+Rx37BvMlZG1vDEHd7DV+Aojv0OT/L1TjLq79jdRPt7pv6KgAjED4FI+ye+ZHiMaRdbyODqpCN4c4GAz3ZGXnofExtNXyXYqJW2wPEOAG7FvMU+ztXXNjcCh/cxsRfYOdKcduhnctGKUY/cTkI/ySlOpAV9FwoszHvGwmcscwBwPJh8lLmm/EVl8PhZR+hnVKPvaWNufjvCETjYnpP4m4tZ3HrsCA0UXJhMn9uuvfN3hevH8lnP42EZqeldnG3O5Egf223iezcMVCfOCEjgiLMBVXdcEeCJJAx154OeDww+PBjWxxdzKs78OdVn3xMk/AkcXL1mlANfzHkSCl92uU+QD0QmQuKWDT7svAUORns8b69+82WXL/YV7dMgKBJwpZohoFwd50OTAgIfXPyXNjAihEe3MgyRD0U+TJyEYJEWOAjUOf6U39MGvpCz38wDQpbeOUm4qs4HMqMqKErwwUeePEmGfeZLtr99rBSI+AAnLzJhf5nDgSfHsAQSOOgQ8jPuQ+W92Db50zHjw5afUfxxjohlW87DPBICB7PMUyBjf3lCCs+eD1RoD8eaKyDeL+fMdcLtHHxgOwlk2R6ZcfyZMI02OyXc+tz+4kTfcE7TaWA4LEUVrjoxoiRcgYO2cKXISSDGSBDuc+ScYNvcukWRzlfg4HXh2u/0m3OOq1Xkx98z5p5REQEREIF4IhBJ/8SXC30GChmMwGP+LiYrp+jsCBncIsvnhfMiHU1fJdiYuT1FhW3Q33ASrRf32d7PjoCg38FoDudUO/of9EPcCBy0hc9SRvJS3Cdb+mt8JvI5zraZoJORFt7FYU0xgwld6cPw/tzazGgI+oFcPPO3fYXtOFuD+T2f81yYC7c43LhowQieQIWn/jGKhM9gLirxGUyxgXOKYgM50Y9hLjYmQWV7LOH6sewz5yWjk+lvctGHfib9YrIlJwpGKiIQkIAEDk2ORCVAAYMr/oxQ4B9NCgssfEGlWMC8FjzO1Ps0DH8CB69hAi5GJ1Bo4Pdsg0k2+QL9AgCu3HsLHHwp5kOIIaP8nvfmQ4Ivg1Stue2F/8/Cl2IeEcYHB/OGMDKBL798SeWDmA9T760Z0RA4aAfvz9UNhhNWA8CIDopC7CdXEBwBgVyZP4R5Etg3JoTiigT5MGqFD0J/AgftZsQKH+R8QHL1wrtesCRgZETFn+NJh4B8+FDkNgkmVvOOiGBfiusE+ftdYQQEs5wzWoSrC6EKx/evfnK2UGxgH5h0lqsddBb4Ms+9rBSN+KD3LuHW5z2ZK4ZbmTgmZMsIDooNXL0qjsBBe+iw0W46WfxdoQBGp5D2MnLEn8DB68K13+k7o4K4jcltzpJQ46HPRUAERMA0ApH0T3z7xkUBis880YtiCl+8ucjDbZaMvPOOEoimrxKMubfAEWpsKHzzhbikz3ZGWdBvoQjEF3gWvqTT93ArcPAa+nV8JtJXqmy/5NPvoejPLcu+xeHNMeeLu+M7MbcUTw1h7hT6WsEKxSousjHBKIWVcIvjE4W6jiezOMfyMuKY9jJik1Ep9NkojjByg5GsTjQt2wzXj2WbjGbpaotx9B25YMgIJApBxeljqL7p8zgjIIEjzgZU3REBERCBOCVAZ5yJzZiglc6cr3AVp91Wt0RABERABOKUgCNwFDdnIUUNRjQywoPRkyoiIAJUw5RkVPNABERABEQgBgi8bSdR45Ypnv6iIgIiIAIiIAKxTKAkAgdFEUaPMnqYCUYZjakiAiIggUNzQAREQAREwGACDPOl48awYe735fYv5lvxPZbW4C7INBEQAREQARHwS6A4AgdzhjC3G3NbMHcZt77wBDd/CdqFXQQSksCsWbOeq1Sp0hUtW7bkFi5XpaioCCtWrGi0d+/ej7t27XprsIuKG3LlyhBVEgEREAERiGsCzGzPVSnuR2ZunNvt/eJx3Wl1TgREQAREICEIFEfgcHJmUOhnPi2+iHnnYksIcOqkCAQjMHv27BtTU1MfqlOnTm7NmjW5tTloKSwsTMrOzq6RnZ2dnJ+ff2uXLl14GEXAIoEjFFF9LgIiIAIiIAIiIAIiIAIiIAIiIAIiUGIC48ePT61YseIT6enpl2VmZqalpqYyka/fUlBQUJSXl4fc3NzdBQUFX+zdu3do3759vU9f/NN1EjhKPERqQAREQAREQAREQAREQAREQAREQAREwA0BihxZWVnXFhUV8aQfnuYTqBxISkrKLioq+qFr165z3LQtgcMNJdURAREQAREQAREQAREQAREQAREQAREwmoAEDqOHR8aJgAiIgAiIgAiIgAiIgAiIgAiIgAi4IZAoAkcagHYAtgIodANGdURABERABEQggQhw/2tNAAsB5CVQv8PpqnyJcGiprgiIgAiIQCIRMMaPSBSBg8c0udqzk0izUH0VAREQAREQAR8CPM5wrqj4JSBfQhNDBERABERABIITKHM/IlEEjvoA1s2ePRu1a9fWpBQBERABERABEfAisHnzZnTp0oU/aQBgveD4JSBfQhNDBERABERABPwQMMmPSBSBoy6ADRs2bEDduvxWRQREQAREQAREwCGwceNG1KtXj//L/2wUGb8E5EtoYoiACIiACIiAHwIm+RESODRFRUAEREAERCDBCZjkmBg8FBI4DB4cmSYCIiACIlB2BEzyIyRwlN080J1FQAREQAREwAgCJjkmRgDxb4QEDoMHR6aJgAiIgAiUHQGT/AgJHGU3D3RnERABERABETCCgEmOiRFAJHAYPAwyTQREQAREwDQCJvkREjhMmx2yRwREQAREQARKmYBJjkkpdz2c2ymCIxxaqisCIiACIpAwBEzyIyRwJMy0U0dFQAREQAREwD8BkxwTg8dIAofBgyPTREAEREAEyo6ASX6EBI6ymwe6swiIgAiIgAgYQcAkx8QIIP6NkMBh8ODINBEQgZITOHjwIPbs2YOcnBwUFRWVvEG1ENMEkpOTUbFiRVSuXBn8PlgxyY+QwBHT007Gi4AIiIAIiEDJCZjkmJS8N1FrQQJH1NCqYREQgbImsGPHDmzZsgVJSUnIyMgI+UJb1vbq/tEnkJ+fj9zcXGRlZTlHyQe8qUl+hASO6M8N3UEEREAEREAEjCZgkmNiMCgJHAYPjkwTAREoPgFGbqxZswaVKlVCnTp1kJKSUvzGdGXcEGAUz9atW7F9+3Y0atQI5cuXl8Bh0OjKKTFoMGSKCIiACIiAWQQkcLgaD/kSrjCpkgiIQKwRYOTGzp070aJFC4kbsTZ4Uba3sLAQK1assKI4KH4FKib5EYrgiPKkUPMiIAIiIAIiYDoBkxwTg1lJ4DB4cGSaCIhA8Qn88ccfKCgoQJMmTYrfiK6MWwKrVq1CWloaGjRoIIHDoFGWU2LQYMgUERABERABswhI4HA1HvIlXGFSJREQgVgjsHbtWstkbkNQEQFfAm7mh0l+hCI4NIdFQAREQAREIMEJmOSYGDwUEjgMHhyZJgIiUHwCbl5gi9+6rox1Am7mh0l+hASOWJ9xsl8EREAEREAESkjAJMekhF2J5uUSOKJJV22LgAiUGQE3L7BlZpxuXOYE3MwPk/wICRxlPmVkgAiIgAiIgAiULQGTHJOyJRH07hI4DB4cmSYCIlB8Am5eYIvfuq4sTQJDhgzB0KFDsXr1ajRu3Dgit3YzP0zyIyRwRGTY1YgIiIAIiIAIxC4BkxwTgylK4DB4cGSaCIhA8Qm4eYEtfutmXjls2DDMmTMHs2fPtsQA5h/hUbmByvTp03H//feD/yYlJaFnz5548skn0alTJ1cdZNvvvvsuLrjgAtfXuGrYp5IEDkACR3Fmjq4RAREQAREQgTgiYKjAcS+AzgC6AGBqf2bBC2c5qh6AKwGcCaAlgCwA9F6/A/AkgO1hDqEEjjCBqboIiEBsEEhEgYMiRbVq1dC5c2dL5OAxqIEEjmnTpuHkk09GvXr1MGjQIGtQX375ZWRnZ2PKlClo3759yIH+5Zdf0LdvX7zzzju4+uqrQ9YvboX8/HzwKyMjwxJiIlHczA+T/IjI9DoS5KLbhpyS6PJV6yIgAiIgAqVEoLCwCJN/34bjm1RHempyRO5qkmPi1aEiADsAzLFFjj1hChz/B+A/AEYD+BXAXgDdAdCz3Aygm/2vW4aR9yU2LwTKVQUqU4tREQEREIGyIeDmBbZsLIveXXn0adOmTa0btGvXDvv27QsocHTv3h1Lly7FkiVLLJGDZcOGDWjdujV69OiBn376KaSh4QocRUVF2L9/PypWrBiy7WhXcDM/TPIjJHBEe0aofREQAREQARGIAIHsvYfw+az1+N/MP7Bux0G8dnlnnNW+TgRaBkxyTLw6RM9zlf3/CwHQywsngqOtHaVBMcO7XA9gOIDnAAwOA2BkBY7/XQ4s/RY48Tbg9KFhmKGqIiACIhBZAm5eYCN7R7NaCyZwrFy5Ei1atMC1116Lt99++yjDr7vuOisig8/Q2rVrB+wUt6Zcc801f/q8T58+oPDhLX5Q1HjllVfw+++/49577wW3nMyYMQOvvvqqFS2yfv16pKSkoEOHDhg8eDAuvPDCo9r1t0XF+RlFmvfeew/vv/8+tm7dilatWoFbdc4+++ygAzj3FKUAACAASURBVOJmfpjkR0jgMOv3S9aIgAiIgAiIwGECjNaYtHIbPpn+B8Ys2YL8QgY1eMrfezfFvWe3jggtkxyTAB0qjsARiE0lAIwG+dHevuKWYWQFjvHDgAlPApXqALcvApJT3NqheiIgAiIQUQJuXmAjekPDGgsmcHzyySe47LLLMHz4cFx/PfXxI4U/u/HGG/Htt9/inHPOCdgrRotQHHniiSes+r169bLq1qpVC6effvphgaNjx47Yvn07brjhBkswadCgAc466yxL6Bg/fjxOO+00K1cI61CooGDx0UcfWfY5JZjAcfzxxyMtLQ39+/dHbm4uXnjhBWzbtg3Lly8PmpDUzfwwyY+QwGHYL5jMEQEREAEREIHsPYfw+ez1+GTGH1i/8+BhIFXLp6F/5/oY0L0hmh8TubBVkxyTUhA4qAotBvA+gKvCmG0RFTg2rVmCOu/28Nz+8i+BFqeFYYqqioAIiEDkCAR7gc0vKMSm3Ycid7MItlSnciZSU0q+VTOYwPHcc89ZkRLfffedJTZ4F/6MwsYbb7xhCRfBSrAtKs5nVatWtUSLY4455qimGNVRoUKFo3524MABHHfccVY0x+LFfKR5SjCBg7aOGjXqcG6OmTNngttv7rnnHiuSI1CRwBHBSRvBpiLqlETQLjUlAiIgAiIgAhYBJ1rj4+lrMWZJNgq8ojWOb1INlx3fEGe0rY3MtMiv9CeYwPEZgEsAnApgXJDpx0gPfjmlFvOBcN913bp0K0pW/vbGVNy+4Xb0SF4CtL0IuOSdkjWoq0VABESgmASCvcCu23EAvZ4eX8yWo3vZpLv6okG18iW+STCB49FHH8VDDz2EsWPH4pRTTjnqXuPGjcOpp56K559/HrfddltQO9wIHLfeeqsVVRGsUNg4ePAgmKPjwQcfxOuvv47du3dbSVJZggkczBXCiBHvUqlSJZxxxhn44osvAt5WAkeJp1hUGpDAERWsalQEREAERKCkBLYwWmPWOvxv5ro/RWtc3MUTrdGsZuSiNfzZm0ACx50AngXwJnf5hBi7IQAe9q0TKYHj4+l/YM7Il/Fs2hsoSslA0uBlnoSjKiIgAiJQygQkcAROMhpOBAcTlfLLu/CklvT09KPybPieouKIH8y9cdNNN/1p9HlaywMPPIBvvvnGOrnFt3D8GjZsGFLgYD6RZs2aHXV548aN0aRJE2sLTKAigaOUfyFd3k4Ch0tQqiYCIiACIhB9AozOmLRiq7UFxTdao0fTari0e0Oc2a42MlIjH62RwAIHN09T2OAxsczKlhdipKMawbHrQC76PP4tpqT+Hyok5QDnPg90vTb6k093EAEREAEfAtqiEljgCCcHhxM94Y2XwgGPmHUTweHvCFlGanArCk9wYYRH165dUblyZWtrCut//PHHWL169eEcGsEiOLzrOTZS4OAX7ZPAEVt/GiRwxNZ4yVoREAERiEsCjNb4bKYnWmPDriO5NapVSAejNf7WrUHUozUSVOCgcvAWAJ7ldz6AnGJMsIj7Eje8PwtnrBiKi1MmAvW7AdePKYZZukQEREAESkbAzQp9ye5g9tWROkWFyUT55V26dOkC5taYMGGCJXT4EzGCiR/z5s1Dp06drG0yQ4cefeLWgAED8Omnn0rg8JleSjJq9u+brBMBERABEYhxAozWmMhojel/YOzSo3NrnNC0Oi61cmvUKrVojQQUOBxxg+pBPwDFzZYXcYFj9PxN+PB/H+CT9Mc9w/LPmUDNljE+42W+CIhArBGQwBE4goNj2a1bNyxbtsxKAOrkYOLWTh6zyiSdY8aEFqdnz55tRV9wy8sdd9xx1BQJJnAsXLgQ7du3t/JtPPLII4ev488pnvA0FEVwHP0bJ4Ej1v4CyV4REAEREIGYIBAqWmNAtwZoGuXcGm5BxUEODm4+Zqa53322nlwN4G0A3Fx8HoAjYTNu4RypF3GB41BeAbo99hO+xyDUT9oGnHQ7cBpTf6iIgAiIQOkRSESB44MPPoDT75deeskSCu68k2maYB3FOnDgwMMDMGXKFPTt2xf169fHzTffbP2c12zZsgWTJ08Gj3cNVZgYlMfC8oQUnspSpUoV63smLg0mcBQUFFjtM3/GoEGDcOyxx1rHuvLklpYtW4LCiQQOCRyh5p8+FwEREAEREIFiEXCiNZhAcpxPtEbPZtWt3Bp/KeNoDX8dM1TgoHfZyLaXHmU6gOfs/18L4AOvvnDzcB8ATQCssX/OaI0RAPYAuMuPuMFMcF+HMdARFzh478Gfz0ODeS/g1tSvgEp1gNsXAcmlk3sljL6rqgiIQBwTSESBg9tFuG3EX+nTp8+fclJMnTrVSvQ5ffp065jVnj17Wkerdu7c2fXM4LGybIPHuubk5MC5TzCBg41zfCiKsB6PjOWWmnvvvRfcvsJtKxI4JHC4noSqKAIiIAIiIAJuCGzefQifzVqHT/3k1rjEzq1hSrRGDAkcjmjhz2R6pSeHEDj8noLidQ1FksZuxteuExWB49cV23Dff0diYsbtnttc8SXQ/LQwzFJVERABESgZgUQUOEpGLLGudjM/TFoo0RaVxJqf6q0IiIAIiECECFjRGsu34uMZsRWtEUMCR4RGKmLNREXg4Dw6YdhYvJRzP45PXgq06w9c/N+IGa2GREAERCAUATcvsKHa0OfxS8DN/JDAUfrjHxWnpPS7oTuKgAiIgAiUNQFGazBS49OZf2Dj7iP5KqvzJJSu9TGgW0M0qVGhrM0M6/4mOSZhGV66laPmSzz67WLsmfoOnkl7E0UpGUgavBwoV6V0e6e7iYAIJCwBNy+wCQtHHT+cq4S5SQIVk/wIRXBo0oqACIiACIhACAJcZZ+wPBsfT1+HcUu3oLDoyAUnNvfk1ji9TdmehFKSQTTJMSlJP6J8bdQEjgXrd2PAyz9jZsZNKJ+UA5z7AtD1mih3R82LgAiIgIeABA7NhGAE3MwPk/wICRyazyIgAiIgAiIQgMCm3Qfx2cz1AaM1Lu3WEI1jLFrDX1dNckwMnoxREziKiopw6r8n4J+7nkX/lElA/e7A9T8bjEKmiYAIxBMBNy+w8dRf9SU8Am7mh0l+hASO8MZXtUVABERABOKcwJFoDU9uDe9ojZOa1zgcrZGemhw3JExyTAyGGjWBg31+cewKTB07Ap+kP+5BMGgWUKOFwThkmgiIQLwQcPMCGy99VT/CJ+BmfpjkR0jgCH+MdYUIiIAIiEAcEmC0BnNrfDZz3VG5NWpUTMfFXRpgQLcGcRGt4W/oTHJMDJ5aURU41m7fj5OfGYdJGbehftI24KQ7gNMeNhiHTBMBEYgXAm5eYOOlr+pH+ATczA+T/AgJHOGPsa4QAREQARGIEwKM1vhlWTY+sU9CSYRoDQkcxZ68URU4aNWFr07GyRuH49bUEUClusDtC4HklGIbrAtFQAREwA0BNy+wbtpRnfgk4GZ+SOAo/bGPulNS+l3SHUVABERABIpLYMf+XHw4ba0lbGzyOgmF0RqXdPVEazSqHlsnoRSXBa8zyTEpST+ifG3UfYn3pqzB26PGYWLG7Z6uXPEV0PzUKHdLzYuACCQ6ATcvsInOKJH772Z+mORHKIIjkWer+i4CIiACCUaA2wDemrQan89eh0N5hYd736uFJ7fGaa1rIZ5ya7gdXpMcE7c2l0G9qAsc2/bl4PgnxuLj1KE4Pnkp0O5i4OK3y6CruqUIiEAiEXDzAptIPNTXowm4mR8m+RHhCBzMpnYrgL8DaAxgK4DPADwEYL+LiVARwC0ALrWvzwGwHMCbAN4D4HXoHt4FcFWANi8B8IWL+3lXibpTEqY9qi4CIiACIlCKBOb+sRNvTlyFHxZtRpH9tKmUmYrLjm+Iy7o3TKhoDX/YTXJMSnFahHurUvElrn5nBmqu/BzPpL0JpGYCg5cDmZXDtVX1RUAERMA1ATcvsK4bU8W4I+BmfpjkR4QjcPzHFihGAPgeQGsANwOYBOA0AEeWwv48rBRHJgDoaYsZ0wCUt8WO7gCeBnC312WOwDHQzwyZCOCPMGdOqTglYdqk6iIgAiIgAlEkUFhYhLFLs/HmxN8xc83Ow3eqV6Ucrj2pCf7WrQEqZqRG0YLYadokx8RgaqXiS3w9dwPu/3QqZmbchPJJOcB5/wG6XG0wFpkmAiIQ6wTcvMDGeh9lf/EJuJkfJvkRbgWOtgAWAKC40d8LDwWOFwFcDuDjINhOADAFwAsA7I2lVu10AEsBVANQxY/A4da+UCNWKk5JKCP0uQiIgAiIQPQJHMorwIi5GzB80iqs2nokwLBt3Szc2Lspzm5fB2kp8XPEaySImuSYRKI/UWqjVHyJ/Tn56PrYGDyGl9A/5VegwfHAdT9FqUtqVgREQAQANy+w4pS4BNzMD5P8CLcCwmMA7gfQ247YcEY4E8B2Ozrj7CDDfgaAHwDcBeAZn3ozANSzv5yPnAgOeqCVAOwLESESasaVilMSygh9LgIiIAIiED0CO+3Eoe9NXYNt+3IP36hPy5r4e++mOKFZdSQluX3sRc9OE1s2yTExkY9tU6n5Erf+by62zv8JH6c/4bn1oNlAjeYGo5FpIiACsUzAzQtsLPdPtpeMgJv5YZIf4dbT+9HehsJtJcyd4V0mA2gJoGYQdFUBrAKQD+AmANPtLSrMs0HR4/8ADPe63hE49toCBz1Vbk15wL423FEqNackXMNUXwREQAREoGQE/th+AG//ugqfzVqPg3kFVmNpKUno17EebujdBK1qZ5XsBglwtUmOicG4S82XGL80G9e+Ox2/ZtyKeknbgV53Aqcy5ZmKCIiACESegJsX2MjftWxbHDZsGObMmYPZs2dj9erVaNSoEdasWRPQqOnTp+P+++8H/+ViSc+ePfHkk0+iU6dOrjrCtt99911ccMEFrq9x1XCASrzXrl27cNttt5WkGetaN/PDJD/CrcDB7SnHAKjlhxATjTLxZwaAI0tmf67YC8BbthjifEoB40oAX/tUf9LevjLbTmDaEQBHh2f2MVJkTIiRYtQHv5xCu+ds2LABdevSP1ERAREQARGIdQLz1u2yEod+v3ATCp3EoRmpuKxHQ1zTswlqV2aQoYobAiY5Jm7sLaM6pSZw5BUUWqepXJ3zIW5J/RrIqgfctgBITimjruu2IiAC8UzAzQtsvPWfIkW1atXQuXNnS+TIysoKKHBMmzYNJ598MurVq4dBgwZZKF5++WVkZ2djypQpaN++fUg8v/zyC/r27Yt33nkHV18d/bxKtJeiSjDRJqTRdgU388MkP8KtwPE7F8QANPQD4n0ATAbKKI1dQUAdZ0dgMJKD+TiYd+OfAFoBOB/AzyEgtwDwG4CNAPh9sDIEwMO+FSRwuJ3GqicCIiACZhJg4tDxy7LxxsRVmLF6x2Ej61bOPJw4tFImH1cq4RAwyTEJx+5SrltqAgf79dA3CzFh2nRMyLjD082BI4Bmp5Ryl3U7ERCBRCDg5gU23jisWrUKTZs2tbrVrl077Nu3L6AY0L17dyxduhRLliyxRA4Wvle2bt0aPXr0wE8/hc6TJIGj9GaQW4GjpBEclLWYa4MJRl/36h63vCwEwFwbzQB4YosDl3cAUPI61j5iNlBNRXCU3hzSnURABEQg6gSYOPSb35g4dDVWZjMtk6e0rpNl5dc4p4MSh5ZkECRwuKJXqgLH7LU70f+1KfgsfSi6Jy8D2l8C9GcgrIoIiIAIRJZAIgoc3gSDCRwrV65EixYtcO211+Ltt98+Cvx1111nRWTwGVq7du2Ag8LtItdcc82fPu/Tpw8ofLAUFRXh9ddfx1tvvWUJKcnJyejWrRseeughK/LDu7z//vtWBMny5cuRl5eHWrVq4YQTTsALL7yAmjVronHjxoe3lXhfN378eCsSJdziZn6Y5Ee4FThKmoPjvwA4qjXspKTeXF9i+iwAzJ7FSJFghVEZjM440Y4CcTs+peqUuDVK9URABERABIIT2HUgFx9N/wPvTGbi0CMpoHq3rIkbezXFic2VODQSc8gkxyQS/YlSG6XqS9DZ7fPML+ixezSeThsOpGYCg5cDmZWj1D01KwIikKgE3LzAxjObYALHJ598gssuuwzDhw/H9ddffxQG/uzGG2/Et99+i3POOScgIkaLUBx54oknrPq9ejFzAyxh4vTTT7e+v+KKK8B7XXzxxdbnOTk5+OijjzB//nx89dVX6Nevn1Xvgw8+wJVXXmnVYd1y5cph3bp1+O677/Dee++hbdu2+Prrr3Hvvfdi27ZteP755w/bxXvxnuEWN/PDJD/CrcAR6hQVJgA9KwgsCiR/sXN4ZPvUe81OMsqtKstCAP/QPpLWjRji3VSpOiXhThrVFwEREAEROJrAuh1MHLoan81ahwO5nuC+1GQmDq2LG3o3tSI3VCJHwCTHJHK9inhLpe5LPPfTMrwzbj5mZd6ETKY5O+9FoAvzs6uIgAiIQOQIBH2BLcgH9myI3M0i2RLzE6WklrjFYALHc889h8GDB1sCwllnHf26y59R2HjjjTcs4SJYCbZFZcSIEbjooov+1E5+fr61BWb79u2gSMK8Iaw3ZswY7NixA6mpgfuuHByhpwW3mMwDMAJAf6/qNwN40c7BQfGBhVtNuAF6qVc9SkdMEno3gKe9fl4FwGIAzATHU1joxTKRKP895GMWc3hMs6M82oQ2+agape6UhGmfqouACIiACACYv96TOPS7BT6JQ49viKtPbIw6lcuJUxQISOBwBbXUfQluxzrt3xPw77RXcVHKr0CDHsB1XDNSEQEREIHIEQgqcOxcC/ynQ+RuFsmWbp0PVG1U4haDCRyPPvqotU1k7NixOOWUo/MgjRs3DqeeeqoVJRHqtJJgAkf//v3x888/g9thuDXFu7zyyisYMmQIli1bhpYtW1pbXRjF8eWXX1pRHRQ9/BUJHO6mhbOVhCLHd9z6DOAWADwmlqNdaDfD83U407xp8//n2IlIP7KvYZLRGwA0tpONvmpfz7N2vrdPVlnhdYrKtfY9GAnyqzuTD9cqdackTPtUXQREQAQSlgATh/6yPNsSNqatOpI4tHYWE4c2xoDuDZGlxKFRnR8SOFzhLRNf4tyXJqHypsn4KH2Yx8ib5wDVuZakIgIiIAKRISCBI3CS0XAiOJiolF/ehSe1pKenW7k2Ap2i0qZNGyvvRrAyceJEa1vKihUrcOaZZ1oRHdWrVwfzeDCy5G9/+xsqVTpyiKgEDne/GzybjFEYjL+hKLENwKdM9A3AeyT9CRy8A5/GrHuqvVXloH0qygsAvvIygRlangHQDQCdCS7XbQIwHgCf7t6RIe4s97SzQaeouMWleiIgAiIQfQI5+QX4Zu5GDJ+0Ciu8Eoe2ql0Jf+/TFOe0r4v01KNXMqJvVWLeQQKHq3EvE1/irUmr8MToRZiceSvqYDvQazBw6oOuDFYlERABEXBDQFtUAgsc4eTgYKTF0KFDj0LuJPYMJnDwNBZuQ/n4448DDleXLl1QtSoPLYWVn4MRJfyaMGGCdcxt3bp1QRGkWTOPAC6Bw83Mj+06ZeKUxDYyWS8CIiAC0SGw+0AePpy+Fu9OWYOte48kDu3VogZu6NUU/DdQyGV0LFKrEjhczYEy8SW27DmEHsPG4o6Uz3Bz6tcA95zftgBI5rqTigiIgAiUnICbJJIlv4u5LUTqFBVGVfDLuzjCBIUIig48deXqq3ko6JHCrSajR4/G7t27UbFixbBBOblAbrrpJnBLCwujRVavXh3w6NtwbuJmfpjkR7hNMhoOAxPrlolTYiII2SQCIiACZUVg/U5P4tBPZx6dOPS8jnVxfa8maFtXp0OU1diY5JiUFQMX9y0zX+Lyt6Zhw+8L8UvGnR4zB34NNDv62EAX9quKCIiACPgl4OYFNp7RBRM42G8e18ocGEuXLrUiJVj43GzVqhW6d+9uJf0MVRhl0bVrV3DLyx133HFU9S+++AKXXHIJBg0ahJdeYlaIo8uWLVsOn37Ck1Fq1ODBpEfK5s2bUadOHQwYMMA6iYXlvPPOsyI6du3aVeJFIzfzwyQ/QgJHqNmoz0VABERABEpEYOGG3XjDThxaUFhktVUxIxWXdm+Aa05sgrpVlDi0RIAjcLFJjkkEuhOtJspM4OBpQnd9MR9fZgxFl6RlQPu/Av2HR6ufalcERCDBCLh5gY03JEzU6fSbokJubi7uvNMjIjdq1AgDBw483OUpU6ZYERH169fHzTfzjA1YQgSFh8mTJ6Njx44h8Rw8eNASKY455hjrVJYqVapY3zuJS6+99loruqNnz54499xzLRFj/fr1mDp1qpV81IkM6dy5s3Ut83E0aNDAEjDeffddLF682Dqu9uyzz7Zsefzxx/HAAw+AUR1sMyUlxboX7xlucTM/TPIjJHCEO8KqLwIiIAIiEJJAURETh27FmxNWYeqq7Yfr18rKwLUnNsGlxytxaEiIpVjBJMekFLsd7q3KTODYcygPXR8bgwuLxuKptOFAajlg8HIgU8clhzuIqi8CIvBnAm5eYOONG7eLcNuIv8LEncyZ4V0oNFAwmD59uhURQdFg2LBhoODgtnArCdugGME8Gr73oejy5ptvYt68eZbgUrt2bat9JhDlF8vw4cPx2WefYcGCBdZRsUw0etxxx+Ff//qXJcI45cCBA5YYQ9GDUR+FhYVw8oG4tdep52Z+mORHSOAId4RVXwREQAREICABJg4d+ZsncejyLUfyTzNxKPNrcDuKEoeaN4FMckzMo3PYojITOGjBTR/NxqQFqzCr3D+RUZQD9HsJ6HylwbhkmgiIQKwQcPMCGyt9kZ2RJ+BmfpjkR0jgiPwcUIsiIAIikHAEdh/Mw8fT/8A7k1cj2ytx6InNq+PG3s3QW4lDjZ4TJjkmBoMqU4Hjx0Wb8fcPZuOFtFdwQcpkoOEJwLU/GIxLpomACMQKATcvsLHSF9kZeQJu5odJfoQEjsjPAbUoAiIgAglDgCeivDx+hSVu7M8tsPqdkpyEczvUsSI22tVT4tBYmAwmOSYG8ypTgYPRUd0eG4P2uXPxUfowD6ab5wDVPUcCqoiACIhAcQm4eYEtbtu6LvYJuJkfJvkREjhif86pByIgAiJQ6gQKC4vw+ex1eOqHZdixP9e6f4X0FAzo3hDXntQE9ZQ4tNTHpCQ3NMkxKUk/onxtmQoc7Nu9X83HpzPWYkb521CjcBvQ+1/AKQ9EudtqXgREIN4JuHmBjXcG6l9gAm7mh0l+hAQOzWYREAEREIGwCMxfvwsPfrMI89btOixs3NS3Oa7o0QiVy6WF1ZYqm0HAJMfEDCJ+rShzgWPaqu0Y8OY0DE79FINSvwGy6gO3LQCSkw3GJtNEQARMJ+DmBdb0Psi+6BFwMz9M8iMkcERvLqhlERABEYgrAozUeObHpfjfzHUo8pz2igs61cV9Z7fGMVmZcdXXROuMSY6JwezLXOBg5NRJT41Dxp7VGJ/hOc4QV34DND3ZYGwyTQREwHQCbl5gTe+D7IseATfzwyQ/QgJH9OaCWhYBERCBuCBQUFiET2b8gWd/WoZdB/KsPvFUlKH92uL4ptXjoo+J3gmTHBODx6LMBQ6yGfb9ErwxYRVGlX8E7QuXAh3+Blz0psHYZJoIiIDpBNy8wJreB9kXPQJu5odJfoQEjujNBbUsAiIgAjFPYPbanXh45EIs3LDH6kuljFTc8ZeWGNijEVJTFBYf8wNsd8Akx8RgpkYIHEs27cFZ/5mEASnj8GTaW0BqOWDwciAzy2B0Mk0ERMBkAm5eYE22X7ZFl4Cb+WGSHyGBI7rzQa2LgAiIQEwS2LYvB099vxSfz15/2P6Lu9TH3We2Qs1KGTHZJxkdmIBJjonB42SEwEE+Zzw/ERu3bMGccjchrSgX6Pcy0HmgwehkmgiIgMkE1q1bh9zcXDRt2hRJSYnyemjyiJhl26pVq5CWloYGDRoENMwkPyJRZrAxTolZ01XWiIAIiMDRBPILCvHhtLV47ufl2Hso3/qwXb0sDO3XDl0aVRWuOCVgkmNiMGJjfIlXf1mJp39YhtfKvYqzin4FGvYErv3eYHQyTQREwGQCO3fuxObNm1G9enXUqFEDyUpcbPJwlaptO3bswJYtW6x5UbNmTQkcpUo/+M2McUoMYiJTREAEROAoAjNW78BD3yzE0s17rZ/zRJR/nXEsLu3eECnJiaKHJ+akkMDhatyN8SXW7zyAk54aj5OSF+DD9GEe42+eA1Rv5qojqiQCIiAC3gQKCwuxadMm7NmzxxI3uFqfkpIiSAlOoKCgADk5OahUqRLq1asXNLrHJD8iUTxWY5ySBP89UfdFQAQMJJC95xCe+G4Jvv5to2Udo1MHdGtoiRvVKqQbaLFMijQBkxyTSPctgu0Z5Uv89fWpmLVmG+ZUvB1V8rcCve8CTrk/gt1VUyIgAolGYP/+/ZbIkZ+fD4oeKolNgGJXZmamFb0RauuSSX6EBI7EnrfqvQiIQAITyCsoxHtT1uCFMSuwL8ezHaVjgyp4pF9b61+VxCFgkmNiMHWjBI6Ppq/F/SMW4r6Mz3Bj0tdA5QbArfMBhZYbPIVkmgiIgAjEJwGT/AgJHPE5x9QrERABEQhKYMrv2/DwN4uwInufVY+RGnefeSwu6dIAydqOknCzxyTHxGD4Rgkcuw7kotvjY9CgcAPGZQz2YLtyJNC0j8EIZZoIiIAIiEA8EjDJj5DAEY8zTH0SAREQgQAENu0+iMdGL8Ho+ZusGtQyrujRCHec3hJVyms7SqJOHJMcE4PHwCiBg5yuf28WxizZgjGVH0PznMVAhwHARW8YjFCmiYAIiIAIxCMBk/wICRzxOMPUJxEQARHwIZCbX4i3fl2Fl8auxMG8AutTnooytF9btKtXWbwSnIBJjonBQ2GcwPHt/I0Y9PFcXJ46Do+nvgWklQcGLwcyKhmMUaaJgAiIgAjEGwGT/AgJ1ILZGAAAIABJREFUHPE2u9QfERABEfAhMHH5VgwZuQirtu23PqlRMQP3ntUKF3UOnhFbIBOHgEmOicHUjRM4DuUVoOtjY5Ccsxtzyg9CamEOcP4rwHFXGIxRpomACIiACMQbAZP8CAkc8Ta71B8REAERsAnwKMlHv12MHxdtsX7Co16vOqExbju9BbIy08RJBA4TMMkxMXhYjBM4yOrOz+bhyznr8UGVN9Hr0C9AoxOBa74zGKNMEwEREAERiDcCJvkREjjibXapPyIgAglPgKu6b05chVd/WYlDeZ5j3ro3qYZHzm+LVrWzEp6PAPyZgEmOicHjY6TAMWnFVgx8ewZ6Jc/HB+lPevDdMheo1tRglDJNBERABEQgngiY5EdI4IinmaW+iIAIJDyBcUu3YOioxVi7/YDFolZWBu47uzX6dawb8gzzhIeXwABMckwMHgYjBY6CwiL0GDYW2/cexPysO1AxNxvoczfQ9z6DUco0ERABERCBeCJgkh8hgSOeZpb6IgIikLAE1m7fj0dGLcbYpdkWg9TkJFx3UhPcfGoLVMxITVgu6rg7AiY5Ju4sLpNaRgocJMHf/f9OXo1hWV/i0twvgcoNgVvnAcnJZQJKNxUBERABEUgsAib5ERI4EmvuqbciIAJxRuBgbgFe+2UlXp+4CjwpheXE5tWt01GaH6OTFOJsuKPWHZMck6h1suQNGytwzF+/C/1enoxmSRswNuNfnp5eNQpo0rvkvVYLIiACIiACIhCCgEl+hAQOTVcREAERiEECRUVF+GnxFmvldsOug1YP6lbOxAPntsFZ7WprO0oMjmlZmmySY1KWHELc21iBg38PTn1ugnVS0q/Vn0D9/QuBjpcCF75uME6ZJgIiIAIiEC8ETPIjJHDEy6xSP0RABBKGwKqt+zBk1GLw+FeW9JRk3NC7Cf7ZtznKp2s7SsJMhAh21CTHJILdinRTxgoc7Oh/xqzA82OW4/8qTMA9BW8AaeWBwcuBDEVyRXoiqD0REAEREIGjCZjkR0jg0OwUAREQgRghcCA3Hy+NW4m3Jq1CXkGRZXWfljUxpF9bNKlRIUZ6ITNNJGCSY2IiH9smowWONdv24+Rnf0EW9uO38v9EcmEucP6rwHGXG4xUpomACIiACMQDAZP8CAkc8TCj1AcREIG4JsDw89ELNuHx0Uuwafchq6/1q5bDQ+e2weltamk7SlyPful0ziTHpHR6XKy7GC1wsEcXvDIZv63bha9rvY1Ou8cCjU4CrhldrM7qIhEQAREQARFwS8AkP0ICh9tRUz0REAERKAMCK7bsxZBRizB55Xbr7umpyfhHn2b4x8nNkJmWUgYW6ZbxSMAkx8RgvsYLHO9OXm1tXzsjcyHewBMelLf8BlRrYjBWmSYCIiACIhDrBEzyIyRwxPpskv0iIAJxSWBfTj7+M2Y53pm8BvmFnu0op7WuZUVtNKxePi77rE6VHQGTHJOyoxDyzsYLHNv25eD4J8aiqLAAi6vcgcxD2UCfe4C+94bsnCqIgAiIgAiIQHEJmORHSOAo7ijqOhEQARGIAgFuR/nmt4144rslyN6bY92hUfXyGHJeW/RtdUwU7qgmRQAwyTExeDyMFzjI7qr/zsCE5Vvxeu2ROHPX/4AqDYFb5gHJyQajlWkiIAIiIAKxTMAkP0ICRyzPJNkuAiIQVwSWbt6Dh75ZhBmrd1j9ykxLxqC+zXF9r6bajhJXI21eZ0xyTMyjc9iimBA4Rsxdj9s/nYfWKZvwfdqdHuOv+hZo0stgtDJNBERABEQglgmY5EdI4IjlmSTbRUAE4oLAobwC63jHtyatRoG9HeWsdrXxwLltUK9KubjoozphNgGTHBODScWEwLE/Jx9dHxuDg3kFmF37SVTfNR/oeBlw4WsGo5VpIiACIiACsUzAJD9CAkcszyTZLgIiEPMEZq7Zgbu+mI/V2/ZbfWlaswKG9muLXi1qxnzf1IHYIWCSY2IwtZgQOMjvlk/mYuS8jXiw1lRct/slIK0CMHg5kFHRYLwyTQREQAREIFYJmORHSOCI1Vkku0VABGKaAFdZn/5hKd6fthZFRUBqchJu6tsc/+zbDBmpOh0lpgc3Bo03yTExGF/MCBzjlm7Bte/OQuWkffit/M1IKsgBLngN6HSZwXhlmgiIgAiIQKwSMMmPkMARq7NIdouACMQsgUkrtuKeLxdgw66DVh/a16uMpy/ugNZ1smK2TzI8tgmY5JgYTDJmBI68gkLrNJUd+3MxtuG7aJb9E9C4F3D1twbjlWkiIAIiIAKxSsAkP0ICR6zOItktAiIQcwR2H8zD46MX47NZ6y3b01OTccfpLXH9SU2QmqITDmJuQOPIYJMcE4OxxozAQYYPfr0QH0xbi4E1VuDRfQ97sN46D6ja2GDEMk0EREAERCAWCZjkR0jgiMUZJJtFQARijsBPizbjga8XHj76tWujqnjq4g5oVlN74mNuMOPQYJMcE4PxxpTAMXvtDvR/bSqSUYjl1e5E6oEtwMn3AiffYzBimSYCIiACIhCLBEzyIyRwxOIMks0iIAIxQ2D7vhwMGbUYo+ZttGwun56Cu89shYE9GiE5OVH+BMfMcCWsoSY5JgYPQkwJHEVFRej9zHis23EQnzb9Hsdv/ACo0gi45TcgWRFjBs8zmSYCIiACMUfAJD8iUbzrmHJKYm5Gy2AREIE/EeDLxaj5mzBk5CJrHzzLSc1rYNhF7dGgWnkREwGjCJjkmBgF5mhjYs6XePbHZXh5/EqcVHkbPsy5xdObq0cDjU8yGLNMEwEREAERiDUCJvkREjhibfbIXhEQAeMJbNlzCPePWIgxS7ZYtlbKTMUD57TGX7s2QFJSovzZNX6YZKAXAZMcE4MHJuYEjpXZe3HavydaSBfWfwoVt80DOl0OXPCqwZhlmgiIgAiIQKwRMMmPSBRPO+acklib1LJXBEQAYNTG57PW49HRi7H3UL6F5LTWtfD4he1QKytTiETAWAImOSbGQgJi0pc458VJWLRxD15sPhv91j8HpFUABi8HMpT/x+C5JtNEQAREIKYImORHSOCIqakjY0VABEwlsG7HAdw3YgEmrdhmmVitQjqG9GuL8zrUUdSGqYMmuw4TMMkxMXhYYlLgGD5xFR7/bgkalsvBhOS/I6kgF7jgdaDTpQajlmkiIAIiIAKxRMAkP0ICRyzNHNkqAiJgHIHCwiK8P3UNnv5xGQ7kFlj2ndexLoac1wbVK2YYZ68MEgF/BExyTAweoZgUODbvPoQTnhyLoiJgeosPUGvd90DjXsDV3xqMWqaJgAiIgAjEEgGT/AgJHLE0c2SrCIiAUQRWbd2Hu7+cj5lrdlp2HVMpA49f2B6nt6lllJ0yRgRCETDJMQllaxl+HpMCB3ldNnwapvy+HXc3W4d/bLjbg/DW+UDVRmWIU7cWAREQARGIFwIm+RESOOJlVqkfIiACpUYgv6AQb/26Gv/+eTly8wut+/61a33cf04bVC6XVmp26EYiECkCJjkmkepTFNqJWYHjs5nrcNeX81EhrQgLsu5A8v4twMn3ASfbYkcUYKlJERABERCBxCFgkh8hgSNx5p16KgIiEAECSzbtwV1fzMeCDbut1upVKWcd/dq7Zc0ItK4mRKBsCJjkmJQNAVd3jVmBY8+hPHR9bIwlyP7U9me0/P0doEoj4JbfgORkV51XJREQAREQAREIRMAkP0ICh+apCIiACLggwBeDV8avtL7yC4usK646oRHuOrMVKmSkumhBVUTAXAImOSbmUorNU1Qcnv/4cDa+X7gZlzbej2Gbb/D8+OrvgMYnGoxcpomACIiACMQCAZP8CAkcsTBjZKMIiECZEpi3bpcVtbFsy17LjiY1KuCp/h3QvUm1MrVLNxeBSBEwyTGJVJ+i0E7MRnCQxQ8LN+P/PpyN5CRgWaNnkLZ5LtDpCuCCV6KASk2KgAiIgAgkEgGT/AgJHIk089RXERCBsAgcyiuw8my8NWkVGLTBF4MbejfF7ae1RGZaSlhtqbIImEzAJMfEYE4xLXDk5Beg22NjsOdQPj7ptAAnLB0GpFcEBi8H0isYjF2miYAIiIAImE7AJD9CAofps0X2iYAIlAmBGat3WCekrN6237p/q9qV8PTFHdChfpUysUc3FYFoEjDJMYlmP0vYdkwLHOz7PV/Ox/9mrsOJ9VLw0a6BQEEucOEbQMcBJUSjy0VABERABBKZgEl+hASORJ6J6rsIiMCfCOzLycfTPyzF+1PXWp+lpSThn32b46aTmyM9Vcn4NGXik4BJjonBhGNe4Jj6+3ZcOnyahXhhu09QceUooElv4KpRBmOXaSIgAiIgAqYTMMmPkMBh+myRfSIgAqVGYOLyrbj3qwXYsOugdc8O9StbURutameVmg26kQiUBQGTHJOy6L/Le8a8wFFYWIQTnxqHTbsP4YXO2bhg8W2ert+2AKjS0CUGVRMBERABERCBowmY5EdI4NDsFAERSHgCuw/k4bHRi/H57PUWi4zUZNxxektcd1ITpKYoaiPhJ0gCADDJMTEYd8wLHGQ77LsleGPiKjSrnokxSf9A0r4tQN/7gT53GYxepomACIiACJhMwCQ/QgKHyTNFtomACESdwI+LNuOBrxdi694c617dG1fDk/3bo2nNilG/t24gAqYQMMkxMYWJHzviQuBYvHEPzn5xktW9Gd1+wTEL3gSqNgZu+Q1IShS30OBZJtNEQAREIAYJmORHJMqTLC6ckhic6zJZBIwlsH1fDh4euQjfzt9k2Vg+PQX3nNUKVxzfCMk8LkVFBBKIgEmOicHY48KXKCoqwhkvTMTyLftwV+cC3LR4oAf5Nd8DjXoajF+miYAIiIAImErAJD8iUbz4uHBKTJ3QsksEYokAnfuR8zZiyMhF2HkgzzK9V4saGHZRe9SvWj6WuiJbRSBiBExyTCLWqcg3FDe+xCvjV+KZH5ehRsUMzKz5GJI2zQWOuwI4/5XIU1OLIiACIiACcU/AJD8iHIGDG9FvBfB3AI0BbAXwGYCHAHjOUQxeGO99C4BL7esZD74cwJsA3gNQ5HP58QAeB8B/+dkUnnAG4LdQN/Lzedw4JcXouy4RARGwCWzefQj3j1iAsUuzrZ9kZabigXPb4JIu9ZGk0GzNkwQmYJJjYvAwxI0vsX7nAZz01HgL9c8nLUOLWUOB9IrA4OVAegWDh0CmiYAIiIAImEjAJD8iHIHjP7ZAMQLA9wBaA7gZADdyngagMAhsiiMTADD2kWIGzyjjUinFju4AngZwt9f1PQD8AmADgJftnw8CcIzdxoIwBzZunJIw+63qIiACVEiLivDpzHV4fPQS7M3Jt5ic3qYWHrugHWplZYqRCCQ8AZMcE4MHI658iUten4KZa3biig4V8djvFwMFucCFbwId/2bwEMg0ERABERABEwmY5Ee4FTjaAqCoQHGjvxdUChwvArgcwMdBYJ9gR2C8AOB2r3rpAJYCqAagitfPZwBoZYsoFDlY6gFYYosjfwlzYOPKKQmz76ouAglNYN2OA9bRr7+u3GZxqF4hHUP6tcW5HeooaiOhZ4Y6703AJMfE4JGJK1/iw2lrrQTLFdJTML/NR0hZOhJo0ge4aqTBQyDTREAEREAETCRgkh/hVuB4DMD9AHrbERsOVy59brejM84OAvsMAD8A4Blkz/jUo5hB8YJfLM0BrADwXwDX+dR9m2mwANDJ2BzG4MaVUxJGv1VVBBKWQGFhEd6bugZP/7AMB/MKLA7nd6qLh89ri2oVqK2qiIAIOARMckwMHpW48iV27s9Ft8fHIL+wCJ/23Y3jp/4DQBJw23ygSkODh0GmiYAIiIAImEbAJD/CrcDxo70NhdtKPGcpHimTAbQEUDMI6KoAVgFgbPhNAKbbW1SuskWP/wMw3L6e21YYDXIDgLd82uTPmLPjXACjwxjYuHJKwui3qopAQhL4fes+3P3FfMxau9Pqf62sDDx+QXuc1qZWQvJQp+OIQGEBkLvf88VcCZlZEemcSY5JRDoUnUbizpe4/r2ZGLMkG39pVR1vbh0I7M8G+j4A9PlXdAiqVREQAREQgbgkYJIf4Vbg4PYU5r/w93bARKOXAMgAkBtkxHrZggXFEKfsBXAlgK+9fnYngGcBMCKEuT68C39GYYOJTil0BCqVAPDLKbR7zoYNG1C3Lv0TFREQgXgkkFdQiDcnrsJ/xq5Abr4nLdCAbg1w79mtUblcWjx2WX0ylUBREZB/yBYj9h0RJRxxwvrX++f7gLwDXvV8PrPqHwDyDx7p8dnPAt2p+5e8mOSYlLw3UWsh7gSOUfM24uZP5iI1OQkLe05C5qxXgapNgFvmAkq8HLWJpIZFQAREIN4ImORHuBU4fgfAtwN/MYvvA+Ah6ozS2BVksI4D8IAdycETUZh34592ro3zmcjbvvZBAI8AOBXAOJ/2TgEw1s7jwXwegcoQAA/7fiiBI95+ldQfEThCYO4fO61cG0s3UzcF6lcthycv6oCTWtQQJhEITqAg/2ixIc+OkPAnQlBkOCxS+BEhDosU+4CiYLm3IzAopw0BTvJOa1X8Nk1yTIrfi6hfGXcCx8HcAnR97Gfszy3Ay6em49zJF3sgXvMD0Ijp01REQAREQAREIDQBk/wItwJHSSM42gNgrg16Yq97IeKWl4UAeMpKMwDcKK8IjtBzSDVEQARsAvty8vHsj8usfBtcNE9OAq7u2QR3/qUlKmSkipMIHCGQsw/YPB/YOPfI1651QIHvzssoQktK8RzHye0l1ld5n//nz7w+T3Pq+fycdSrWBDIrR8RYkxyTiHQoOo3EncBBTHd89hu+mrMB3RpXxedJ9wKbfgOOGwic7xxiFx2YalUEREAERCB+CJjkR7gVOEqag4MJQ5kclEupTErqXV4CwCNgmVyUkSLKwRE/c109EYGoEhi7ZAse/HohNu4+ZN2ndZ0sPHlRe3Rs4H0oU1RNUOOmEsg7CGxe6CVmzAG2LuOhwe4tTqP4UAGw/vUWJXzFhiAihO/1qRlGhv6b5Jh4DdC9ADoD6AKgCYC1ABq7H8DDNbkVlgssPJ1tD4BRANj21jDbikuBY+Lyrbjyv1yDAuaeuRpVf7kfSK8EDF7uEeBUREAEREAERCAEAZP8CLcCR6hTVCYCOCtIvymQ8GhX5sLI9qn3GgAmGaXjQe9Tp6joV0gERCAogey9hzB05GKMXrDJqpeRmozbTmuJ63s1QVoKA8JUEopAfi6QvejoyIzsJUAh81r7KdWaAXWP83zVaAlkVPpzJAVFjeSUhMFokmPiBZ1q1A7m0LJFDooT4QocFDb+bZ/2xgTm9Rm0YIsl3QHsD2OQ41LgyC8oRI9h47BtXw4eOKUWrp92JlCYB1w0HOjw1zDwqKoIiIAIiECiEjDJj3ArcHCLyTwAIwD09xq4mwG8aOfg+ND+ObeaMF/HUq96zwO4DcDdAJ72+jmXWRcD4HGzPIXFc5YjMBPAsbbosdH+GR0LtsllhtPCnDxx6ZSEyUDVRSDmCfDo109nrcOw75ZgzyHPy+uJzavjiQvbo1H1CjHfP3XABQHmy9i2DNgw54igsWUhUBAgxzWPu3TEDP5bpxNQThE+vqRNcky8bGtq5+3ij7idtWKYAgejRhn1sQgAE0o4PsZ5AEYCuB/AEy5mnVMlbn2JoaMW4Z3Ja9CyVkX8WPctJC0ZCTQ9GbjymzDwqKoIiIAIiECiEjDJj3ArcHCsnK0kFDm+YzQ4gFsA8JhYJv90sqmtAdDIc5j64cL/5woME5F+ZF/DJKNM/87VGCYbfdWrfk8A4wGst+/LjyimMALkRFtsCWf+xK1TEg4E1RWBWCawMnsf7huxADNWc0EXqFI+DQ+e0wYXda6HJGX7j+WhDWx7YSGwfaUtZNiCxqb5R58k4n11pTpA3c5egkYnoIKSzLqZHCY5JgHsLY7Acb19BD23qHzg0y63xDL5Shs3fOw6cetLzFu3C+e/QncOmHh+Dhr+yF3FScBtC4AqDcJApKoiIAIiIAKJSMAkPyIcgYOxuozCuNEWJbYB+BTAQwD2eQ2kP4GDHzOyg3V5OgqFCp519xsAnobylZ+JwNUWbo053t40zZNXuGeWQkm4JW6dknBBqL4IxBoBHvf6+oTf8fK4lcgt8OioF3SqiwfPbYPqFXk6tUpcEGCG2J2rPWKGFZ3xG7BpHpDrORXnT6V8DY+QUc8WNBiZkVUnLlCURSdMckwC9L84Ascbts/SAsBKn3a52MKcX1k+Pkww/HHrSxQVFeGU5yZg9bb9+MdJDXH3kouA/dnAKQ8Avf9VFlNS9xQBERABEYghAib5EeEIHDGE+E+mxq1TEsuDIttFIBSB2Wt34J4vF2BFtkdD5dGvj1/YHn1ackebSswSoJixe/3RCUApaBwKcNI4Two5vM3EFjQq1zcyWWesjolJjkkEBQ4mEz0XADNlclHFu3C7LN/cuR12eYB7VgLAL6dwcWZOvB45/8KY5XhhzArUqZyJKceNRdK0V4BqTYGb5+h3LVZ/sWW3CIiACJQSAZP8CAkcpTTouo0IiIB7AnsO5eGZH5bhw+lrDx/9en2vprjttBYon66jX92TNKTm3s1HJwBllMb+AAdY8LQSRmPU5ZcdoVG1iV6wojyUJjkmERQ4xtpbaBmB6myjdZp/BMCDAI6zo0n93XYIgId9P4hXgWPNtv04+dlfrO5+c3FldPz2HE/Xr/0RaNgjyjNQzYuACIiACMQyAZP8CAkcsTyTZLsIxCGBHxdtxkPfLMSWPdweD7Srx6NfO6Bdvcpx2Ns47NL+7X8WM/Y6uaJ9+ptaDqjT4ejojOrNgWSdhFPaM8MkxySCAociOMKcSMzDwXwcA7o1wJPbBnm2iXW+EujHNGwqIiACIiACIuCfgEl+hAQOzVIREAEjCGzefQgPj1yIHxdtsewpl5aCO05viWtObIxUHf1qxBj5NWJfNrDgC2DdNI+wsesP/7ampAO12h19oknNVkCKInJMGFyTHJMIChzKwRHm5Hpn8moMHbUYWZmpmH3GKqT9eA+QXgkYvNxzlLKKCIiACIiACPghYJIfIYFDU1QERKBMCfDo149m/IGnv1+KvTmeo197t6yJxy9ohwbV5FCX6eAEujmPav19LDDnfWD5D0ChZ9wOl6QU4Jg2QL3jjgga/P9UJYU1cjwBmOSYRFDgCHWKCs8W5olwbkvc5/PaujcHPYaNRUFhEd6+pAlOHd0bKMwDLnoL6HCJW06qJwIiIAIikGAETPIjJHAk2ORTd0XAJAIrtuzFPV8twOy1Oy2zqlVIx0PntsH5nerq6FeTBsqxZecaYO6HwNyPAO9tJ5lVgJZnAvW6eASN2u2AtHIm9kA2BSBgkmNSTIGjoZ1MlMe/5tltMBvxWgALAPD4+QL75+cBGGnn4OBpbW5L3AscBHHlf2dg4vKtOKtdbbyW9jywZBTQtC9w5dduOameCIiACIhAghEwyY+QwJFgk0/dFQETCOTkF+CV8b/jtV9WIq+gyDKpf+f6eOCc1qhaId0EE2WDQyDvELD0W0+0xuoJR3Np0sezP7/VuUBappjFMAGTHBMvjAMBNLL//2YA/OPwnP3/FC4+8KrL7Jh9ADQBwOPqnXIngGcB8PNPANQDwJ+tA9AtjCNi2V5CCBxfzVmPOz6bh/TUZPz2t3yU//IKAEnA7QsBnl6kIgIiIAIiIAI+BEzyIyRwaHqKgAiUKoEZq3fgnq/mY9XW/dZ9G1UvjycubI8Tm9coVTt0sxAEtizyiBrzPwUOeiJsrFKpDtDpcuC4K4BqfJdUiQcCJjkmXjwd0cIfYqptJ/up6ytwsMrVAG63j4TdA+BbAPcAyA5z7BJC4NiXk4+uj/2MQ3mFeObC1rhk4l88px6d8iDQe3CYyFRdBERABEQgEQiY5EdI4EiEGac+ioABBHYfzMOT3y/FJzM8SShTkpNwY++muPXUFshM4ymOKmVO4NAeYOGXwNwPgA2zj5jDnBrHnuWJ1mh2qhKDlvlARd4AkxyTyPcuYi0mhMBBWjd/Mhej5m1Ez2bV8XGDkcC0V4BqzYCbZ+vI5ohNJzUkAiIgAvFDwCQ/QgJH/Mwr9UQEjCRQVFSE7xduxsMjF4EJ7Fg61q+MYRd1QJu6WUbanFBGFRUB62Z4ojUWfQXkHTjSfb7QUNToeClQqVZCYUm0zprkmBjMPmEEjrFLtuC692YhKQmYeV1t1PjwFM+wXPsT0PB4g4dIpomACIiACJQFAZP8CAkcZTEDdE8RSBACG3cdxEPfLMSYJZ5I8PLpKRj8l2NxVc/GVgSHShkS2L8NmPeJR9jYtvyIIanlgLYXAMcNBBr11GptGQ5Rad7aJMekNPsd5r0SRuDIKyhE98fHYOeBPNx/dmvcsPgqYPN8oPNVQL8Xw8Sm6iIgAiIgAvFOwCQ/IlHeMBLGKYn3Xx71LzYI8IjBD6auwTM/LsP+XM/BBX2PrYlHL2iH+lV19GuZjWJhAfD7eGDu+8DS7zzHPzqlTkdPtEa7i4FyVcrMRN24bAiY5JiUDQFXd00oX+KBrxfgw2l/oG3dLIzuvgj44W4gIwsYvFynJLmaLqokAiIgAolDwCQ/QgJH4sw79VQESoXA0s17cM+XC/Dbul3W/WpUTMfD57XFuR3q6OjXUhkBPzfZ9YfnaFce8bpn/ZEKGZWBDn8FOg8EKHCoJCwBkxwTgwchoQSOWWt24OLXp1rDMe4f7dD0vS4eUbT/20D7iw0eJpkmAiIgAiJQ2gRM8iMkcJT26Ot+IhCnBA7lFeClcSvwxoRVyC/0HP06oFsD3HNWK1Qpr6NfS33Y83OAZd95tqAwagOeMbFK416eLSht+mklttQHxswbmuSYmEnIsiqhBA7mT+r19His33kQg/o2x+Cdj3iOjG52CjBwhMHDJNNEQAREQARKm4BJfoQEjtIefd1PBOKQwJT3l9oWAAAgAElEQVTft+H+EQv/n73zAJOyuv7/h63A0nuTXpTee7FLU1AElSJFKSKgiTHG/IwxiSYak78UC0XpKqCAgGDDRm8iCAICAtJ7Z9m+/+fOO4ZlWdmZ3dmZO7Pf+zw8PO7ccu7nvO58Oe+957D3pFP6tWqJGF66tx6tqhUPwd1avqXj22HjDPhhFsSeumJsgdJXyrsWr2b5JmSevwnYJEz8vXcv1stVAQ7D5dXPdvDG1z9zQ7F8LLvnMnlm9QbywO9+hMLlvUCnriIgAiIgAqFMwCYdoQBHKD9p2psI5DCBs7EJvLR4Ox9851x7iAjLw2M3V+PxW6qr9GsOs79q+viL8ON857TGwXVXPjLlXWvc6eTWMH+HR/jTKq0VRARsEiYWY8t1AY5dxy5wx2vLXC6ZO7QpTT5oDbEn4bbnod1TFrtKpomACIiACPiTgE06QgEOf3pea4lAiBAwR5cX/XCEvy/6kZMXE1y7alSxCC/fV59aZQqGyC4t34Yp73roO9g4DbbOg4SLVwwuWsXJq9GgNxQqa/lGZJ4NBGwSJjbw+A0bcl2Aw3DoPGY5246c5+FWlfh79Luw5k0oXh1GbFCVJYsfVpkmAiIgAv4kYJOOUIDDn57XWiIQAgQOnonluY+28s1PJ1y7KRAdwR871qJPi0oq/eoP/146BT/Mhu9nwPFtV1YMj4ba3ZzARqW2EBbmD2u0RogQsEmYWIw0VwY4Ji77mX8u2UGxmCjWDipF5KT2jose+QJuaG6xu2SaCIiACIiAvwjYpCMU4PCX17WOCAQ5AVP6dcrKvfz3851cTnRKv95+U2n+0b0OZQvnC/LdWW5+Sgrs/da5gmKS/CU7p2ZcrXQ95wpK/Z6Qr6jlG5F5thKwSZjYyii3JRn91Q9Hz8XR6uUvMYfGpgxoxi3f3AdHt0Dj/nDPWIvdJdNEQAREQAT8RcAmHaEAh7+8rnVEIIgJ/Hj4HM/O28IPB8+5dlGqYDR/u6cOHeuWUenXnPTruYOw6T3ntIYp9fpriy7klGk0gY2yDXVMPCd9kEvmtkmYWIw8V57gMP54aOIaVu85RbeG5RhTeQ18+icwOX76L4TKbS12mUwTAREQARHwBwGbdIQCHP7wuNYQgSAlcDkhmdFf7uTt5XsxJzhM692iIs90vJHC+SKDdFeWm52UADs/dZd3/RJSU64YXLG1E9QwV1Gi8lu+EZkXTARsEiYWc8u1AY7Z6/fzzNwt5IsMZ8MzrYmZ0RmObYGYkjB0GRQyaNREQAREQARyKwGbdIQCHLn1KdS+RSATAqt/PsUzc39g/+lYV89qJWN4uUd9mlUuJna+JhB3Hg6uh5+/cvJrXHLym7ia+QdEg4ecwEaJGr5eWfOJgIuATcLEYpfk2gDHucuJNHtxKQnJKYx+oCHdKyXAxA4Qdw4qNIMBiyEi2mLXyTQREAEREIGcJGCTjlCAIyc9rblFIEgJzNt4kKc//MF1aiMyPI+r7Ksp/xodER6kO7LM7AtHYf9q2L8GflkFx7ZefVIjTxhUv90JatTsCOE6LWOZB0POHJuEicVwc22Aw/hk2Izv+PTHo9xcqyRTBzaHXV/Auz2BVGg6CLq+ZrHrZJoIiIAIiEBOErBJRyjAkZOe1twiEIQEJi3bw0tLtrssv6lsIcY91JDqpVT6NcuuNJn5Tu6C/aucgIYJbJzZd+10JqhRui7cdDc07AOFy2d5SQ0UAW8J2CRMvLXdj/1zdYDj061HGDZzo6ta1to/30aJAtHw7avw9YuOC+553anipCYCIiACIpDrCNikIxTgyHWPnzYsAhkTSE1N5eVPdjBh2R5Xh5ZVizHx4aYUyqvTA149MyaHxpHNV05omIDG5dPXThGRDyo0hYqtoGJL55h33kJeLaXOIuArAjYJE1/tKQfmydUBjrjEZJq9tJQLcUmuJNP9W1cGU+Fpdh/4aQmYUtWDPoXyjXMAvaYUAREQARGwmYBNOkIBDpufFNkmAn4ikJScwp/mbeHD7w66VuxYpwyjH2xI3khdScnUBa78GevcpzPWwMENkHT52mH5i18JZpigRpn6EBGV6fTqIAL+IGCTMPHHfrO4Rq4OcBhmz3z4A7M3HKBRxSLMH97GwWjycEy8BU7/DIUqwNBvIaZEFhFrmAiIgAiIQDASsElHKMARjE+QbBYBHxIwlVJGvLeRL3ccd836UPOKvNi9rusYsloGBM4fcZ/OMDk0VsOxH6/On/HrkKJVnIBGJXNCoxUUr65yrnqgrCVgkzCxFhLk+gDHqp9P0nvSWpeLvn36ZioVj3HcdXw7TLoNEi9BlQ7Qdx6ER1jsSpkmAiIgAiLgSwI26Yjc8i+YXC9KfPkAa67QIXAuNpFHpq1nwy9nXJsadWt1fndHTfLkyS2/GjLxpSt/xk4nEeiv+TPO/nLtIJM/o0w9MGVczXUT86dgmdB5ULSTkCdgkzCxGHau1xIpKam0fvkrjp6P4/d31GTUbWkqO/04Hz4Y4LivzRNwx98tdqVMEwEREAER8CUBm3REbvlXTK4XJb58gDVXaBA4ei6OhyevZeexi5h4xgt3u+9Uh8b2srYLV/6MTWnyZ6zJOH9GZP5r82dEKxFr1qBrlA0EbBImNvD4DRukJYB/LtnOxGV7KFMoL5892Z7C+dPkafr8L7BqrIOv51Soc6/F7pRpIiACIiACviJgk45QgMNXXtU8IhBEBHYfv0j/yes4dPayqwzs/+vVkLsbGO2ey5q5O35g/ZUrJ4e+g6S4ayHkL+E+meG+blK2vkq35rJHJdS3a5MwsZi1AhzAvpOX6DhmGXGJKa58TW/1bXzl1F9yEsy8F/Yug8gYGPwVlLrRYpfKNBEQAREQAV8QsElHKMDhC49qDhEIIgKbDpxl4JR1nIlNJH9UOBP6NaFdjZJBtINsmHr+cJrrJmvg2FYg9doJi1V1JwT9NX9GNeXPyAZ2DbWfgE3CxGJaCnC4nTN7/X6embvF9V//6F6Xfi0rXXHbpZMwoQOcP+jkHjJBjryFLXarTBMBERABEcguAZt0hAIc2fWmxotAEBFYtvMEw2Z+R2xCMsViopg6sBn1KxQJoh14YaopX3jyp6vLtZ7df+0EecLBnMj4tVzrDSZ/RmkvFlJXEQh+AjYJE4tpKsDhdo4pK/7ErE0s3HyYqIgwPhrehtrl0pS5PrQRJneE5Hio1QUemAlhYRa7VqaJgAiIgAhkh4BNOkIBjux4UmNFIIgILNh0iKfmbCYpJZXyRfIx45HmVC1ZIIh24IGp5nj09gXwwwdwwOTPcJKnXtVc+TOaXQloVGgKyp/hAVx1CWUCNgkTizkrwJHGORfiEuk6bgW/nIqlaskYFo1oS0x0msopG2fAwhHOiFufg/ZPW+xamSYCIiACIpAdAjbpCAU4suNJjRWBICEwZeVe/rZom8vaWqULMv2R5pQulDdIrPfAzMTL8P1MWP06nNl39YCYklfnzzDVTsLTJMXzYHp1EYFQJ2CTMLGYtQIc6Zzzw8Gz9HhrFYnJqdzfpAL/6dng6h6LnoTvpgB5oM8HUOMOi90r00RABERABLJKwCYdoQBHVr2ocSIQBATMMeL/fP4Tb3z9s8vaZpWL8vbDza7Oeh8E+/hNE2NPw/q3Ye0EiD3p7pYHbuwCtTo5pzRMPg2VvQ1mL8t2PxCwSZj4YbtZXUIBjgzIvb18Dy8u3u765LUHGnBvowpXeiXFw5TOcGiDk4djyDfO72Q1ERABERCBkCJgk45QgCOkHi1tRgTS6MrkFJ77aCuz1h9w/fD2m0rzeu9G5I0MD35MZw/Amjfhu2mQeMnZT3gUNHgQWo+CEjWCf4/agQj4kYBNwsSP2/Z2KQU4MiBmAumPTtvAlzuOuxJXfzyy7dXXH88dgokd4NIJKF0XHvkCovJ7y179RUAEREAELCZgk45QgMPiB0WmiUBWCcQlJjPq/e/5fNsx1xS9mlbgn/fWIyI8yJO8HdsGK8fA1g8hJcnBE10Img6Clo9BwTJZRaZxIpCrCdgkTCx2hAIcv+Gc05cS6DxmOUfPx1GnXCHmDW9NdESaYPq+FTDtHkhNhno94b5JOlln8YMu00RABETAWwI26QgFOLz1nvqLgOUEzl1OZPD0Dazbe9pl6WM3V+OPd9UiT7Be00hNdUq7msDGrs+u0C9QBloNhyYDVILQ8mdS5tlPwCZhYjEtBTiu45y1e07x0KQ1pKTCgNaVeeGeOlf3Xv0mfPas87OOr0DLYRa7WqaJgAiIgAh4Q8AmHaEAhzeeU18RsJzA8fNxPDx5HTuOXnBZ+peutXmkbRXLrf4N80yZ158WO4GNg+uvdCpeA9qMgvoPQER0cO5NVouAZQRsEiaWoUlrjgIcmThnzNJdvLZ0p6vXxH5NuLNOmlN1Jlg991HnBF5YBDy8ECq3sdjdMk0EREAERMBTAjbpCAU4PPWa+omA5QT2nrzEw5PXcuD0ZSLC8riy2XdvVN5yqzMwzySl+2E2rBwLp3Zd6WBKu7Z5Emp1hrAgv2oTfF6RxSFOwCZhYjFqBTgycU5ySip93l7Dmj2nKZwvkiVPtHOVJf9fS7gEb98Bx3+EmFIw9FsoZLCqiYAIiIAIBDMBm3SEAhzB/CTJdhFwE9h66Bz9J6/j1KUE8kWG81bfxtxcq1Rw8Yk7BxumwJq34OLRK7bXuAvaPAGVWuvOdnB5VNYGEQGbhInF2BTg8MA5x87H0WnMckxejqaVijJrSMur8z+d3gMTbwbzO98Ergcs1mk8D7iqiwiIgAjYTMAmHaEAh81PimwTAQ8IrNp9kiEzvuNifBJF8kcyZUAzGlUs6sFIS7pcOOoENTZMhvjzjlHm+HLd+52rKKXT3eO2xGyZIQKhRMAmYWIxVwU4PHTO1zuOM3Cqc7VwxC3V+cNdta4eufNzeK8XkApNH4Gu/8/DmdVNBERABETARgI26QgFOGx8QmSTCHhIYMmWIzw5axMJySmUK5yX6Y80p3qpgh6ODnC3k7th1RjYPAuSExxjImOgSX9oORyK3BBgA7W8COQeAjYJE4upK8DhhXNeWryNScv3YvJbzxjUgrY1Slw9+ptX4Jt/Oj/r9gY06uvF7OoqAiIgAiJgEwGbdIQCHDY9GbJFBLwgMGPNLzy/YCsmb1v1UgWYPqg55dLedfZiLr92PbgBVo6G7R87b+9My18cWgyDZo9C/mJ+NUeLiYAIgE3CxGJ/KMDhhXMSklLoOWE1mw+cpWTBaJaMauf6+3/NJJKe9RDs/BTCo+GRz6BcIy9WUFcREAEREAFbCNikIxTgsOWpkB0i4CGB1NRURi/dxZgvnQScjSoWYXL/ZhSNifJwhgB0M1GY3UthxWj4ZcUVA4pUgtYjoWEfiMofAMO0pAiIgCFgkzCx2CMKcHjpnP2nYukydjkX4pNoV6ME0wY2JywsjfS8fBYm3QImL0fhG2DItxBT3MtV1F0EREAERCDQBGzSEQpwBPpp0Poi4AUBk6H+rwu3MnPNfteom2uV5M0+jckfFeHFLH7smpwIW+c5pV5N1vxfW5n6TuLQ2t0h3FLb/YhJS4lAoAnYJEwCzeI66yvAkQXnfPzDYUa8971r5J863ciwDtWunuXYNnj7NkiMhSodoO88fS9kgbOGiIAIiEAgCdikIxTgCOSToLVFwAsC8UnJ/G72JpZscSqM3NeoPK/cX5/IcAtLpppSgBtnwOrX4dyBK7s04rXtk1D1FlVE8cL36ioCOU3AJmGS03vNxvwKcGQR3rPztvD+uv2uEuZzhrWicfpE2FvnwoeDnNlNOfA7/pbFlTRMBERABEQgEARs0hEKcATiCdCaIuAlgQtxiQyd8R2rfj7lGjm4XRWe7XTT1Ud9vZwzR7pfOgnrJjp/Lp9xlsgTBrW7OSc2dL86R7BrUhHILgGbhEl295KD4xXgyCLcywnJdHtjBTuPXaR8kXyufByF80dePdtn/+cExU3rNd353lATAREQAREICgI26QgFOILikZGRuZnAiQvxDJy6jq2HnBKqz3a6kaHpj/gGGtCZfbDqdfh+JiRddqwxSeMa9YFWI6B4uiPJgbZX64uACFxFwCZhYrFrFODIhnN2HbvA3a+vIC4xhU51y7iuV+YxJVZ+bclJMKM77FsOUQXg0S+h1I3ZWFFDRUAEREAE/EXAJh2hAIe/vK51RCALBEyCtn6T1/LLqVjCw/Lw8n316NnUovKpR35w8mv8OB9Sk50d5i0MzQZDi6FQoFQWdq0hIiAC/iZgkzDx9969WE8BDi9gZdR19vr9PDN3i+ujf3SvS7+Wla7uZk4BTugA5w9C8eow+CvnO0VNBERABETAagI26QgFOKx+VGRcbiaw7fB5+k9ZhznBkTcyjDd6N+a2m0oHHompiLJ3mVPq9eevrthTqDy0ehwaPwzRBQNvpywQARHwmIBNwsRjo/3fUQGObDI3VcBGzdrEos2HiYoIY8HjbbipbKGrZz30HUzuCMkJUKsLPDATwizMNZVNFhouAiIgAqFEwCYdoQBHKD1Z2kvIEFiz5xSDp21wldYrlDeCKQOb0aRSscDuLyUZti90TmwcdjLiu1rJm5z8GnV7QITFpWoDS0+ri4DVBGwSJhaDUoDDB84xOaW6jF3B/tOxVCsZw6KRba+tBLZxOiwc6ax261+g/R98sLKmEAEREAERyCkCNukIBThyysuaVwSySODTrUcZNet7EpJSKF0omumDWlCrTABPRCTGweb3YNU4OL3nyq4qtnKy3de4U2/XsuhrDRMBWwjYJExsYZKBHQpw+Mg5Pxw8S4+3VpGYnMr9TSrwn54Nrp150RPw3VSTqRr6fgjVb/fR6ppGBERABETA1wRs0hEKcPjau5pPBLJBYNa6/fx5/hZSUqFqyRimD2pOhaL5szFjNoZePgvr34a14+HSiSsTmSPD5sRGxRbZmFxDRUAEbCJgkzCxiUs6WxTg8KFz3l6+hxcXb3fN+NoDDbi3UYWrZ0+KhymdwFxZyVsEhn4LRSv70AJNJQIiIAIi4CsCNukIBTh85VXNIwLZIGDuJb/x9W7+8/lO1ywNKhRmysDmFIsJ0JWP/WtgTn+4eNTZVVgk1H8A2oyCkrWysVMNFQERsJGATcLERj5umxTg8KFzzPfeo9M28OWO48REhfPxqHZUKRFz9QrnDsGE9hB7EsrUg0GfQ1SAgv4+3LumEgEREIFQI2CTjlCAI9SeLu0n6AikpKTy94+3MXXVPpft7WqUYHzfJsRER/h/LyaB6Jq34Iu/QEoSRMZAs0HQcjgUMtpeTQREIBQJ2CRMLOarAIePnXP6UgKdxyzn6Pk46pQrxLzhrYmOCL96lb3LYXo3p1KXCbTfOwHSlpf1sU2aTgREQAREwHsCNukIBTi8959GiIDPCJg8G099sNmVUd60exqUc91FNtnl/d7iLzhJ3UzJV9NK1Xay1xev5ndTtKAIiIB/CdgkTPy7c69WU4DDK1yedTZJtXtPWuO6mjmgdWVeuKfOtQNXvwGf/dn5ead/O2XI1URABERABKwhYJOOUIDDmsdChuQ2Apfikxg28zuW7zrp2roRds93rU1YWAD+tzzxE8zuByd/ctxQrxfcPRqi0h0Xzm1O0n5FIJcQsEmYWIxcAY4ccs7opTsZvXSXa/aJ/ZpwZ50yV69kThfOfQS2zoWwCOi/CCq1ziFrNK0IiIAIiIC3BGzSEd78S8q8Un4CMGFzk+XJZB2cAzwPXMoEwgvAX6/TJwmITPP59fo/DfzHS+gSJV4CU/ecJXDqYjyDpq5n88FzroWevqsWw2+uRp5AHLs1JzYWjICEi06ujY7/gmaP6ghwzj4Cml0ErCJgkzCxCszVxkhL5JBzklNS6fP2GtbsOU3hfJF88kQ7yhXJd/VqCZfg7dvh+DaIKQVDl0GhsjlkkaYVAREQARHwhoBNOsKbAMcYYBRgzq9/AtwEmCLlywFTuyvlOhDqA+ZP+mZ+ZgIWZs77Mghw/A5wXm9fad8BTtptz5tEiees1DOHCRw8E8vD76xjz8lLmMMa/7y3Hg82r5jDq2YwfXIifPFXWPOG82Gh8tBzGtzQzP+2aEUREIGAErBJmAQUxPUXl5bIQeccOx9HpzHLMXk5mlYqyqwhLYkIT3dd89TPMPEWiD8HFZrDgMUQEaBk3DnIQlOLgAiIQLARsElHeBrgMBcit7gDET3SADcBjrFAH+C9LDhiAjAE6AosTjP+1xMcVQAn82L2mkRJ9vhptI8I/HT0Ag9PXsux8/GuPBvjHmrEXemP4vporetOc+EofDAA9q92ulW9GXq8AzEl/LG61hABEbCMgE3CxDI0ac2Rlshh53y94zgDp653rTLy1uo8dWcGVbt2fgbv9XIsMacNu/w3h63S9CIgAiIgApkRsElHeBrgeBH4P6C9+8TGr3vMC5wCvgU6Z7bxdJ+by/0ms+J595WX5DSfpw1wnAZiAXONJatNoiSr5DTOZwQ27DvtupZyPi6JgtERTOrflJZVi/tsfo8n2rfSCW5cOu4MafcHuOXPEJYuc73HE6qjCIhAsBOwSZhYzFJawg/OeWnxNiYt3+sqlDLzkRa0qZ5B4P2bl+GbfznWdHsTGpn3bGoiIAIiIAKBImCTjvA0wPGZ+xqKKT4enw7cSqAmUNJLoAOAKYAJnvwl3dhfAxwXgIKACX6sA/7hvh7j5VJIlHhLTP19SuDL7ccY/u5G4pNSKFkwmmkDm1O7XCGfrpHpZCZJ26pxsPQFp9xedGG4bwLU6pTpUHUQAREIbQI2CROLSUtL+ME5prpYz/GrXDmqzPflklHtXH9f1VJSYNZDsPNTCI+GRz6Hcg39YJ2WEAEREAERyIiATTrC0wCHuZ5SCiidwYZMotGegPn2SfDC5SZ3RxvA1KDcm27ck+4cH6uAM4A5o2h+ZrJJDQKmZrKOCYqYP782Y/fGQ4cOUa6c0SdqIuA/Ah9sOMCf5m3BJFGrXDw/0we1oGJxEyv0Y4s7Dwseh+0LnUVL14MHpkOxqn40QkuJgAjYSsAmYWIrI9DLEn/5Zv+pWLqMXc6F+CTa1yzJ1AHNrq0wdvksTLwZzuyFwhVhyDcQE4BTkf6ConVEQAREwGICNukITwMcP7urnGSUCXE60A8oCpz1kLsJWOwAvnSfDPFkmPnW2gqYazE3ABevMyjDKiwKcHiCWX18SeCrHccYNHWDa8o65QoxdWDza99E+XLBjOY6vh1m94VTu51PG/Zx7ixHpstQn9N2aH4REAFrCdgkTKyFpACHX12zaPNhRr7/vWvNP3W6kWEdzPuwdO3Yj05llcRYJ5dU33m6bulXL2kxERABEXAI2KQjPA1w+PoEx7/d1VMeAmZ58WCYUrMmeHEX8Pl1xukEhxdQ1TVnCBw4HUvXcSs4dzmRRhWLMH1QcwrmTVsNOWfWvWrWLR/CwpGO+AuPgk7/hiYDVALWD+i1hAgEEwGbhInF3HRFxc/OeXbeD7y/7gARYXmYM6wVjSuad2npmvmem/uI88O2v4PbjUxUEwEREAER8CcBm3SEpwEOX+bgiAAOAubv8hnk9LieL/q7r6d4W7VFosSfT7jWIj4pmV7jV7vuEBePiWLxqHaUKWwOH/mpJSXA5/8H6yY6Cxa+AXpNg/JN/GSAlhEBEQgmAjYJE4u5SUv42TmXE5Lp9sYKdh67SIWi+VzfpYXzZfCi4LP/g9WvO9b1mg61u/nZUi0nAiIgArmbgE06wtMAR2ZVVJYBnmYqvBeYB4xx59Xw5mn41Y7b3ddbPB0rUeIpKfXzCYHnF2xl+upfXFngZwxqQdsafiy/eu6QUyXloMnLa7Lc3AY93ob8xXyyN00iAiIQegRsEiYW05WWCIBzdh67wD2vryAuMYVOdcvwZp/G5DFfrmlbchLM6A77lkNUARj8FZTMoMRsAOzXkiIgAiKQGwjYpCM8DXDUAzYD84EeaZw0EhjrzsEx0/1zc0nShNdNjo2M2sdAF6A+YK6+pG/mZIcpIXsu3Qcm78YmINWdg+OyFw+LRIkXsNQ1ewQWbj7MKPe94d/dXpMnbq+RvQm9Gb3nW/hwEMSedEZ1eMb5oxKw3lBUXxHIdQRsEiYWw5eWCJBzZq3b70rWbdqL3evSt2Wlay25eAImtIcLh6F4DSfIkdfP1coCxEfLioAIiECgCdikIzwNcBhm44AR7iDHEneVk1GAKRN7K5DiBrsPMN88Gc1txMF+4DugxW84ooi7qspHwPY0VVQeBQoAJm/HB146UaLES2DqnjUCu4+bN00riU1I/u3M71mb+vqjTAnYlaPhy79DagrkLQL3TYKad+bEappTBEQgxAjYJEwsRistESDnpKamMmrWJkzi0aiIMBY83oabymYQvDi4AaZ0guQEuLEr9JoBYWEBslrLioAIiEDuIWCTjvAmwBHuvlIyBKgMmFfEs4Hn01U0uV6A48/AS4CZY9JvuNyUm33DHQCp4A5qmLVMIMUkJ3Wfu/fqgZEo8QqXOmeFQGxCEt1eX8mu4xcpWziv665wsZiorEzl3Zi4czD/MfhpsTOubAPnDnJR87+pmgiIgAhkTsAmYZK5tQHrIS0RMPRwIS6RLmNXsP90LNVKxrBoZFvyR5lDv+nad1Nh0RPOD297Hto9FUCrtbQIiIAI5A4CNukIbwIcwewdiZJg9l4Q2G7eLv1+zmbmf3/Ile199tBWNKmUQbZ3X+/l6FaY0w9O73FmbvwwdHoVIv2Y0NTXe9J8IiACfidgkzDx++Y9X1BawnNWOdLzh4Nn6fHWKhKTU+nZpAKv9myQ8TqmetjG6c5h4r5zofptOWKPJhUBERABEXAI2KQjFODQUykCPiDw3tr9/Hm+cz/4+a61GdS2ig9mzWSKzbNg0ZOQdBnCo6HLf6Fxv5xfVyuIgAiEHAGbhInFcBXgsMA5by/fw4uLzQ1mGP1AQ7o3MgX50tWHJsQAACAASURBVLXEOOeqyuGNkK8oDPlGpxot8J1MEAERCF0CNukIBThC9znTzvxEYMvBc643SgnJKXSuV4Y3emeQ4d2XtiTFw6d/gg2TnVmLVIIHZjhXU9REQAREIAsEbBImWTDfX0MU4PAX6eusY05MPjJtA1/tOE5MVDgfj2pHlRImN326du4gTOjgJN0uUw8GfQ5R+S3YgUwQAREQgdAjYJOOUIAj9J4v7ciPBM7FJtL19eUcOH3ZJbAWjmhDwbymiFAOtbMH4IP+cMjk6QVq3An3TXTeUKmJgAiIQBYJ2CRMsrgFfwxTgMMflD1Y4/SlBDqPWc7R83HULV+IuY+1JjrCpIpL1/Yug+ndnOTb9R+Ee8fjqt+uJgIiIAIi4FMCNumI3PJbXqLEp4+wJjMEzFukwdO/Y+n2Y0RHhPHRb2V19xWu3V/C3Efh8mnnXvEtf4Z2f1CGeF/x1TwikIsJ2CRMLHaDtIRFzlmz5xS9J60hJRUGtK7MC/fUydi6VePg8+eczzr/B5oPtmgXMkUEREAEQoOATTpCAY7QeKa0iwAQmPDtz/zrkx2ulV+9vz49m96QM1akpMDy/8LXpgBRKuQrBj3eVtK0nKGtWUUgVxKwSZhY7AAFOCxzzuilOxm9dJfLqkkPN+WO2qWvtdCUUf9wEPw4D8IioP/HUKmVZTuROSIgAiIQ3ARs0hEKcAT3syTrA0RgrXlz9PZaklNS6dW0Av++P4fyX1w+A/OHwc5PnZ2Wawy9pkGRigHauZYVAREIRQI2CROL+SrAYZlzzHewOcWxdu9piuSPZMmodpQrku9aKxMuwdu3w/FtEFMKHvkcivkhGbhlvGSOCIiACOQUAZt0hAIcOeVlzRuyBE5ciKfL2OUcvxDPjWUKuq6m5I3M4O5vdgkc2Qyz+8HZX5yZmg6Cji9DRHR2Z9Z4ERABEbiKgE3CxGLXKMBhoXOOnouj89jlmLwczSoX5f3BLYkID7vW0lM/w6RbIO6cU1Fl0GdQsIyFO5JJIiACIhB8BGzSEQpwBN/zI4sDSMC8Ler79lpW7zlFwegIFo5sm3H29uza+P1MWPwUJMVBRF7oOhoaPpTdWTVeBERABDIkYJMwsdhFCnBY6pyvdxxn4NT1LutG3lqdp+6slbGl+9fA9O5OefVStWHAYshfzNJdySwREAERCB4CNukIBTiC57mRpRYQ+M9nP/H617tdlozv25iOdcv61qrEOPjkj7BxmjNv0SpOCVhT4k5NBERABHKIgE3CJIe26ItpFeDwBcUcmuOlxduYtHyvq0jKzEda0KZ6iYxX2r0U3nsQUhKhfFN4eAFEF8ghqzStCIiACOQOAjbpCAU4csczp136gMDXPx1n4BTnDdEjbavwl661fTBrminO/AJzHoYjm5wf1uoM3d+CfEV8u45mEwEREIF0BGwSJhY7RwEOi52TkJRCz/Gr2HzwHCULRvPJE+0oUeA3rnT+OB8+GOgk7q7SAfp8oOufFvtWpomACNhPwCYdoQCH/c+LLLSAwMEzsXQdt4KzsYk0qVSUWUNaEpnRHd+s2rrrC6cEbNxZyBMGt/4F2jypErBZ5alxIiACXhGwSZh4Zbh/OyvA4V/eXq+2/1SsK0fWhfgk2tcsydQBzQgL+w2p+91UWPSEs8ZNd8P9UyE8wus1NUAEREAERABs0hEKcOiJFIFMCLjeCk1YzeYDZykWE8XiUW0pWziDLO1ZIWlKwH77ivPHvEnKXwLufweq3pyV2TRGBERABLJEwCZhkqUN+GeQAhz+4ZytVRZtPszI9793zfFspxsZ2qHab8+3cgx88bzzecO+cM84vVjIFn0NFgERyK0EbNIRCnDk1qdQ+/aYwAsLf2Tqqn2ue73TBjZ3vRXySYs9DfMGg7kPbFqFZtBzGhQu75PpNYkIiIAIeErAJmHiqc0B6KcARwCgZ2XJZ+f9wPvrDhARloc5w1rRuGLR355m6Quw4jXn85aPw10v4frCVxMBERABEfCYgE06Irf8Bpco8fjxVMe0BNK+CXry9ho8eXtN3wA6tBHm9Idz+535mg+BO1+CiCjfzK9ZREAERMALAjYJEy/M9ndXaQl/E8/iepcTkun2xgp2HrtIhaL5WDyqHYXzRWY8W2oqLP49bJjsfH7Lc9Dh6SyurGEiIAIikDsJ2KQjFODInc+gdu0BgZ9PXOSecSu4lJBMuxolmDqwOeG/dZfXg/lcXYyQMhVSljwNyQkQmR/uHgP1e3k6g/qJgAiIgM8J2CRMfL45302oAIfvWOb4TDuPXeCe11cQl5hCp7pleLNPY/L81smMlGTnROXWuY5dnf8DzQfnuI1aQAREQARChYBNOkIBjlB5qrQPnxKITUii+xsrXW9/yhTK68q7Ufy3srF7unLiZVj8B9g00xlRvDr0mgGlfVyNxVN71E8EREAE3ARsEiYWO0UBDoudk5Fps9bt50/ztrg+erF7Xfq2rPTbO0hKgFm9YfcXTp/7JunlQ5D5W+aKgAgEjoBNOkIBjsA9B1rZUgKpqak8NWcz874/5Lq/O3toS5pUKpY9a0/vhTn94KgjtFwZ27u9CXkLZW9ejRYBERABHxCwSZj4YDs5NYUCHDlFNofmNd/no2Ztwlw3jYoIY8Hjbbip7HW+dxNiYeZ9sH815AmHB9+FWp1yyDpNKwIiIAKhQ8AmHaEAR+g8V9qJjwi8v24/z7rf+DzX5SYebVc1ezP/9CnMHwJx5xzBdPsL0Hqkkphlj6pGi4AI+JCATcLEh9vy9VQKcPiaqB/muxCXSJexK9h/OpZqJWNYNLIt+aOuUw728lmY1tV5IREeDX3nQpV2frBUS4iACIhA8BKwSUcowBG8z5EszwECWw+d4763VmFKw3asU4a3+l7nzq4n62+cAQtHOiVgY0pBzylQua0nI9VHBERABPxGwCZh4rdNe7+QAhzeM7NixA8Hz9LjrVUkJqfSs0kFXu3Z4Pp2XTwBUzrCqd0QVRD6L4Tyja3Yi4wQAREQARsJ2KQjFOCw8QmRTQEhcO5yInePc97yVC6en4Uj21Io729kXffEwvXvOJnZTSvfFB6YCYXKejJSfURABETArwRsEiZ+3bh3iynA4R0vq3q/vXwPLy7e7rLpvz0b0KNJhevbd/YATL4Lzh+CfMVg0KdQspZVe5IxIiACImALAZt0hAIctjwVsiOgBMw93aEzvuPzbceIjghj/vA21C6XjfwYa8bDp884e6rUBnrPhuiCAd2jFhcBERCB3yJgkzCx2EsKcFjsnMxMM9/zg6dvYOn24+SLDGfBiDbULJ3J9/KJnc5JjthTUKi8E+QoUjGzpfS5CIiACOQ6AjbpCAU4ct3jpw1nRGDSsj28tMR5s/PvHvXp1eyGrINaOQa+eN4ZX6UDPPQ+RMVkfT6NFAEREIEcJmCTMMnhrWZnegU4skPPgrFnYxNc+TgOnb3sysexcERbYqKvk4/D2Hx4E0ztCgkXoFg1J8hRoJQFu5EJIiACImAPAZt0hAIc9jwXsiRABNbvO82DE9eQnOLh3dzr2fntq/D1i06P6rc711Ii8wVoZ1pWBERABDwjYJMw8czigPRSgCMg2H276KYDZ+k53snH0a1hOUY/0JA8eTKRw/tWOtVVkuKgdD0Y8DHkK+JbwzSbCIiACAQxAZt0hAIcQfwgyfTsEzh5MZ4uY5dz7Hw8N5Yp6Lqaki8q3PuJU1Ph63/Csn87Y2t2gl7TICLa+7k0QgREQAT8TMAmYeLnrXuznAIc3tCyuO/UlXt5YdE2l4X/vLcevVt4cO1k52cwqzekJMENLaHffIjKb/EuZZoIiIAI+I+ATTpCAQ7/+V0rWUbAnNh4ePJaVu4+RYHoCBaOaEPVkgW8t9IEN5b+FczVFNNuugd6vAMRUd7PpREiIAIiEAACNgmTAGzf0yUV4PCUlOX9TD6OEe99z+ItR4iKCGPeY62pW75w5lb/8AHMG+xURjOnNB98X9/1mVNTDxEQgVxAwCYdoQBHLnjgtMWMCfy/z39i7Fe7XR++2acxnetlocKJCW589mdY86azSN0ecO9ECM/kTq+cIgIiIAIWEbBJmKTBEgY8AQwFKgMngDmASXJ0yQN8JmI9CnjIPT4e2AlMBKY5/0r1qinA4RUuuztfiHMqp+07FUvFYvn5eJSHldPWvw2Ln3I2V+de54VGWBZOftqNR9aJgAiIgFcEbNIRCnB45Tp1DhUC3/x0nIFT12PiEwPbVOavd9fxfmspKfDJ02DEjmkNHoJub0joeE9SI0RABAJMwCZhkgaFORZnAhTzgU/M+ThgJLAcuB1IuQ42Exz5FmjtDmasAcx9AhPsaG7ySQPuUlcew1eAw2NUwdFx2+HzdH9zJQlJKXSsU4a3+jbOPB+H2dqy/8BX/3A22WQAdB0NmeXxCA4kslIEREAEskTAJh2hAEeWXKhBwUzAZE/vOnY5Z2ITaVSxCLOHtHIdUfWqmeDGx0/AxunOsMYPQ9cxEOblPF4tqs4iIAIikDMEbBIm7h2aqPMWd3CjR5pdmwDHWKAP8N51aLQCVgGjgd+l6WfuDu4AigHeZolUgCNnHr+Azjpr3X7+NM88avB819oMalslc3vM25HPn4PVrzt92zwJd/wt83HqIQIiIAIhSsAmHaEAR4g+ZNpWxgTMW5peE1ZjsqgXzR/J4lHtKFfEyyonKcmw4HHY/L6zSLNHodOrCm7ooRMBEQhaAjYJEzdEU47q/4D27hMbv7LNC5xyn87ofB3gdwGfAn8EXk3Xbx1Q3v3HG58pwOENrSDpa/JxPDVnM/O+P0REWB7mDGtF44pFM7feBDkWjoTvZzh9b38B2qaNpWU+hXqIgAiIQKgQsElHKMARKk+V9uERgb8t+pEpK/e5TpJOHdicDjVLejTuf52SE2H+UNg61/lRy8fhrpd0NNU7iuotAiJgGQGbhIkbzWfuayjmWonJnZG2rTS1qoDr/QI3/0LdAyQBw4G17isq/d1Bj2HAJC/doACHl8CCpXtsQhLdXl/JruMXKVc4r+vlR9EYDxKFmxceHwyA7QudrZqrKk0HBsu2ZacIiIAI+IyATTpCAQ6fuVUT2U5g8Q9HePy9jS4zR91Wg9/fYfSxFy0pAeYOgu2LnEHmTc1tf1VwwwuE6ioCImAnAZuEiZuQuTNQCiidATGTaLQnYOpwJ1yHaDvAJElK+8v+AvAw8JEHnigImD+/NmPLxkOHDlGunIl1qIUSgd3HL3D3uJVcTkzmllolead/M8LCPJDJSfHw/oPw81dAHrj/HSfhuJoIiIAI5CICNukID35zh4Rn9NYlJNyY9U3sOXGRe15fycX4JNpWL8G0Qc0J90S4/LqkETBz+sNOk+cO6PAM3PysghtZd4lGioAIWETAJmHixvIzEAlUzACTSX7UDzCnNM5eB2Mj4Dn3SQ6Tj8Pk3XgcuBHoBnyRiQteAP6avo8CHBY9uD42Zf73B/nd7M2uWf/YsRbDb67u2QoJl2B6dzi4DsIi4KHZUMPkwVUTAREQgdxBwCYdoQBH7njmcvUuLyckc++bK9lx9AKlC0W7jp6WKGBe/HnYEi/D7L6we6kz4NbnoP3THg5WNxEQARGwn4BNwsRNK7snOOoBJteGSYowPo0HzJWXrYDJCF0NSL6Od3SCw/5H1+cWPjtvC++v2495B/Le4Ja0rFrcszUun4EpXeD4jxCRD/rNh0om162aCIiACIQ+AZt0hAIcof+85eodmuRhf/jgB+ZuPOg6sTF7SEuaVjYv8Txs5q3M+w/BXlNtELjjH9DGVC1UEwEREIHQIWCTMHFTzW4OjsmASYZQwp2UNK2zxgEjAPN63pwU8bTpNKinpIK4X1xiMve9uYptR85TqqDzUqRkQQ9filw4CpM7wpm9EF0YBnwMZesHMQ2ZLgIiIAKeEbBJRyjA4ZnP1CtICcxev59n5jrl357rchOPtqvq+U7iL8B7D8AvJp8d0PEVaGny0qmJgAiIQGgRsEmYuMlmVkVlGdDpOl4wAZI73Tk8jqfr9xZgfpmbqyo/eeFJBTi8gBXMXfedvMTd41ZwIT6J1tWKM+ORFp5faz2zzwlyXDgCMSVh4KdQwsOrLsEMTbaLgAjkagI26QgFOHL1oxjam//x8DnXW5j4pBTuqlOa8X2bkMeUT/GkxZ2Dmfc792lN6/oaNB3kyUj1EQEREIGgI2CTMHHDM1dMTDKE+UDajI0jgbHuHBwz3X3NVROTr2NHGvCvAU8CzwD/TvPzIsA2wJSbNVVYrndFJb0fFeAIuic76wZ/suUIj72bxcTkx7fDlE5grq0UvgEGfQqFK2TdGI0UAREQAcsJ2KQjPPzXnuVEMzdPoiRzRiHV43xcouvtyy+nYqlUPD8LR7SlcD6jfz1oRpDMuA8OG2GTB7q9Do36ejBQXURABEQgOAnYJEzSEPz1KokJciwBbjJFsABzrO5WIMXddx9QyfmF/b9m/tv8EjeJSN91jzH3EwcDld3JRt/00lvSEl4CC/buaUvLTxvYnPbelJY/+B1MvwcSLkKJmjDwE4gxN6bUREAERCD0CNikIxTgCL3nK9fvyOTdGDbzOz778RhREWHMH96aOuUKe8bl0imY0Q2OboE8YdB9PDR4wLOx6iUCIiACQUrAJmGSBmG4+xTGEHdQ4iQwG3geuJimX0YBDvOxOdlh+t7mvqpyGdgEjAbmZcFVCnBkAVowD0lISqHnhNVsPnCWYjFRLBnVjjKFzeEfD9ueb+HdnpAcD2UbQv9FkLeQh4PVTQREQASCh4BNOkIBjuB5bmSphwTeXr6HFxdvd/V++b56PNg8oyqDGUx28ThM7wbHt0GecOjxNtS9z8NV1U0EREAEgpeATcLEYooKcFjsnJwy7eCZWLqMXcG5y4k0q1zUVVklMtwU4fGw7VgMs/tBajJUagt9P4TIfB4OVjcREAERCA4CNukIBTiC45mRlR4S2LDvNA9OXENSSio9GlfgPz3re5Z34/wR5yjpyZ0QFgk9p8JNXT1cVd1EQAREILgJ2CRMLCapAIfFzslJ077acYxBUze4lhjavirPdja3pbxom96Hj9xJymt2hAdmQriH12a9WEZdRUAERCBQBGzSEQpwBOop0Lo+J3DqYrzrLcvR83HUKl2Qjx5vQ74oc8I5k3buIEy7G07vgfAoR3jUvCuzUfpcBERABEKGgE3CxGKoCnBY7JycNu2VT3fw1jdOVeFJDzfljtqlvVtyzXj41OS8Ber1gnsnQJgXJ0G8W029RUAERMCvBGzSEQpw+NX1WiynCCSnpDJgyjqW7zpJTFQ4C0e2pVrJApkvd+YXJ7hx9heIyAsPvgfVzXVtNREQARHIPQRsEiYWU1eAw2Ln5LRpSckp9J60lnX7TlMobwSLR7XjhmL5vVv2m5fhm385Y5oNhs6vgqfV3bxbSb1FQAREwK8EbNIRCnD41fVaLKcIvPbFTsZ8ucs1/eu9G9G1vtGhmTRzYmPaPXDuAETmh4dmQdUOmY3S5yIgAiIQcgRsEiYWw1WAw2Ln+MO0Y+fj6DxmOacuJdCgQmHmDGtFdIQHJ0V/NS41FT79E6wd7/yk/dNw63P+MF1riIAIiECOErBJRyjAkaOu1uT+ILBs5wn6T1mH0Q0DWlfmhXvqZL7syV3OyY0LRyCqAPT5ACq1znyceoiACIhACBKwSZhYjFcBDoud4y/TVuw6Sb/Ja73THGmNS0mBBcNh8/vOT+98CVqP8Jf5WkcEREAEcoSATTpCAY4ccbEm9ReBw2cv02Xscs7EJtLwhiLMGdrKVRr2uu34dufkxqXjEF0Y+s6FG5r5y2StIwIiIALWEbBJmFgH54pBCnBY7Bx/mjZ66U5GL3VOjb7RuzFd6pf1bvnkJJjzMPy02Bl3z+vQuJ93c6i3CIiACFhEwCYdoQCHRQ+GTPGOgKlP/+DE1Wzcf5Yi+SNd92HLF8mk9NrRLU4p2NhTkLcIPPwRlGvk3cLqLQIiIAIhRsAmYWIxWgU4LHaOP01Lm/erQHQEC0e0oaoneb/SGpkYB+/1hL3LIE8Y9JwGte/x5za0lgiIgAj4jIBNOkIBDp+5VRP5m8DfF21j8sq9rmWnDGzGLbVKXd+Ew9/D9O4QdxbyF4eHF0CZev42W+uJgAiIgHUEbBIm1sG5YpACHBY7x9+mnXRVblvOsfPx3FjGqdyWN9KLfBzG4PgLzonSwxudKm6950C1W/y9Fa0nAiIgAtkmYJOOUIAj2+7UBIEg8MmWIzz27kbX0iNvrc5Td9a6vhkHN8CM+yD+HMSUgv4LoZSXdewDsVGtKQIiIAJ+IGCTMPHDdrO6hAIcWSUXouPW7T3NQ5PWYE50PNjsBl7uUd/7ncaehimd4MQOiIxxXr7o2qz3HDVCBEQgoARs0hEKcAT0UdDiWSGw9+Ql7h63govxSbSuVpwZj7QgPOw6j/Ivq+HdnpBwAQqWhf6LoESNrCytMSIgAiIQkgRsEiYWA1aAw2LnBMq08d/+zMuf7HAt/9+eDejRpIL3ppw/DJPvgrP7neuzA5dAaQ8Spnu/kkaIgAiIQI4QsElHKMCRIy7WpDlFIC4xme5vrGTH0QuUKhjtyrtRsmD0by+3dzm89wAkXoJCFZyTG8Wr5ZR5mlcEREAEgpKATcLEYoAKcFjsnECZlpKSyuDpG/hyx3HyRYazYEQbapYu6L05p36GyR2dBOgFSsOgT6FYVe/n0QgREAERCAABm3SEAhwBeAC0ZNYJ/PHDzczZcNB1YuP9wS1pXqXYb0/281fwfm9IugxFKkL/j6FopawvrpEiIAIiEKIEbBImFiNWgMNi5wTStLOxCXQZu4JDZy9TrWQMC0e0JSY6wnuTjm6FqZ0h7hwUqQSDPoNCXlZo8X5VjRABERCBbBOwSUcowJFtd2oCfxGYs/4Af5z7g2u5P3e+kSHtr3MSY+fnMLsvJMc7b0DMtZTCWTg26q/NaR0REAERCCABm4RJADFktrQCHJkRysWfbzpwlp7jV5GYnEq3huUY/UBD8uTJgsw+sM6p9pYYCyVvcq6r5L/Oy5xczFxbFwERsIeATToiC7957QHphSUSJV7AsrHr9iPnXVdT4pNSuKN2aSb2a/LbwmH7x/DBAEhJhBI14eGFegNio1NlkwiIgDUEbBIm1kC51hBpCYudY4NpU1fu5YVF21ym/PPeevRuUTFrZpkTqO/2cnRM+SaOjokukLW5NEoEREAE/EDAJh2hAIcfHK4lskfAJBO9Z9wK9py8xA3F8vHxyHYUzheZ8aQ/zoe5j0JKEpSq7WQjL5BJ+djsmafRIiACIhD0BGwSJhbDVIDDYufYYFpqaiqPv7eRJVuOEhURxrzHWlO3fOGsmfbjR/DhQEhNgSodnBKykXmzNpdGiYAIiEAOE7BJRyjAkcPO1vTZI2DEwqhZm1i0+TBR4WHMG34dsfDDHJg/1BEDZepBvwUQUzx7Bmi0CIiACOQCAjYJE4txK8BhsXNsMe18XKLrpcy+U7FUKp6fRSPbUijvb7yUyczojdNh4Uin141docc7CnJkxkyfi4AIBISATTpCAY6APAJa1FMCM9f8wnMfbXV1/0e3OvRrVTnjod+/CwseB1KhXCPoO093Vj2FrH4iIAK5noBNwsRiZyjAYbFzbDJt2+HzdH9zJQlJKXSsU4a3+jbOWj4Os6lV4+Dz55ztlbwRur/pXFtREwEREAGLCNikIxTgsOjBkClXE9h66Bz3vbmKhOQUutYvy7iHGmUsEDZMgY+fdAZXaA59P4S8WTwSKieIgAiIQC4kYJMwsRi/AhwWO8c202at28+f5m1xmfV819oMalsl6yauGA1f/h1SkyFPGLR5Am5+FiKisz6nRoqACIiADwnYpCMU4PChYzWV7wiYI55dx65g/+lYqpQwJdfaUDCjI55rJ8InTzsLV2wNfeZAdBbqz/vOdM0kAiIgAkFHwCZhYjE8BTgsdo5tppkrtk/N2cy87w8REZaHOcNa0bhi0aybefh7+Gg4HHeSmOo0R9ZRaqQIiIDvCdikI7wJcIQBTwBDAXNP4AQwxwSmgUuZYHoB+Ot1+iQB6S8o1gJeAToAUcBG9xxfZcElEiVZgBaoIUYUDH93I59sdZJ0zR/emjrlMjiRkfbYZpX28NAsiIoJlNlaVwREQASCloBNwsRiiNISFjvHRtNiE5Lo9vpKdh2/SPkiJkl6W4rGGEmbxZYUD9/+G1a85j7NEQ5tn4QOz+g0RxaRapgIiIBvCNikI7wJcIwBRgHzgU+AmwCT+Wg5cDuQch089QHzJ30zPzOv382c96X5sBqwDjCBj9HAOWAwUBfoBCz10hUSJV4CC2R3j8qsLfsPfPUPx8xqt8GD70JkvkCarbVFQAREIGgJ2CRMLIYoLWGxc2w1bdexC9zz+kouJyZzS62SvNO/GWFh3sjvDHZ2aKNzmuPEdudDUzWu2xtQvrGtGGSXCIhAiBOwSUd4+hu2DmAuEppARI80/jEBjrFAH+C9LPhtAjAE6AosTjPenAwx65gsSpvcPzcFwH8E4kwuaSebpMdNosRjVIHtuPnAWe4fv4rE5FS6NyzHaw80vDrvRmoqfPMyfPuyY2jNjtBzmrKKB9ZtWl0ERCDICdgkTCxGKS1hsXNsNm3+9wf53ezNLhP/2LEWw2+unn1zXac5XgGTn8OVm0OnObIPVTOIgAhklYBNOsLTAMeLwP8B7d0nNn7duynIfQr4FujsJRBzl+AwcN595SXZPd783My5Ergt3Zx/Af4OtHCf8PB0SYkST0kFsN+52ES6jFvOwTOXqVbS5N1oS0x0xBWLTHDjy785RzNNu+lu6DEZIrJx3DOA+9XSIiACImALAZuEiS1MMrBDWsJi59hu2rPztvD+uv2YwxvvDW5Jy6o+KmPvOs3xGJzY4SAwpzlMpRVTUU5NBERABPxEwCYdluOQPgAAIABJREFU4WmA4zP3NZT8QHw6TiYQUdOkO/KS3wBgCmCCJyZw8WtrZYpiAS8B7rpY//vsDuBzYATwhhfrSZR4ASsQXU3ejSEzvuOLbcfIGxnGgsfbUqtMmmShJrhhyqStft0xr24PuHcChGextnwgNqk1RUAERMBSAjYJE0sRGbOkJSx2ju2mxSUmuyrDbTtynlIFo1k8qh0lC/qoCoo5zWFOt640pzlSnNMc7X4P7f+ol0C2PxiyTwRChIBNOsLTAIe5nlIKKJ2BD8x1kp6A+S2d4IWPTO6ONiaDArA3zThzNeVDYDjwVrr5aruvqfwL+PN11jL/Mk5bSsPYvfHQoUOUK2f0iZptBN5evocXFzt3Sf99f316Nb3hahPTJhSt/6DzdiIs3LZtyB4REAERCEoCNgkTiwEqwGGxc4LBtH0nL9F13AouxifRpnpxpg9qQXh283Gk3fjB75zTHCd/cn5aqo77NEfDYMAjG0VABIKYgE06wtMAx8/uKicVM+A+HegHmNpXZz30i6mQYs7Sfek+GZJ2mJnLzPkIMDndfFUBY4tJePrkddbKsGqLAhweesfP3TbuP0Ov8atJSkmlR+MK/LdXg6stMMcv37kTUhKhXi+4d7yCG372kZYTAREIbQI2CROLSSvAYbFzgsW0T7Yc4bF3TWFAGHVbDX5/hzkE7cOWGOfkKVs5Js1pjqeg/dM6zeFDzJpKBETgagI26QhPAxy+PsHxb3f1lIeAWekeEJ3gyEX/x5y5lOB6m3Ho7GVqlCrAghFtyB+VJu9G/AWY0B5O74EStWDINxBlbkqpiYAIiIAI+IqATcLEV3vKgXkU4MgBqLlxyr8t+pEpK/eRJw9MG9ic9jW9veXtATWd5vAAkrqIgAj4ioBNOsLTAIcvc3CYf70eBMzf5TPI6aEcHL560iyfJyUllUenb+CrHcfJFxnOwhFtqFE67c0iU7fnMdj8HoRHweCvoEw9y3cl80RABEQg+AjYJEwspqcAh8XOCSbTEpJS6DlhNaZyXLGYKJaMakeZwiZvv4+bOc3xzb9g1VjnNEdYBLR7Ctr9Qac5fIxa04lAbidgk47wNMCRWRWVZUAnDx17LzDvOtdMTDnYk5lUUWkJrPVwPdNNosQLWP7qOv7bn3n5Eyfr9//r1YD7Gle4euktH8Jcc1MJ6PgKtBzmL9O0jgiIgAjkKgI2CROLwUtLWOycYDPt4JlYuoxdwbnLiTSrXNRVWSUyPCxntnFwgzs3x05n/tL1nNwcZevnzHqaVQREINcRsElHeBrgMK/NTQHv+YC5QvJrGwmMdefgmOn+oUkaakpbuOtVXePfj4EugPmtaq6+ZNQ+AO4DGrvXNX1M4ONH94kPk8Mj1YsnR6LEC1j+6Lp+32kenLiG5JRUHmh6A6/cn+5L9vRe52pK/HmocRf0no3rLKeaCIiACIiAzwnYJEx8vjnfTSgt4TuWmskkott+jEembXCxGNqhKs92uinnuJjTHF+/5FSj+99pjj84JzoionJuXc0sAiKQKwjYpCO8+RfjOHd5VhPkWAKY38Kj3CctbgVS3N7bB1QCMprbiIP9wHdAi+t4uzqwDkgEXgPOA4MBE2gxwRFzZcabJlHiDa0c7nvqYrzrrcXR83HcWKYgHz3ehryRaSqiJCfC5I5waAMUKA2PrYKYEjlslaYXAREQgdxLwCZhYrEXpCUsdk6wmmZOspoTraZNergpd9TOqGChD3d3YL1zmuPULmdSnebwIVxNJQK5l4BNOsKbAIf5F6ipXDIEqOy+RjIbeB64mMad1wtwmNKuL7nnmJTJI2ACKC8DHQATWjYpp011lKVZeHQkSrIALSeGmLwbA6auZ9nOE8REhbNoZFuqljSHc9K0L/8Oy//rxMj6zYdqt+SEKZpTBERABETATcAmYWKxU6QlLHZOsJqWlJxC70lrWbfvNIXyRrB4VDtuKJbDydQTL7tPc7xx5TSHqbJiTnOEm0PYaiIgAiLgHQGbdIQ3AQ7vdmlXb4kSS/zxxte7efUzpz772IcacU8D45o0be8ymHaPcwOpzRNwx98tsVxmiIAIiEDoErBJmFhMWVrCYucEs2nHzsfRecxyTl1KoEGFwswZ1oroiDQnW3NqcwfWuU9z7HZWMIncu7+lhO45xVvzikAIE7BJRyjAEcIPmm1bW7PnFL0nrSElFfq0qMhL96ariHLpFIxvAxeOQLlGMOhz3Qu1zYmyRwREICQJ2CRMLAasAIfFzgl201bsOkm/yWtJTYUBrSvzwj11/LMlc5rjqxdh9RvOyyVTaaX9H6Hd73Wawz8e0CoiEBIEbNIRCnCExCNl/yZOXDB5N5Zz/EI8dcoVYu5jra/Ou2G+0Wf1hp+WQFQBGLoMipt8tWoiIAIiIAI5TcAmYZLTe83G/ApwZAOehmZOYPTSnYxe6uTGeKN3Y7rUL5v5IF/12L8WFgyHU7+e5qjvPs1R11craB4REIEQJmCTjlCAI4QfNFu2Ziql9J+8jhW7T1IgOoKPR7alcomYq81bNwmW/MH52b0ToMGDtpgvO0RABEQg5AnYJEwshq0Ah8XOCQXT0uulhSPaXJunLCc3es1pjkjo8Edo+zud5shJ7ppbBEKAgE06QgGOEHigbN/CmKW7eG2pU3v9zT6N6Vwv3RuJY9tg0i2QFAf1ekGPzPLP2r5j2ScCIiACwUXAJmFiMTkFOCx2TqiYdtJVaW45x87HZ1xpzh8b3b8GPhoOp53qLpRt4JzmKO2nazP+2KPWEAER8CkBm3SEAhw+da0mS09g5e6T9H3HuVPav1Ul/tYt3VFH87Zg4i1wYjsUrQxDl0PeQgIpAiIgAiLgRwI2CRM/btvbpRTg8JaY+meJwLq9p3lo0hrMiY4Hm93Ayz3qZ2mebA1KiHVyc6x5052bw5zmeAbaPqnTHNkCq8EiEJoEbNIRCnCE5jNmxa6Om6zgY5dz8mIC9SsU5oOMsoJ//HvY8I6T1GrQZ1ChqRW2ywgREAERyE0EbBImFnNXgMNi54SaaeO//ZmXP9nh2tZ/ezagR5MKgdniL6ud3Byn9zjr6zRHYPygVUXAcgI26QgFOCx/WILVPFPX3ZzcWLPnNAXzRrAko7ru2z+G2X2cLd72Vydjt5oIiIAIiIDfCdgkTPy+ec8XVIDDc1bqmU0CKSmpDJ6+gS93HCdfZDgLRrShZumC2Zw1i8Ndpzn+AWveunKa4+ZnoI3JzRGRxUk1TAREIJQI2KQjFOAIpSfLor389/OfGPeVk4l7Qr8m3FWnzNXWnTvklIS9fAaqtId+CyAszKIdyBQREAERyD0EbBImFlNXgMNi54SiaWdjE+gydgWHzl6mWskY5g1vQ+F8kYHb6i+rYMHjaU5zNHTn5qgdOJu0sgiIgBUEbNIRCnBY8UiElhHLdp6g/5R1rrwbj7Stwl+6pvviS0mG6d1g33LIVwweWwWF/FgKLbRwazciIAIikG0CNgmTbG8m5yZQgCPn2Grm3yCw6cBZeo5fRWJyKjVLF2DKwOaUL5IvcLzMaY4v/w5rxzunOcKjnNwcbUxuDp3mCJxjtLIIBJaATTpCAY7APgsht/rRc07ejdOXEmh4QxHmDG1FVES6kxnLXnUSV5n20Cyo1SnkOGhDIiACIhBMBGwSJhZzU4DDYueEsmkLNx/mqTmbXEGO0oWimTygGXXKFQ7slvetdE5znNnr2FGukXOao9RNgbVLq4uACASEgE06QgGOgDwCobmoybvRe9Ja1u077TpCuXhUWyoUzX/1Zg+sg8kdITUZmg+Bzq+GJgztSgREQASCiIBNwsRibApwWOycUDdt1e6TDJ3xHRfikygQHcGbfRrTvmbJwG474VKa0xw4pzlu/hO0fkKnOQLrGa0uAn4nYJOOUIDD7+4P3QVf+XQHb33j1Ex/p39Tbrup9NWbjTsH49vC2f1Qqg4M/goi84YuEO1MBERABIKEgE3CxGJkCnBY7JzcYNpPRy8wYMo6jpyLIyIsD/+6rx49m94Q+K27TnMMhzP7HFvKNXaf5rgx8LbJAhEQAb8QsElHKMDhF5eH/iJf7zjOwKnrXRsd2r4qz3ZOd0TRJOSY+whsnQsReWHIt1BKX3yh/2RohyIgAsFAwCZhYjEvBTgsdk5uMc1cBTZBjh1HL7i2/OTtNXjithrkyRNgSW9Ocyz9G6yb4LjCdZrjWWg9Sqc5csvDqX3magI26YgA/zb023MgUZKDqA+fvezKu3E2NpEmlYoya0hLIsPT5d34/l0num9al/8HzR7JQYs0tQiIgAiIgDcEbBIm3tjt577SEn4GruUyJnAhLpHh725k+a6Trg69mlbgpXvrXau9AgFw73InN8fZX5zVyzeB21+Ayu0g0EGYQPDQmiKQSwjYpCMU4MglD11ObTMxOYUHJqxm4/6zFM0fyZIn2lG2cLrs3id3w4T2kHgJbuwKD8zUl1xOOUTzioAIiEAWCNgkTLJgvr+GKMDhL9JaJ1MCRn/9ae4W5m486Opr8nGYvBwmP0fAW/xFWPoCrJ90xZTSdaHFMKjXU9eTA+4gGSACvidgk45QgMP3/s1VM760eBuTljsZtKcObMbNtUpdvf+kBHjnDjiyCQqVh2ErIH+xXMVImxUBERAB2wnYJEwsZqUAh8XOyY2mpaam8trSXYz9cpdr+7XLFmLKwGaULmRJfjNzmuOrf8CBtVfck784NBkIzR6FQmVzo9u0ZxEISQI26QgFOELyEfPPpr7YdozB0ze4Fnv8lmo8fVcGOTU+fw5WjQPywICPoXJb/xinVURABERABDwmYJMw8dho/3dUgMP/zLWiBwRmr9/Pn+dvJTkllfJF8rmCHDVLF/RgpJ+6HNoIa8fD1nmQkugsGhYBtbtDy+FQoYmfDNEyIiACOUXAJh2hAEdOeTnE5z1wOpYuY5dzPi6JFlWK8e6jLYhIn3dj91KY2cMh0f5puPW5EKei7YmACIhAcBKwSZhYTFABDoudk9tN++an4zz+7kYuJSRTMG8EE/s1pVW14nZhuXAUNkyG9e9ArJM/xNUqNHOur9TuBuGRdtksa0RABDwiYJOOUIDDI5epU1oCCUkp9Jywms0HzlI8JsqVd+Oa45AXT8BbreHScajQHAZ+oizaeoxEQAREwFICNgkTSxEZsxTgsNg5Mg22Hjrnqmh34kI8UeFhvNqzPt0alrcPTWKcU1Vv7VtwdMsV+wqWc5LQmyssMZYFZ+yjKItEwCoCNukIBTisejSCw5i/LfqRKSv3uZJhzxjUgrY1SlxteEoKvNcLdn8B0YVh2HIoWik4NicrRUAERCAXErBJmFiMXwEOi50j0xwCB8/EMmDKenYfv+j672c63siwDlUDX0Y2IwelpsIvq2DNm/DTEkhNcXpF5IX6vaDFY1C6tlwrAiIQBARs0hEKcATBA2OTiZ9uPcKwmRtdJo26rQa/v6PmteatfhM+e9b5+f2Toa77mopNG5EtIiACIiAC/yNgkzCx2C0KcFjsHJl2hcC52ESGzNjA2r2nXT/s27IiL9xd59qrxDZBO/MLrJsIG2dA/LkrllVp7wQ6at4FYeE2WSxbREAE0hCwSUcowKFH02MC+0/F0mXcci7EJdG6WnFmPNKC8LB0j9CRzTDpNieJVMO+0P0Nj+dXRxEQAREQgcAQsEmYBIaAR6sqwOERJnWygUB8UjJ/+OAHFm0+7DLn9ptKMfahRuSPsqCM7PUAmRKzm993kpKe2n2lZ9HKTp6Ohn0gbyEbEMsGERABBTgC+gxIlGQTv/mivP+t1Ww5dI4SBaJZ8kRbShVMV4Ys4RJM6ACndkHx6jDkW4gukM2VNVwEREAERCCnCSjA4RFhaQmPMKmTLQRSUlJ55bMdTPh2j8ukBhUK886AZi4dZ30z151//hLWvOX8/WuLKgiN+kDzIVC8mvXbkIEikFsI2KQjdIIjtzx12dzn8wu2Mn31L5gDGzMfbUHraunybpj5F4yA72dAWCQ8uhTKNczmqhouAiIgAiLgDwI2CRN/7DeLayjAkUVwGhZYAtNX7+OFhT+SkgoVi+Vn6sBmVC0ZRC+gTvwEayc4JzsSY90w80DNjtByGFTpgCsxnJoIiEDACNikI3LLbwOJkmw87uZ448j3v3fNYHJumNwb1zRT2/zDgc6P73wJWo/IxooaKgIiIAIi4E8CNgkTf+7by7WkJbwEpu72EPhi2zFGvr+RuMQUiuaP5O3+TWlSqZg9BnpiyeUzsHE6rJsE5w5cGVGqNrQYCvUfgMh8nsykPiIgAj4mYJOOUIDDx84Nten2nrzE3eNWcDE+iXY1SjBtYHPC0ufdMImhxrdzkkJVuw36fAhhYaGGQvsRAREQgZAlYJMwsRiyAhwWO0emZU7g+/1neHTaBk5dSiA6IowxDzakY92ymQ+0rUdyEvy0GNaMh/2rrliXryg0GQDNBkNhC8vj2sZR9oiADwnYpCMU4PChY0NtqrjEZO59cxXbj5yndKFoloxqR/H09zbNl8zUznBgLcSUhMdWQYFSoYZC+xEBERCBkCZgkzCxGLQCHBY7R6Z5RuCXU5dcZWTNCyxzq+MvXWozqG0Vzwbb2OvwJich6da5kJzgWJgnHGp3g5aPQYVmur5io99kU8gRsElHKMARco+X7zb07LwtvL9uv6tSyvuDW9K8SgZHGb/+J3z7irNon7lQ43bfGaCZREAEREAE/ELAJmHilw1nbREFOLLGTaMsI3D6UgKPTlvPxv1nXZYNalOF57rcdO0JXcvsvq45F4/Dhsmw/h24dPxK13KNoeVwJ+ARERVMO5KtIhBUBGzSEQpwBNWj4z9jF2w6xBOzNrkW/GPHWgy/ufq1i+9bCdO6QmoKtPr/7J0HmFRF1obfSTBkJEsOkgQEUZKACcw5rzmnVUz8uoquGV3XsK4Zs2vOOYMiGMhKzjlHyQwMM/M/p/sOjMPAdE+nut3ffR4ecbi36tR7qqe//rrq1LVw1MD4BaieREAEREAEokbAJWEStUFFvyEZHNFnqhYTRMBW6V7/zu98O3l5IIJjO9TjsTM7kZ2VkaCIotTt9q1gdeFGPgtLx+9stHI96HIZHHgxVCqhUH6UulczIpCqBFzSETI4UnUW7mHcs1Zs5MSnfmbztjwOa12bly7ssqurv3kNPNcL1i+GevsFT03J9MGxY8q3CIiACIjALgRcEiYOp0cGh8PJUWjhE8jLL+C+L6bw6q/zAg8f2GQvXrjgQPaqlAQrHQoKYMGIoNEx9fPgl3F2ZZSHDmcET1+p1yF8aHpCBESgRAIu6QgZHJqkfyGwZVseJz/9C9OXb2Dvatl8eV1vahR/o7M3jffOD75hZFWEK4dBrRJOVhFbERABERABXxBwSZg4DEwGh8PJUWhlJ/Di8Dnc/+XUQAPNa1Xi1Yu70rhmxbI36NqTaxcET14Z9xrkrNsZXdPe0O0qaH0MpPt85YprzBVPyhFwSUfI4Ei56bfnAd/ywXjeG7OIzPQ03r2ye8lHiI15Bb64IdjQSU/D/ueJogiIgAiIgI8JuCRMHMYog8Ph5Ci0yAh8OWEpN773B9u251OrcrnA6t2OjapH1qhrT2/bBOPfgZGDYNX0ndFVbwJdr4DO50N2NdeiVjwi4AsCLukIGRy+mDLxCfLDsYvo/35wv+KAY9twxcEtdu14xTR4/lDYvgXanQqnv6zq1PFJj3oRAREQgZgRcEmYxGyQkTcsgyNyhmrBYQKj563h8v+NYe3mXCpkZfDUOfvTp21dhyMuY2i2Enn2DzDiWZj1/c5GsirB/udC1yuhVgm158rYnR4TgVQg4JKOkMGRCjMuhDHOWL6Bk576hS25efRtWyewBzPNzg8reuXmwAuHw4rJUK0xXDUcKiSZux8CK90iAiIgAslGwCVh4jBbGRwOJ0ehRYfA7JUbufDlUSz6cwvpaXDvSe05r3uT6DTuYiurZgZXdPzxFuRu2hlhyyNhv7Og+aEqSupi3hSTcwRc0hEyOJybHvEPaNPW7Zz09C9YcdEG1Svw5XW9qF6xhAJTX90CowYFzxe/+Gto3C3+wapHERABERCBqBNwSZhEfXDRa1AGR/RYqiWHCazYkMOlr45h4uJgvYqrD23BzUe29vcxsqXx3rIWfn8dRj0PVrOj6GXFSJsfBi0Og8Y9IKtCaa3p30Ug5Qi4pCNkcKTc9PvrgAsKCuj/3ng++n0xWRlpvH/VQXQqac/l9G/g7bOCDx92Bxxyc4qT0/BFQAREIHkIuCRMHKYqg8Ph5Ci06BKwL7/6vf07P0xbEWj45E71+ffpHSmXmR7djlxrLT8Ppn8FY1+DeT8Ht2QXvTKzoXF3z/A4HOq2h/QkZ+JajhSPkwRc0hEyOJycIvEL6t3RC/jHhxMDHd55/L5c0qvZrp2vXwrP9YTNq6FJL7jwM1Wbjl+K1JMIiIAIxJyAS8Ik5oMtewcyOMrOTk/6kMD2vHzu/Gwyb40Mrmjo0bwmz51/ANUqZPlwNGUI2bZmLxwJc36E2T/CUqtTV/DXhirWCm5jsdUdtsqjWoMydKRHRMD/BFzSETI4/D+fyjyCqUvXB46E3bo9n6Pb1ePZ8zrvWncjPx9ePxnm/gTZ1eHqX6BawzL3qQdFQAREQATcI+CSMHGPzo6IZHA4nByFFhsCttL3maGzefjb4KkjrepW5pWLuwa2NKfctWl1UA8HDI+hsK7YVhYDUqvVzu0sTXtB+Soph0kDTk0CLukIGRypOQfZuHU7Jz75M3NWbaJRjQp80a93yY78z/+BwXcHKZ31BrQ9IUWJadgiIAIikLwEXBImDlOWweFwchRabAl8/PsibvlgArl5BdStWp6XL+pCu/opfKSqncSyZk7wNJY5Q2HuMNi6/q9JSM+Ehl12Gh71O0NGZmwTpdZFIEEEXNIRMjgSNAkS2a258de/8wefjV9CuYx0Prz6IDo0LOFNatFYePlIyN8OB14Cx/8nkWGrbxEQAREQgRgRcEmYxGiI0WhWBkc0KKoN3xL4ddYqrnx9LBu2bqdy+UyeObczB7eq7dvxRDXwvO2wZFxwK4ut8Fg0Oqifi17lq0Gz3t6WlsOhRnMofmJhVINSYyIQPwIu6QgZHPHLuzM9vTlyPrd/PCkQz70nteOCHk13jS1nPQw6GP6cC7XbwOU/QrmKzoxBgYiACIiACESPgEvCJHqjinpLMjiijlQN+o3A9GUbuOiVUSxdl0NmehoPntqBMw5s5LdhxD5e09HzfwkaHrbKY/XMXfus1hhaHBpc4WF1PCrWiH1c6kEEYkTAJR0hgyNGSXa12clL1nHKM7+ybXs+x3XYm6fO2X/XuhsW/EdXwIR3IaM8XPEj1G3n6pAUlwiIgAiIQIQEXBImEQ4llo/L4IglXbXtGwLL1uUETI5pyzYEYr6hb0uu79OyZD3pm1HFONB1i3au7rAtLVa4/y9XGuzdcWexUjupJbN8jINS8yIQPQIu6QgZHNHLqy9auvDlUfw0YyVNa1bk8369qJJdQiXs8e/Cx1cEx3PMw9DN+7svRqggRUAEREAEwiXgkjAJN/Y43i+DI46w1ZXbBDbk5PL3N8cxfOaqQKBnHtiQgad0ICtDR6aWmjkr4L984k7DY/5vkLf1r49lVoAmBwUNjxaHQ519tZ2lVLC6IZEEXNIRMjgSORPi3Pfy9Tn0eHAI+QXw3HmdObr93rtGYAWTnusN2zZCq2Pg7Lf1CzXOeVJ3IiACIhBvAi4Jk3iPPYz+ZHCEAUu3Jj+B3Lx8bv1wIh+OWxQYrNXjsLocVp9DVxgEcrfAgt92Gh7LJu76cOW6wW0shdtZqpag4cPoUreKQLQJuKQjZHBEO7sOt/f8sNk88NU0qlfMYtSAvpTLLOayb98GLx8VLJJUZW+46heoVNPhESk0ERABERCBaBBwSZhEYzwxakMGR4zAqln/ErDC9f8ZPJMnhgRrTOy7d1VeubgLdatm+3dQiY5848rgcbSFBUvXL941otptd25nadoTylVKdNTqP8UJuKQjZHCkyGS0N6CjHx/O9OUbuKBHE+49qf2uI//+LvjlcSANLvgUmh+SInQ0TBEQARFIbQIuCROHMyGDw+HkKLTEEnh39AIGfDyJvPwCGlSvEDA5WtWtktigkqF3O4521czgySxmeMwbHlxlXfRKz4JG3byCpYdD/U6QnpEMo9cYfETAJR0hg8NHEyeSUCctXsfxT/4caOKTa3rSqVH1vzZnBY/+dzJQAL1uhL53R9KdnhUBERABEfARAZeEicPYZHA4nByFlngCQ6ev4Jo3x7FpWx5VsjN5/vwD6dFCK4Gjmpm8XFg0ZqfhsXgsFOT9tYuKtaDNsdD2RGh2sIqVRjUBamx3BFzSETI4UmSe3vv5FF7+ZS4taldi8E2H/LXS9aZV8GxP2LgMGhwAl3wLGSUUH00RVhqmCIiACKQaAZeEicPsZXA4nByF5gYB+0Lt4ldHs3LDVsplpPPwGftxUqcGbgSXjFHkrIO5w3caHmtm/3WU5atCq6Og7QmwT19tZUnGOeDImFzSETI4HJkUsQzDikB1f2AIqzdt4+ajWnPNYfvs7M6Wvr39N5jxDZSrAlcNhxrNYhmO2hYBERABEXCMgEvCxDE0RcORweFwchSaOwQW/bmZi14ZzawVwa0Ul/RsRv8jW1FJxUdjn6Q/58P0r2Dq5zD/1+DK7MIrMxta9AmaHa2Phgp7xT4e9ZAyBFzSETI4UmDaDZm6nEtfG0NaGvzyj8OpX73CzlGPHARf3xL8/1NfgP3OTAEiGqIIiIAIiEBRAi4JE4czI4PD4eQoNLcIrNucyxWvj2Hk3DWBwKwux8BT2nNo6zpuBZrM0WxcsdPsmPMT5OfuHG16JjTtHTQ72hwPVeomMwmNLQ4EXNIRMjjikPBEd/H3N8fy1cRl9NynJm9e1n1nOMsmwQuHB8/e3u9vcOoqXHxmAAAgAElEQVSgRIeq/kVABERABBJAwCVhkoDhh9qlDI5QSek+EQBsBfHzw+bw3yEz2bY9P8DkpE71+efx+1KrcnkxiieBLWth5ncw9TOYORi2bynSe1qwSKmZHW2Ph72axjMy9ZUkBFzSETI4kmRS7W4Y5qB3GTiYbXn5PHZmR07t3DB467bN8PyhsGo61GgOVw6D8qp2neTTQcMTAREQgRIJuCRMHE6RDA6Hk6PQ3CUwZ+VGBnw8kRFzgqs5qlfM4o7j9uW0zg3+WhPO3SEkV2T2GWD2kOA2lunfwNZ1fx1fvf2CBUrN8KjdmsAScF0iUAoBl3REqszYlBUlb4yYzx2fTKJiuQxG39535/7Hz2+Asa+ALVG79LtgcVFdIiACIiACKUnAJWHicAJSVks4nBOF5hMCBQUFvDdmIQO/nMr6nO2BqHvtUyuwbaVJzUo+GUUShrl9G8wbFjQ7pn0Jm1b+dZA19/FWdpwA9TvL7EjCKRCtIbmkI2RwRCurjrZz6jO/MG7BWk7t3IDHzuwUjHLKZ/De+cG/970Het3gaPQKSwREQAREIB4EXBIm8RhvGfuQwVFGcHpMBAoJrNiQwz2fT+HLCUsDP8rOSueGvq24rFczMjPSBSqRBPLzYOGooNlhf9Yt+Gs0VRsGt7DYyo7GPSA9I5HRqm/HCLikI2RwODY5ohnO3FWbOOyRoYEm37qsGwftUwvWLQoeCZuzFpofCud9DOl6Q4kmd7UlAiIgAn4j4JIwcZidDA6Hk6PQ/EVg8JTl/PPTSSxdlxMIfN+9q/LQafvRoWE1fw0kWaO1UxaXjt9pdtiW9qJXxVrQ5tjgVpZmB0Omaqok61QIdVwu6QgZHKFmzYf3PfrddJ78YRb1q2Xz8z8OJz09DT64FCZ9ABVrwtW/QpV6PhyZQhYBERABEYgmAZeESTTHFeW2ZHBEGaiaS20CG7du55Fvp/Pab/Owz9MmU+1I2ZuObEXFcpmpDce10a+cvtPsWPrHX6MrXxVaHRVc2bFPXyinLUeupS8e8bikI2RwxCPjCegjP7+A3v/+kcVrt3DNYS24+ag2sGk1PNYG8rbBiU9BZ2+bSgLiU5ciIAIiIALuEHBJmBShYssLrweuBKysv20Ofw+4E9gUIr0awADgZMCqbG8AJnltDA+xjcLbZHCECUy3i0AoBMYt+JNbP5zAjOUbA7c33MuOlO3AIa1qh/K47ok3gbULYOoXMO0LmP8rULAzgsxsaNEnaHa0Phoq7BXv6NRfggi4pCPCMTjiKTReBS7cTX7OAD4IM3cpJ0p+m72as18YEcA0pP8htKhdGX57Gr4dAOa09p8mhzXMSaTbRUAERCBZCbgkTIow/i9wHfAx8DXQFugHmDHRFwieO7n7qwlg+zQrAy8BMwBb/74f8C3wTpj5TDktESYf3S4CZSZgx8gO+ml2YOWxnfxn18nekbI1daRsmbnG/MGNK2D6V8HVHXN+gvzcnV3aQQZNewfNjjbHQ5W6MQ9HHSSOgEs6IhyDI55Co9DgKGmJwTCgWNWbUpOZcqLk5vfH8/7YRXRqVJ1PrulJYO3fM91h5TQ48FI4/rFSoekGERABERCB1CDgkjDxiLcDJnrmxmlFsmAGxxPAuVZeqpTsmBFiKz+6AsGKhpFdKaclIsOlp0UgfAKzV27kto8mMmpu8EjZvbwjZa1YfpqOKw0faDyf2LIWZn4HUz+DmYNh+5YivadBo27eiSzHw172q1lXMhFwSUeEanDEW2gUGhyhxlfa/EgpUbJ523a63D+YTdvyuO+kdpzfoyksGAkvHxnkdMVPUN87UaU0cvp3ERABERCBpCfgkjDxYN8P3A4c7K3YKMxBNrAa+Ak4dg+JsefsHlsB8iSQ5f3ZHEEyU0pLRMBJj4pARARsm/W7YxbywFdT2eAdKdu7ZS0GntyBxjUrRtS2Ho4TgW2bYfaQ4MqO6d/A1nV/7bjefsECpba6o3ZrHT8bp7TEshuXdESoBkK8hUahwWHbYqoAtimvtKWoe8pZSomST35fzA3v/kFWRhqjb+9L9Yrl4JNr4I83YO+OcKUtgtElAiIgAiIgAkECLgkTLye2hcS2odinma3F8vQL0ArY0wb9fwH/8GpvXAYcA9iZhjOBe4E3ypD7lNISZeCjR0QgqgRWrM/h7s8n89XEZYF27UjZG/u24lIdKRtVzjFvbPs2mDcsaHZM+xI2WTmlIlfNfYJGR8sjoVZrqFhDhkfMkxL9DlzSEaEaHPEWGoUGhxUDM4NjG2Cfyu8ARpYhJSklSs5/aSTDZ67i6Hb1eO78AyBnPTzaGnI3w3GPQhfTerpEQAREQAREwFmDw7an1AFK2rRthUatHpedS2j6oKTL6nZYYVFT0mZqPAOUA/oDtir1EuCVUvJv+sP+FF4Wy7jFixdTv77JCl0iIALxIPC9HSn7ySSWrQ8eKduufvBI2fYNdKRsPPhHtY/8PFg4cueJLOsW7tp8dnWo2QLM+Khh//X+bv8tX/RXclQjU2MREvCjwRFvoWHfvJgQGetVSu8I3ADYuUO2JHWwREnJBJaty6HHv4YESm48f/4BHNmuHox5Gb64ETIrwP9Nh2y9IUT4GtbjIiACIpBUBFwSJh7Y2d6WksYlgP4fYDW6rDz/2t0kwnRCH2COV5y00AixZ+xn9kmpQSmrQ+8G7irevgyOpJr6GoxPCGzIyeXhb6fz+oj5O46UtZUcNx6hI2V9ksJdw7QPK0vH7zQ7Vk0vfSiV6+5qepgRslczyLIdjLoSRcAlHRHqCg4XhEZLwA5eXgLY3/d0pawoee6n2fzr62nUqFSOEbf1oVxmOgw6BOzM6o7nwCnPJmreq18REAEREAFHCbgkTDxEkX6x8jlwPDDQW/1ZlPxrwAXAvsDUPaREKzgcna8KK3UJjJ2/hls/nMjMFcEjZRvVqBCozXGwjpT1/6SwFedrZsPqwj+zvP+fBTnFanjsMto0qNbIW+1RbPVH9SaQkel/Po6PwCUdEarB4YLQsLTactKLgNbecW+7S3VKipKCggKO/M+wwC/9iw5qyt0ntgs6o4Os1potyP0WGnd3/OWh8ERABERABOJNwCVh4o090q2x5uZf5R0r+1QxnoX1OXoCv4bBOqW2u4bBRbeKQFwJ2JGy9oXeU0WOlD1l/wb88/h9A1/w6UoyArbSY/Nqz/goYnqsngOrZxU7raWEsdtxtXZqS0lbXqrUh3Qr+agrUgIu6YhQDQ5XhIYtFbXVGRIlJczCiYvWccJTPwf+5fNre9GhYTX4sj+MfjFYtOeakSraE+mrV8+LgAiIQBIScEmYeHhLK25udbmscOjurouBl4GHgFuL3WQFRu2YWVsNOiuMdMrgCAOWbhWBWBOYtWIjA+xI2Xk7j5Q1k8PMDh0pG2v6jrSfnw8blu5c6VG4+sNWgqyZC/m5ew7Utu/XaP7XlR+FRkilWvrcFEaaXdIRoRocrgiNQlGyD2DbZkK9UkKU3P3ZZF79dR4t61TmuxsPJi13CzzaJng005ED4aBrQ+Wl+0RABERABFKIgEvCxMPeARgPWLHQ04qkoh/whFeDo/AklBZevY5pRe6zWhvzgfVAG+80Nvvnvb2io4u91aDhZDkltEQ4QHSvCCSagB0p+/boBfzrq2ls2Lo9EI4dKfvAKR1oVENHyiY6PwntP287rFsAhSs9AttfZgX/rLXipgV7Dq98Nahp5sc+xVZ/tFA9wxLIuaQjQjU44ik0rJBonlcArCi+/YERnrFh+2bDuZJelNhyve4PDmHNpm3cekwbrjqkBfzxNnxyFaRnQf9pYE6kLhEQAREQAREoRsAlYVIktCcBc+bN5PjKKxZ6HWDHxB5epEDoPKAJUFzTXAEMAiZ7qzls7frVnslh9Tm+C3MiJL2WCJOHbhcBZwgsX5/DXZ9O5pvJO4+UvemIVlzSsxmZGdqC4EyiXAkkNwf+nLfryg8zPzYG59Aer0q1/2p61GoFtVsHt8JkZJX2dFL+u0s6IlSDwxIRL6HRCfga+MT7lmUTYKeo2JFu+cCRQHAfRuhX0ouS7yYv44rXx5KWBr/d2od61bLh5WNgwa/Q7hQ4w07e1SUCIiACIiACuxJwSZgUiS7DO0HNjIqmwCrgXeDOIisy7PbdGRz2b6cCtwD2RY1piN+AezyTJNypkPRaIlwgul8EXCPw7eRl3PnpJJav3xoIrX2DqvzrVB0p61qenI5n6wZYY/U9vGKnRVd+bPlzz6FbvQ/b8mKGR+Gf2q2gZkvIrur0sCMNziUdEY7BES+hUQ94GOgCmJioACwFfgQeBIouQQ01F0kvSq56fWzAtbZlea9f2g1WzoCnDaEt5P0YWtiXXbpEQAREQAREwDcGh2upSnot4RpwxSMCZSGwPieXf38zjTdGLAg8npGeRuBI2b6tqFDOPs7oEoEyEti8Jmh87DA9ZsOqmaEVO62y967Gh5kg9nP7htrnl18NDj9jT2pRsnbzNroMHExuXgGPn9WJk/dvAN/dAb8+CdUbw3XjVSHYz7NXsYuACIhAjAm4JExiPNRImk9qLREJGD0rAi4SGDNvDbd+NBErRmpX4xoVA7U5erXUlm0X8+XrmKzY6bqFQbNj1QxYNX3n3zet3PPQylWBWi0986NlcKuLGR97NYNM/5wK5JKO8L9dFNqrIalFyeu/zeOfn06mUrkMRt/Rl4rp+fBYW9i8Cg67Aw65OTRKuksEREAERCAlCbgkTBxOQFJrCYe5KzQRKDOBrdvzeHbobJ75cTbb8myXGpzauQH/PG5f9tKRsmXmqgfDIGCrPkoyPqwGSEFwTpZ4BY63bbar8WFmSHa1MAKIz60u6QgZHPHJeUx7OfnpX/hj4VrOOKAhD5/RESZ/Au9fCGnpcONkqGqaTJcIiIAIiIAIlEzAJWHicI5kcDicHIUmAnsiMGvFBm79cCJj5gdrKNSoVI47j9+XkzrV15GymjqJIWCFTq3WR2DFh/dn5fTgdpfczXuOqXK9nas+Ais+vBUgVRskbLuLSzpCBkdipnTUep29ciN9Hv0p0N7bl3enR4ua8PopMPsHaHU0nGP12HSJgAiIgAiIwO4JuCRMHM6TDA6Hk6PQRKA0Anak7FujFvDQ1zuPlD2kVW3uP7m9jpQtDZ7+PX4EbLvL+sV/3eZitRXNBNm0Ys9xZFUqYnwUKXRao0XMt7u4pCNkcMRvusakp4e/ncbTP86mQfUKDL/lMNLtvOf/2qEzBfC3t6HNsTHpV42KgAiIgAgkDwGXhInDVGVwOJwchSYCoRJYti6Huz6bxLeTlwceqZCVQf8jW3HRQU11pGyoEHVfYgjYKS47trvMCB4qYcbHn3P3vN0lLSN4hG3gONsixof9f4XqURmLSzpCBkdUUpqYRsyJ7vXQDyxZl0O/w/eh/5Gt4YeBMOzfYEuXbHtKRmZiglOvIiACIiACviHgkjBxGJoMDoeTo9BEIFwC30wKHim7YkPwSNkODarxr9M60K6+e/UNwh2b7k8xAtu3/nW7S6HxYWZI7qbdw6jXAa76OSqwXNIRMjiiktLENPLrrFWc8+LIQOc//t+hNKuRDf9pDxuWQO/+0OfOxASmXkVABERABHxFwCVh4jA4GRwOJ0ehiUBZCNiRsrZl5c2RO4+Uvax3M27ooyNly8JTzzhGoKDA2+5SZLVHYb2Pjcuh/elw+ktRCdolHSGDIyopTUwj/d8bz4fjFtG5cXU++ntPmPEdvHVGMJjrfocazRMTmHoVAREQARHwFQGXhInD4GRwOJwchSYCkRAYNXcNt300gdkrg992N6lZMVCbo3fL2pE0q2dFwF0CW9ZC7haoundUYnRJR8jgiEpK49/Ipq3b6TJwMJu35QV+AZ/XvQm8cy5M+wKaHQIXfhb/oNSjCIiACIiALwm4JEwcBiiDw+HkKDQRiJSAHSlrde2eHTqL3LyCQHMndKzPHce1pW7V7Eib1/MikNQEXNIRMjh8OtU+GreIm94bT7mMdEbf3pdqeWvgsbZQkAenvQQdTvfpyBS2CIiACIhAvAm4JEziPfYw+pPBEQYs3SoCfiUwc/kGBnw8kdHzgkfKVi6fGShCen73JipC6tekKu6YE3BJR8jgiHm6Y9PBuS+O4JdZqzm2Qz2eOfcAGP4YDLkHKuwF/adDZvnYdKxWRUAEREAEko6AS8LEYbgyOBxOjkITgWgSsEL+tg38wa+nsWbTtkDT7epXDaya3r/xXtHsSm2JQFIQcElHyODw4ZRasnYLPR/6Aasb89KFB9KnTR14Yv/gEUHd/w5HP+jDUSlkERABERCBRBFwSZgkikEI/crgCAGSbhGBZCLw56Zt/Pvb6bw9KliENC0Nzu7amFuOak31iuWSaagaiwhERMAlHSGDI6JUJubhp3+cxcPfTqdmpXKMGNCHrAU/w2snBIP5+wio0zYxgalXERABERABXxJwSZg4DFAGh8PJUWgiEEsC4xb8ye0fT2Lq0vWBbkyD33ZsW07r3IA0cz10iUCKE3BJR6TKKzJpRElBQQF9H/spUOX5kp7NuPOEfeHDy2Di+9CwK1z2fYq/vDR8ERABERCBcAm4JEzCjT2O9yeNlogjM3UlAklDYHtePv/7bT6PfT+DjVu3B8bVtWkN7j+lPa3qVkmacWogIlAWAi7pCBkcZclgAp8Zv3AtJz39SyCCL/r1ov1eefBoG8jbCic9Dfufl8Do1LUIiIAIiIAfCbgkTBzmJ4PD4eQoNBGIF4Hl63O474spfDFhaaDLzPQ0Lu3djOv7tKRiucx4haF+RMApAi7pCBkcTk2N0oO589NJAfe4dd0qfHNDb9JGDoJv/gHlqsD/TYdylUpvRHeIgAiIgAiIQBECLgkThxMjg8Ph5Cg0EYg3gWEzVmK6fN7qzYGu61fL5q4T23HkvnW1bSXeyVB/CSfgko6QwZHw6RB6ANu259P1gcGs3ZzLgGPbcEXv5vDsQbBiChxwMZzweOiN6U4REAEREAER8Ai4JEwcTooMDoeTo9BEIBEEcnLzGPTTHJ4eOgvT6XZZ8f+7T2xHoxoVExGS+hSBhBBwSUfI4EjIFChbp99MWsZVb4wlPQ1G3NaHOusnwYt9go1d/iM06Fy2hvWUCIiACIhAShNwSZg4nAgZHA4nR6GJQCIJzFu1iTs/m4yt6rArOyudfoe35PLezSmXmZ7I0NS3CMSFgEs6QgZHXFIenU6u+N8YvpuynENa1ea1S7rCp9fC769DvQ5w5fDg2VW6REAEREAERCBMAi4JkzBDj+ftMjjiSVt9iYDPCNhBAF9PWsa9n09h2fqcQPQtalfivpPac9A+tXw2GoUrAuERcElHpMonYt+LkjWbttHtgcHk5hXw37914qS2VeGR1pC7CY59BLpeHt4s1N0iIAIiIAIi4BFwSZg4nBTfawmH2So0EUgaAnbCyuPfz+CVX+eRl18QGNfJneoz4Li21KmSnTTj1EBEoCgBl3SEDA6fzM3Xfp3HXZ9Npkr5TEbf0ZfsCa/D59dDZjb0nw4VqvtkJApTBERABETANQIuCRPX2BSJRwaHw8lRaCLgGoGpS9dz+8cTGbdgbSC0KtmZ3HxUa87t1oQM22+uSwSSiIBLOiJVXl2+FyUnPvUzExat46wDG/HQ6fvBC4fD4rGw39/g1EFJ9PLQUERABERABOJNwCVhEu+xh9Gf77VEGGPVrSIgAlEgkJ9fwPtjF/Lg19MChwTY1aFBNQae0p79GurLySggVhOOEHBJR8jgcGRS7CmMmcs3cMR/hgVuee/KHnStsASe6xl85OKvoclBPhiFQhQBERABEXCVgEvCxFVGdgoksHjx4sXUr29/1SUCIiACoRGwreYPfT2Nd8csDDxgZfPO69aE/zuqNdUqZIXWiO4SAYcJuKQjZHA4PFEKQ3vom2k8O3Q2jWpU4Kf/O4z0b/4BowZBzZZw7WgVF/VBDhWiCIiACLhMwCVh4jAnGRwOJ0ehiYAfCIyZt4Y7PpnEtGUbAuHWqlyO249ry8mdGpCmwwL8kELFuBsCLukIGRyOT1MrTtTzXz8EqjFf16clNx3aCB5tDTnr4Ij7oOd1jo9A4YmACIiACLhOwCVh4jArGRwOJ0ehiYBfCGzPy+fVX+fxn+9nsGlbXiDs7s1rcP/J7dmnThW/DENxisBfCLikI2RwOD45f565ivNeGhmI8qebD6XJ4i/ho8shPRNumgaVazs+AoUnAiIgAiLgOgGXhInDrGRwOJwchSYCfiOwdN0W7vtiCl9NXBYIPSsjjct7N6ff4S2pUC7Db8NRvClOwCUdIYPD8cl447t/8PHvizmwyV58cPVB8MpxMP9naHsinPW649ErPBEQAREQAT8QcEmYOMxLBofDyVFoIuBXAkOnr+DOTyezYM3mwBAaVK/APSe2o+++df06JMWdggRc0hEyOByegHaOdpf7B7MlN48HT+3A2c23wVMHBCM+70PYp6/D0Ss0ERABERABvxBwSZg4zEwGh8PJUWgi4GcCObl5PDN0Ns8Nnc22vPzAUI7Yty53nbAvDfeq6OehKfYUIeCSjpDB4fCke3/MQm7+YALlMtMZfXtfqv18H/zyX6jWCK4fD+lavuZw+hSaCIiACPiGgEvCxGFoMjgcTo5CE4FkIDB31Sbu/HQSw2euCgynQlZGoAbfpb2aBT4P6BIBVwm4pCNkcLg6S4Cznx/Bb3NWc/x+e/PUWR3gsbawaSUcOgAO/YfDkSs0ERABERABPxFwSZg4zE0Gh8PJUWgikCwECgoK+HLiUu79fAorNmwNDKtlncrcd3J7ujevmSzD1DiSjIBLOkIGh6OTa9Gfm+n10I+B6F65qAuH5Y+A986HtHS4YSJUa+ho5ApLBERABETAbwRcEiYOs5PB4XByFJoIJBuBDTm5PPb9DF77dR75BcHRndq5AQOObUutyuWTbbgaj88JuKQjZHA4Opme+mEmj3w3I/ALbMRth5P59pkw63toeSSc+76jUSssERABERABPxJwSZg4zE8Gh8PJUWgikKwEJi9Zxx2fTOL3BWsDQ6yancktR7fhnK6NSU9PlY9yyZrd5BmXSzoiVV4VvhIltjStz6M/MWfVJi7r1Yw7elWBxzsABXDWG9D2hOR5NWgkIiACIiACCSfgkjBJOIzdB+ArLeEwR4UmAiIQJoH8/ALeGb2Qh76ZxrotuYGnOzaqzsCT29O+QbUwW9PtIhB9Ai7pCBkc0c9vxC2OW/Anpz7za6Cdr67rzb4znoGhD0KlOnDTFMjIirgPNSACIiACIiAChQRcEiYOZ0UGh8PJUWgikAoEVm/cyoNfT+ODsYsCw7UFHBf0aMpNR7aiarY+H6TCHHB1jC7pCBkcDs6SOz6ZyBsjFtB276p83e8geHw/WL8Iet4AR9zjYMQKSQREQAREwM8EXBImDnOUweFwchSaCKQSgVFz12CfF2Ys3xgYdu0q5bnjuLac2LE+aWmp8vEulTLu/lhd0hGp8grwjSjZuj2PrgOHBJaf2S+qy+rNgTdPC87qfuOgZgv3Z7giFAEREAER8BUBl4SJw+B8oyUcZqjQREAEokQgNy+fV36Zy+ODZ7J5W16g1Z771OTek9rTonblKPWiZkQgNAIu6QgZHKHlLG53fT1xKVe/OY6M9DR+u+1w6nx1GUz9HJr2hou+iFsc6kgEREAERCB1CLgkTBymLoPD4eQoNBFIVQJL1m7hns8n8+3k5QEEWRlpXNijKf36tKRaBW1bSdV5Ee9xu6QjZHDEO/ul9HfZa2MYPHU5h7WuzStnNIXH2kL+djj1RdjvDMeiVTgiIAIiIALJQMAlYeIwTxkcDidHoYlAqhP4Ydpy7vpsMgvXbAmgqFGpHDce0YqzuzQiMyM91fFo/DEm4JKOkMER42SH07wVDur2wBC25xfw5Nn7c8KG92DwXZBdHfpPh6zscJrTvSIgAiIgAiIQEgGXhElIASfmJhkcieGuXkVABEIkkJObxyu/zOOpH2ayydu20rpuFf55/L70alkrxFZ0mwiET8AlHSGDI/z8xewJ20d3z+dTqJKdyegBfch+riusmQ3droJjHopZv2pYBERABEQgtQm4JEwczoQMDoeTo9BEQAR2ElixIYdHv53Be2MXUlAQ/HnftnW4/bh9aVarklCJQNQJuKQjZHBEPb1lb/D4J4czafF6zu7aiAc7r4dXjws2dvWvULdd2RvWkyIgAiIgAiKwBwIuCROHEyWDw+HkKDQREIFdCUxavI57v5iCnbpil+pzaJbEioBLOkIGR6yyHGa705dt4KjHhwWe+uCqHhw47laY8C40OBAuHxJma7pdBERABERABEIn4JIwCT3quN8pgyPuyNWhCIhApAQKCgr4ZtIyHvh6qupzRApTz++WgEs6QgaHIxP1wa+mMmjYHJrUrMjQazqR9lgb2J4DJzwBB1zoSJQKQwREQAREIBkJuCRMHOYrg8Ph5Cg0ERCBPRMoqT5Hq7qVA/U5eresLXwiEBEBl3SEDI6IUhmdh/PyC+jx4BBWbNjKjX1bcX2VH+Hrm6Fc5WBx0fI6yzo6pNWKCIiACIhASQRcEiYOZ0gGh8PJUWgiIAKhEdhdfY4Bx7aleW195giNou4qTsAlHSGDw4H5OWzGSi54eVQgkuE3H0qj946E5ZOg8wVw4pMORKgQREAEREAEkpmAS8LEYc4yOBxOjkITAREIj0Dx+hyZ6WlceFBTruvTkmoVssJrTHenPAGXdIQMDgem4/Xv/M6nfyyha7MavHd8OXjh8GBUl/0ADQ9wIEKFIAIiIAIikMwEXBImDnOWweFwchSaCIhA+ARKqs+xV8UsbjqyNWd3aURmRnr4jeqJlCTgko6QwZHgKbghJ5cuAweTk5vPQ6d14Kylj8C416BOO7j6F0hLlRQlOBHqXgREQARSmIBLwsThNMjgcDg5Ck0ERBgzweYAACAASURBVKDsBFSfo+zs9GSQgEs6IlU+PTsrSt4bvZBbPpxA+cx0Rt/SnapPtYdtG+GYf0O3K/WaEQEREAEREIGYE3BJmMR8sGXvwFktUfYh6UkREAER2ElA9Tk0G8pKwCUdIYOjrFmM0nNnDfqNkXPXcGLH+jzRehJ8di1klIf+06BijSj1omZEQAREQAREYPcEXBImDudJBofDyVFoIiAC0SOg+hzRY5kqLbmkI2RwJHDWLVyzmd7//jEQwasXd+HQ4efAotHQ4Uw47YUERqauRUAEREAEUomAS8LEYe4yOBxOjkITARGILgHV54guz2RvzSUdIYMjgbPtiSEzeez7GdSpUp5fL6lH5qCewWgu/AKa9U5gZOpaBERABEQglQi4JEwc5i6Dw+HkKDQREIHYEFB9jthwTbZWXdIRMjgSNLvMFT3skaHMW72ZKw5uzoC012Dks1CjOfQbp+KiCcqLuhUBERCBVCTgkjBxmL8MDoeTo9BEQARiS0D1OWLL1++tu6QjZHAkaDaNnb+G0579LdD7d9d2pdWbXWDLn9D3buh1Y4KiUrciIAIiIAKpSMAlYeIwfxkcDidHoYmACMSHgOpzxIez33pxSUfI4EjQ7Bnw8UTeGrmAdvWr8uVhy+HDSyE9E26cAlXqJigqdSsCIiACIpCKBFwSJg7zl8HhcHIUmgiIQPwIqD5H/Fj7pSeXdIQMjgTMGtvL1nXgYNbnbOefx+/LpbOvg7nDoM3x8Lc3ExCRuhQBERABEUhlAi4JE4fzIIPD4eQoNBEQgfgTUH2O+DN3tUeXdIQMjgTMki8nLOWat8aRkZ7G6L83p8aL3YJRnPsBtDwiARGpSxEQAREQgVQm4JIwcTgPMjgcTo5CEwERSByBkupz9GlThwHHtaVF7cqJC0w9x42ASzpCBkfc0r6zo0teHc0P01ZgL/yXGn4FPz8GVRvCDRMgPSMBEalLERABERCBVCbgkjBxOA8yOBxOjkITARFIPIHd1uc4vCXVKmYlPkBFEDMCLukIGRwxS3PJDa/csJXuDw4hL7+AZ8/uwDHf94WNy+GQW+Gw2+IcjboTAREQAREQAXBJmDicDxkcDidHoYmACLhBYLf1OY5oxdldG5OZke5GoIoiqgRc0hHhGBw2G68HrgSaAiuB94A7gU0hEqoBDABOBhoCG4BJXhvDi7Vh+zYGAvbfAuBX4FbgjxD7KnqbM6LkxeFzuP/LqVTNzmTM6dso98F5QFpw9Ub1xmUYmh4RAREQAREQgcgIuCRMIhtJTJ92RkvEdJRqXAREQASiQED1OaIA0UdNuKQjwjE4/gtcB3wMfA20BfoBZkz0BfJLyUETYChgG7FeAmYA1YD9gG+Bd4o83927dzHwlPfza4E6wEHAxDDz7YwoOfa/w5mydD3ndmvMwM33wcxvYZ++cN6HYQ5Jt4uACIiACIhAdAi4JEyiM6KYtOKMlojJ6NSoCIiACMSAgOpzxACqg026pCNCNTjaeaaCmRunFWFqBscTVh4TeKsU1maE2MqPrsDSUu4dBbTxTBQzOexqAEwFRgBHhplXJ0TJ1KXrOea/wYUqn13QjP3e7wkF+XDm/2Dfk8Ickm4XAREQAREQgegQcEmYRGdEMWnFCS0Rk5GpUREQARGIMQHV54gx4AQ375KOCNXguB+4HTjYW7FRiDAbWA38BBy7B672nN1jK0CeBKzKjP3ZXMIz+wAzgZeBS4v9u638uBgwkbEsjDw6IUoGfjmFF4bPpVmtSvxw4EjShj4AlWrDjVMgs1wYw9GtIiACIiACIhA9Ai4Jk+iNKuotOaEloj4qNSgCIiACcSKg+hxxAp2AblzSEaEaHLaFxLahVAS2FmP2C9AKqL0Hlv8C/uHV3rgMOAaw40LMyLgXeKPIs2d7q0EuB14s1qb97HngeODLMHKXcFGyPS+fHv/6ASsy2r/vPvSbeDqsWwAHXQdH3hfGUHSrCIiACIiACESXgEvCJLoji2prCdcSUR2NGhMBERCBBBEoqT7HPnUqc/UhLTihY33KZaoQaYJSU+ZuXdIRoRocVvPC6l/ULWHUVmj0DKA8sG03VGxrixUWtcKkZmo8A9iShf6AbX+5BHjFe9Z+9oi3IsRqfRS9bJWIGRtW6NSMjt1dVQD7U3hZ3OMWL15M/fqmT+J//Th9BRe/MjrQ8aiz0qjzqfk4wLVjoZYtWtElAiIgAiIgAokh4JIwSQyBkHqVwRESJt0kAiIgAqERKKk+R72q2Vzaqxl/69qIKtk6WjY0kom/yyUdEarBMdvbUlLSMR//A84H9gLW7gbvYKAPMMerq1FohNgz9rMcr8aGFSr9p7eqw+7/oVh7hwNDgBuBx/eQyruBu4r/eyINjn5v/87n45fQvXkN3qn+HEz5BJr0hIu/SvyMVAQiIAIiIAIpTcAlYeJwImRwOJwchSYCIuBfAlOWrOe5n2bz5cSl5OXb4ZlQpXwm53RvzCU9m1G3qlVF0OUyAZd0RKgGR6QrOD73tpXYsa93FEvOa8AFwL5eEdGkW8GxPieXLvcPZuv2fP57QkNOGtIH8nPhlEHQ8W8uz1XFJgIiIAIikAIEXBImDuOWweFwchSaCIiA/wksXLOZl36ey7ujF7IlNy8woKyMNE7u1IArDm5Oy7pFF+j7f7zJNAKXdESoBkekNTieBa7yjpUtPPa1MKeF9Tl6Ar8CSVeD451RC7j1o4lkZ6Uz/ogZlP/hTihfDf5vOmRVSKa5rbGIgAiIgAj4kIBLwsRhfDI4HE6OQhMBEUgeAn9u2sYbI+bz2m/zWLVxZwWEw9vUCRgd3ZrVIC0t1I+xycPF5ZG4pCNCnRmlnaIyzCscujvudvKJnYryEHBrsZuswKgdM9sSmAUk3SkqZz73G6PmreHkjnvz+KorYPVM6HI5HGelRnSJgAiIgAiIQGIJuCRMEktij73L4HA4OQpNBEQg+QhYMdKPxi3mxeFzmLNq044BdmxUnSsPbs5R7eqRkR7qx9nk4+PSiFzSEaHOiA7AeMCKhZ5WBGY/4AmvBkfhSSgtvHod04rcZ7U25gPrgTbARu/f9vaKji4GWhe536px2v/bvUu8n5uwsDZHeSe6hJPThImSBas3c/DDPwZi/fSEdDp+721JuepnqGdYdYmACIiACIhAYgm4JEwSS0IGh8P8FZoIiECKErC6HN9PWc7zw2YzbsHOko9Nalbkst7NOeOAhmRn2QGduhJFwCUdEarBYayetDM/PJPDKmO2Ba4D7JhYK/5pBULtmgc0AYq3fQUwCJjsreawU1SuBszksGNfvyuSkIMAcwUWef3aP5mZYqeh2FYWM1vCuRJmcDw+eAaPD55J3arl+a3N+6RPeAfq7w9XDA0nft0rAiIgAiIgAjEj4JIwidkgI284YVoi8tDVggiIgAgkB4Ex89bw3E9zGDx1+Y4B1ahUjgt6NOGCHk2xv+uKPwGXdEQ4BofZYjcAZlQ0BVYB7wJ3FlmRsSeDw/7tVOAWwJYumCHyG3CPZ5IUz0QPwLbGdAOsnK7V57jNjnstQ8oSIkoKCgo45OGhLFizmet61uam8SfC9i1w/ONwoO3a0SUCIiACIiACiSfgkjBJPI3dRpAQLeEwD4UmAiIgAgkjMGvFxsDWFdvCsi0v+D271Ts888BGXNarOY1rVkxYbKnYsUs6IhyDw8+5SogoGT1vDWc8Zx4OjDxiLnWH3w5ZFaH/dMiu6meeil0EREAERCCJCLgkTBzGmhAt4TAPhSYCIiACCSewYn0Or/46L1CUdH3O9kA8VpbjmA57B+p07NewesJjTIUAXNIRMjhiOONu/XAC74xeSIcG1fg86zZYNgH2Pw9OejqGvappERABERABEQiPgEvCJLzI43q3DI644lZnIiACIhA6gY1btweOl31p+ByWrMvZ8WD35jW48uAWHNq6tk5eCR1n2He6pCNkcISdvtAesKq/Xe4fzIat23nyEDhh5DnBBy/9Hhp1Da0R3SUCIiACIiACcSDgkjCJw3DL2oUMjrKS03MiIAIiECcCuXn5fDlhKYOGzWHqUjvfIni1rluFyw9uzokd61MuMz1O0aRONy7pCBkcMZp3n49fQr+3fyczPY1JXb4he/xrULst/P030LnNMaKuZkVABERABMpCwCVhUpb44/SMDI44gVY3IiACIhApAauF+POsVQz6aU7gv4VXvarZXNKrKWd3bUyV7KxIu9HzHgGXdIQMjhhNy4teGcXQ6Ss5rk1Vnl5yNmzbAEc9CD3+HqMe1awIiIAIiIAIlI2AS8KkbCOIy1MyOOKCWZ2IgAiIQHQJTFq8jheGz+GLCUuxI2ftqlI+k3O6Nebins2oVy07uh2mYGsu6QgZHDGYgCs25ND9gSHY6+fzXvPoMGYAZJQLFhetWCMGPapJERABERABESg7AZeESdlHEfMnZXDEHLE6EAEREIHYEVj052Ze+nluoFbH5m15gY6yMtI4qVMDrji4Oa3qVold50nesks6QgZHDCbbC8PmMPCrqVSrkMXvDR4mfdEoaH8anP5yDHpTkyIgAiIgAiIQGQGXhElkI4np0zI4YopXjYuACIhAfAis3bwtcOqKnb6yauO2HZ0e1ro2Vx7Sgm7NaqggaZipcElHyOAIM3mh3H7048OYtmwDN3XK57pp5wUfueAzaH5IKI/rHhEQAREQARGIKwGXhElcBx5eZzI4wuOlu0VABETAaQJ2KMTHvy/Gvpyes2rTjlg7NqzGFQe34Oj29ciwM2d1lUrAJR2RKhmLmyiZvGQdxz3xc2ASjDpgCHUmvwR7NYV+v0O6KvaW+urQDSIgAiIgAnEn4JIwifvgQ+8wbloi9JB0pwiIgAiIQKQE8vML+H7qcp4fNoex8//c0VyTmhW5rFczTj+gERXKZUTaTVI/75KOkMER5al23xdTAnu7Wtcqxzd5V5C2ZQ30uRN6949yT2pOBERABERABKJDwCVhEp0RxaQVGRwxwapGRUAERMAdAmPmrQkcMTt46nIKgvVIqVGpHBf0aMIFPZoG/q5rVwIu6QgZHFGcoXbuco8HhwT2cg3afz5HTb0N0jLgpilQpV4Ue1JTIiACIiACIhA9Ai4Jk+iNKuotyeCIOlI1KAIiIAJuEpi9ciMvDp/Dh+MWs217fiDI7Kx0zjigEZf1bkaTmpXcDDxBUbmkI2RwRHES/DBtOZe8Ooa0NJja8jmyFwyD1sfB2W9FsRc1JQIiIAIiIALRJeCSMInuyKLamgyOqOJUYyIgAiLgPgE7HfO1X+fx+m/zWZ+zPRCwleU4pv3egZNXOjaq7v4g4hChSzpCBkcUE37NW+P4csJSTm6yjceXXxRs+ex3ofXRUexFTYmACIiACIhAdAm4JEyiO7KotiaDI6o41ZgIiIAI+IfApq3bA8fLWimCxWu37AjcTly5uGdT+ratS2ZG6tZbdElHyOCI0utq3eZcujwwOLCE6esOQ2k783moUh9umAgZmVHqRc2IgAiIgAiIQPQJuCRMoj+6qLUogyNqKNWQCIiACPiTgJUk+GriUgb9NIcpS9fvGETdquU5u2vjwJ+6VbP9ObgIonZJR8jgiCCRRR99a+QCBnw8kcpZMKHaTaRvXAYH3wyH3xGlHtSMCIiACIiACMSGgEvCJDYjjEqrMjiiglGNiIAIiID/CRQUFPDzrFW8+ss8fpi+YkdBUjtW9sh963Je9yYc1KImaVa7IAUul3REahCHmIuS0579NXCs0B37zOOyRQOANLh+POzVJAWmtIYoAiIgAiLgZwIuCROHOcZcSzg8doUmAiIgAiKwGwIL12zm7VELAltYVm/atuOu5rUrcW63JpzeuSHVKmYlNT+XdIQMjihMtXmrNnHoI0MDLY1r8RI1Fg+B5ofBBZ9EoXU1IQIiIAIiIAKxJeCSMIntSCNqXQZHRPj0sAiIgAgkN4Gt2/P4ZtIy3hgxn9Hz/twxWDt95cSO9Tm/e1M6NKyWlBBc0hEyOKIwxR77bjpP/DCL/apu4tPcK0kryIczXoV2p0ShdTUhAiIgAiIgArEl4JIwKTJSq9Z2PXAl0BRYCbwH3AlsCpNIRWAS0Ax4Grg2zOftdhkcZYCmR0RABEQgFQlMW7Y+YHR8PG4xm7bl7UDQsWG1wPaVEzrWJzsrI2nQuKQjZHBEOK3y8ws4+OEfWfTnFv7XchgHL3wOKtaEm6ZCZvkIW9fjIiACIiACIhB7Ai4JkyKj/S9wHfAx8DXQFugHDAf6AvlhkHnEM0oqy+AIg5puFQEREAERiIjAxq3b+fj3xbw5Yj7Tlm3Y0Va1ClmccUBDzu3ehGa1KkXUhwsPu6QjZHBEOCNGzlnNWc+PII18ZtQZQNb6BdDjWjhqYIQt63EREAEREAERiA8Bl4SJN+J2wETP3DitCAUzOJ4AzgXeCpFOZ2AUcAvwqAyOEKnpNhEQAREQgagRsKKkY+b/GVjVYaew5OYV7Gi7d8tagVodfdvW8e1Rsy7pCBkcEU7bWz4Yz3tjFnF+3Xnct86KiwLXjILarSNsWY+LgAiIgAiIQHwIuCRMvBHfD9wOHOyt2CgEYWfvrQZ+Ao4NgY6t/zVzY6m3LWWuDI4QqOkWERABERCBmBFYtXFroCCpncK5eO2WHf3Uq5rtHTXbiDo+O2rWJR0hgyOCqbtlWx5dBg7Glh792PQ1mi37Fhr3gEu+iaBVPSoCIiACIiAC8SXgkjDxRv6ttw3FamdsLUbjF6AVUDsESv8H3APYihC7ZHCEAE23iIAIiIAIxJ5AXn4BQ6evCKzqGDpj5Y6jZjPtqNl2waNmezT3x1GzLukIGRwRzN1P/1jM9e/8QZ2MDYzM7kda3jY4+VnodE4ErepRERABERABEYgvAZeEiTdy255SB6hbAgkrNHoGYIWudp7Ht+uNVlDUCoveCzzkFSoNx+CoAtifwstiGbd48WLq17d6o7pEQAREQAREIDoE7KjZN0cu4L0xC1lT5KjZFrUrBYyOU+2o2QruHjXrko6QwRHBnLzg5VEMm7GSfzcYzpmrn4XyVaH/dChnXzjpEgEREAEREAF/EHBJmHjEZgOm5BqXQPB/wPnAXsDaPRC2VSDmRFgNjtwyGBx3A3cVb18Ghz/mtKIUAREQAT8SsKNmv564jNdHzGfs/J1HzVbIyuCkTvUDZkf7Bu4dNeuSjpDBUcaZv3x9Dj0eHEJ+QQGT6txF5fWz4MBL4fjHytiiHhMBERABERCBxBBwSZh4BCJdwXEeYEaI1fD42WvTjprVCo7ETDH1KgIiIAIiECaBKUvW88bI+Xzy+2I2FzlqtlOj6pzfvQnH7be3M0fNuqQjZHCEOdEKbx/002we/Hoah1aYw6sFdwR/fOUw2LtjGVvUYyIgAiIgAiKQGAIuCROPQCQ1OGzrykKvuOgNRYg2AIYCb3h1OVaVsgKkeDJsNchireBIzBxVryIgAiKQqgQ25OQGjpq1Wh0zlm/cgaF6Re+o2W5NaJrgo2Zd0hEyOMrwSrFjfo56fFhggn1U/006r/kyaGyYwaFLBERABERABHxGwCVh4qEr7RQVe8M9ZjeYqwM71/XuPhc3A4+EkSoZHGHA0q0iIAIiIALRJWCfQUfNXRPYvvLt5GW7HDVrqzoOb5OYo2Zd0hEyOMow79ZtyeWiV0Yxc8ESJlTuR/r2LXDcY9Dl0jK0pkdEQAREQAREILEEXBImHokOwHjgY+C0InT6AU94NThsJYZdLbx6HdO8/7faHSeVQNROXXkGsKPOXgImADPCIC+DIwxYulUEREAERCB2BFZsyOE976jZJetydnS0d7VszunamLO6NqJOFTtZPT6XSzpCBkcEOV819DlqDf0HZFWE/tMg272CLxEMT4+KgAiIgAikCAGXhEkR5E8C13omx1dAW+A6wI6JPRzI9+6dBzQBStM04dbgKJ59GRwp8nrQMEVABETALwTsqNkfpgWPmv1pxsodYdtRs0e1rxeo1dGtWQ3S0kp7i4xsxC7piNiONDJO0Xw6NqJk0CGw9A/odC6cbF8K6RIBERABERAB/xFwSZgUoZcBWA2NK7wTUKxmxrvAncDOTcggg8N/U04Ri4AIiIAIRJnAvFWbeGtU8KjZtZvt8LDg1bJO5cDpK6d0bkDV7NgcNeuSjpDBUdaJtXQ8DLLi7MAl30Lj7mVtSc+JgAiIgAiIQEIJuCRMEgpiz53H5ssShwes0ERABERABPxHICc3jy8nLA2cwPL7gp2nqVcsZ0fNNuC87o1pVz+6Ow9c0hEyOMo6Z7/sD6NfhFqt4ZqREONlP2UNU8+JgAiIgAiIQGkEXBImpcWawH+XwZFA+OpaBERABEQgfAKTFq/jzcBRs0vYkpu3o4HOjasHVnWc3KkB6emRWwIu6YjIRxM+50Q8EV1Rsm0zPNoGtq6DIwfCQbZFWJcIiIAIiIAI+JOAS8LEYYLR1RIOD1ShiYAIiIAIJBcBOyTj43GLAiewzF65KTC4tntX5avrekWlPodLOkIGR1nmbu4WGP82/PEWnP0uVKpZllb0jAiIgAiIgAg4QcAlYeIEkJKDkMHhcHIUmgiIgAiIQOkE7KjZEXPWBIqSHtKqNmd2aVT6QyHc4ZKOkMERQsJ0iwiIgAiIgAgkMwGXhInDnGVwOJwchSYCIiACIpA4Ai7pCBkciZsH6lkEREAEREAEnCDgkjBxAkjJQcjgcDg5Ck0EREAERCBxBFzSETI4EjcP1LMIiIAIiIAIOEHAJWHiBBAZHA6nQaGJgAiIgAi4RsAlHSGDw7XZoXhEQAREQAREIM4EXBImcR56ON1pBUc4tHSvCIiACIhAyhBwSUfI4EiZaaeBioAIiIAIiEDJBFwSJg7nSAaHw8lRaCIgAiIgAokj4JKOkMGRuHmgnkVABERABETACQIuCRMngJQchAwOh5Oj0ERABERABBJHwCUdIYMjcfNAPYuACIiACIiAEwRcEiZOAJHB4XAaFJoIiIAIiIBrBFzSETI4XJsdikcEREAEREAE4kzAJWES56GH051WcIRDS/eKgAiIgAikDAGXdIQMjpSZdhqoCIiACIiACJRMwCVh4nCOZHA4nByFJgIiIAIikDgCLukIGRyJmwfqWQREQAREQAScIOCSMHECSMlByOBwODkKTQREQAREIHEEXNIRMjgSNw/UswiIgAiIgAg4QcAlYeIEEBkcDqdBoYmACIiACLhGwCUdIYPDtdmheERABERABEQgzgRcEiZxHno43WkFRzi0dK8IiIAIiEDKEHBJR8jgSJlpp4GKgAiIgAiIQMkEXBImDudIBofDyVFoIiACIiACiSPgko6QwZG4eaCeRUAEREAERMAJAi4JEyeAlByEDA6Hk6PQREAEREAEEkfAJR0hgyNx80A9i4AIiIAIiIATBFwSJk4AkcHhcBoUmgiIgAiIgGsEXNIRMjhcmx2KRwREQAREQATiTMAlYRLnoYfTnVZwhENL94qACIiACKQMAZd0hAyOlJl2GqgIiIAIiIAIlEzAJWHicI5kcDicHIUmAiIgAiKQOAIu6QgZHImbB+pZBERABERABJwg4JIwcQJIyUHI4HA4OQpNBERABEQgcQRc0hEyOBI3D9SzCIiACIiACDhBwCVh4gQQGRwOp0GhiYAIiIAIuEbAJR2RKgZHQ2Dh2LFjqVevnmvzQfGIgAiIgAiIQEIJLFu2jAMOOMBiaAQsSmgw7nYuLeFubhSZCIiACIhAAgm4pCNSxeDYHxiXwJyraxEQAREQARHwA4HOwO9+CDQBMUpLJAC6uhQBERABEfAVgYTriFQxOLKA9sBKID9KU6SuZ5pYEpdHqc14NOPXuI2NX2NX3PGY2Tv78CtvzfH4zhPx/ivvdKA2MAnIjX8qfNGjtIT/f8/69f1Bccf/V4SYx5e5eMeXdyw0kDM6IlUMjlhMmUCxMaABsCQWHcSoTb/GbTj8GrvijtFk3k2zfuWtOR7feSLe8eetHncl4NffV4o7vrNZvOPLW+8P4h0qAb++Nv08x0vNjQyOUhHt9ga/Tmi/xu3nF6JfmSvusv9+KOuTYl5WcmV7TrzLxk1PRY+A5mD0WIbSkniHQil69/iVtzRn9OZAqC35da74NW4/z/FS55QMjlIRyeAoO6KoP+nXXyKKO+pTYY8N+pW3n99s/Mpcccf3tanediWgORjfWSHe4h0qAc2VUElF5z7xjg7HcFrxK/NSxyiDo1REu72hCtAfeBTYUPZm4v6kX+M2UH6NXXHHd5r7lbfmeHzniXjHn7d63JWAX39fKe74zmbxji9vvT+Id6gE/Pra9PMcLzU3MjhKRaQbREAEREAEREAEREAEREAEREAEREAEXCcgg8P1DCk+ERABERABERABERABERABERABERCBUgnI4CgVkW4QAREQAREQAREQAREQAREQAREQARFwnYAMDtczpPhEQAREQAREQAREQAREQAREQAREQARKJSCDo1REukEEREAEREAEREAEREAEREAEREAERMB1AjI4ws/QbUBn4ACgGTAfaBp+M3F9ohVwHnAk0ALIBmYD7wOPA5viGk3onbUG7vR421FGWcAC4CvgYWBp6E0l/M6KwCRvzjwNXJvwiHYfQMFu/snmSWWH47bQagADgJOBht4JR8bd5tFwB2O/G7hrD3Ft9+a9g6EH5sJ1wNne78CtwAzgeeA1YHfzKNFjqQvcAxwH2N+XAR97eVib6OC8/sN9n+kGDATsv8b9V+BW4A9HxqMw/kog3Py6wM+vOsLYJYuWkI6IzyvBbzrCqEhLxGduFO3FdS0R7vtMUukIGRzhvyBMPK4Bxnkmx3ofGBz/Aq4BPgNGALnAYcCZwASgO7AlfBQxf6IPcLsX8yLAPux1AC4GjHsnYEXMo4hOB48AV3oGgR8MDjMD7INq0cvmzbvRwRGTVpoAQz3GL3kftqsB+wHfAu/EpNfI+Hg1HgAAEJRJREFUGrXY7E/xy352s/fB+9TIuojJ0+nAT8BBnplhv1dMfJvZ0RX4N/CPmPQcWaN1gFGAGaaDPNOxvffanAz0BDZH1kVUng7nfcZ+f9u8Xww85fVuBqqN1fIzMSoRqZFoEggnv9HsN5K2/KojbMzJoiWkIyKZwaE960cdYSOTlggtv9G6yw9aIpz3maTTETI4wp/qzYE53mP2zbB9i+n6Co4DgZnAumLDvd8zEPoVEcbhE4n/E2cA73kfoOyDlOuXrfixD1W3AI8CfjA47Bv4i1wHWyw+M2XstWgfsP20uqckzPbh+wrgeOBLB/PQw1slYCvAbiwSXzlgmreSprqDcVu81wPnAG8Xic+MmbeAfwL2ezHRVzjvM/a7pQ3Q1jM5LPYGwFTPHLaVe7rcIhBOfl2JPNl0hHH1k5aQjojPKyGZdIQRk5aIzbzxg5YI530m6XSEDI7IJr5fDI7djdJWQ9gKDvsFeFVkKOL6tH2AHQnYN0q2BMvlK8MzN+wDt32rOtdHBod9wLYPrBtdBuzFdrC3osC2TDzpbeuwLU0ufBsfLr5KwBJvlZIZNnnhNhCH+48CvvFMO9suVvSyN0r7gG1/XLvGAy0BY1x0C42tSLEtWMbdtvG5dO3pfWYfz7x+Gbi0WNC2islWu9lqFduGo8tNAtIRicuLX7SEdER85kgy6QgjJi0Ru3njNy2RcjpCBkdkk9/vwuQYr57FvaXUAYiMUuRPW80QWylj/90XeMjbnmJvRi7WVSg64v/z9vu3837oF4PDPuwZbxNWK72tKXeUsAoo8uxGpwUzu2xLhNXeuAywuW2x28olm99vRKebuLRiK2de8VYS2IoCF6+9vJVstm3s757haFtULvRMDzNMX3AwcFtdYktLbY918cu2Htq4agOrHIp9T+8zhStPLgdeLBaz/cy2mbm6CsghxAkNRToifvj9qiWkI+IzR5JJRxgxaYnYzRu/aYmU0xEyOCKb/H4WJvbhz8yBLoDtQZ8eGYqYPm0rH+xb+cJrHmAftt+Maa+RN25FaG2O2AdsM2Xs23g/GBy2OsYK0M4CqgLHAmd5e/ltT7+LKzqsSKSZG2bGmKnxjLf6pD9g5tIlnmkQeVZj34K9Lq0WhK0ksPni6tXb+1BtxQcLrw3ABcAnjgb9IWA1TfYvVoDT6vn87sVsBaStxpIr157eZ2x+2758e41+XSxg+5ltb7LaP8Xr6bgyNsURfI/ww1bXknLlJx1h8ftRS0hHxO+3RDLpCKMmLRG7ueM3LZFyOkIGR2ST38/CxAwDe7O3EycejAxDzJ+20zBsj7mJQPtgciLwKvDfmPccWQdW2NKWh9veWSvQ6ReDo6RR2zyxUxrMWLL/unYN9grJWX0cq0WwzQuwcKVBjrdlIt+1wIvFY9X+7ZuBIUBfx2O116LNB2Nup3bYqggrZmyv1ZOA7x2M30wZK8hpp0jd4BmQZoDZflr7IGHbmuyenx2KfU/vM7bCxwxUK6L4Q7GYD/fmkdVIsfHpcpOAdET88uJHLSEdEb/5kSw6wohJS8R23vhNS6ScjpDBEdkLwK/C5D7vg4l9q2ff7vntsmrRo71jsVw1Z+xY3v8Bto2m8MOSnw0O++BnKzfGeiczuDZnPveW4heaMEXjs4KptqrAtjdZ4UWXLyuaa6en2NYDF099KWRn9Xus1oZ9eH6uCNDCYwytpoWtQHGxfogVFnwCqOfFbTHa9g7bunIK0NGrTeTKPEm5b15cAR+nOKQj4gS6hG5c1xLSEfGdG8miI4yatETs546ftETK6QgZHJG9APwoTArPyrY9/laUrmihvchoxPdpO5bSihg2im+3IfVWHljofQC0b4kLL4vXvj22ehD3ePv814bUohs32XYJW4lSdEuCG5HBs16h3JJOBCrcV2vbPmylgatXJmDHIdt/ba5sdTVQwIpaWgHLWsDqYnEWrg6zApi2UsLFy5bWm0lTxdueZ8dNm2Fjq1LsaGGXitOm3N5ZFydMDGOSjogh3BCadlVLSEeEkLwo35IMOsKQSEtEeWLsoTm/aImU0xEyOCJ7EfhNmBSaG/aNttUkcH25/p6yYxWM7QOUVYl27bLjMf8MISj7pt72z/vhsuJsVl/BxKAtzXPtsg/b9qHbap3cWiw4M5TO9U7PsLoirl62euAjb+tVUWPMxXht2bQdP1oXMHOg6FUoEm2risu1fYrGbKs5zJT8ycGtQSlX/dzFCR/DmKQjYgg3hKZd1RLSESEkL8q3JIOOMCTSElGeGGE056qWSDkdIYMjjFlbwq1+EiZ3eqsGXvcqK/vB3LBfFCUdb3gYYHslbTWE7T137bLtHFaDoPhlpzNY8Us7XtOOcLQjemc4FnzNEr6RtxDtKFCr5G4nldjSR9cuq7Ux3zta1T5YFxZC3dsrOrrY25PqWtxF4/kCOA6wZdMTXQ4U+I9Xw6L4fDBRPsU7gcfmu4tbVIqjte00th3odO/3yY+OsS/tfca269l+a5v3dsytXVb7x2q52KoU12u5OIY77uGUlt+4B7SHDv2oI2w4ftQS0hHxn/nJoCOMmrRE/OeO9eiylijtfSbpdIQMjvBfBOcDTbzHbDl8OeBR7//tA5YZCK5dVvjvKWABYEXpipsbyx0tCGgVre0DqhXPM7a2isBOOPibt4T80GInIbjGvXg8fqjBYR9cuwP2Ic/mixV2tdMYzFSy01Xsv1scBX0FMAiY7K3msNfm1d4csqMyv3M07sIPpMbbapx0czjOwtDsd6CdNGKC0E4z+sUrMmpHk9o8t985Zua5dtl8tg/99rvFtlzZdhSrd2K/V24HHnAk4HDeZ+xkI3u92vamwtOm7L3JVtfYtiz7hlqXWwTCya8rkftVRxi/ZNIS0hGxfUX4WUdIS8R2bhRt3Q9aIpz3maTTETI4wn8x2KqBQ3bzmC1vtg/drl124siFewjK1bjP9IpDWtE/+zbY6oWY0WGnM9iKAvtA6KfLD8LEVp783Ts62FZz2Dfwduzqe8BjgJ1G4vJlR4De4tVXMCPvN2/lkn0Ad/kqPKXGxNULLgdaJDYrImrf6NoqKvswbcbXH96JHbbVxsXLTC/bomcmnpmnVmvDvrmwuW3bbly5wn2f6QHc75lj9nvSas3c5thxt66wdSGOcPPrQsx+1RHGLpm0hHRE7F8NftURRkZaIvbzw3rwg5YI930mqXSEDI74vBDUiwiIgAiIgAiIgAiIgAiIgAiIgAiIQAwJyOCIIVw1LQIiIAIiIAIiIAIiIAIiIAIiIAIiEB8CMjjiw1m9iIAIiIAIiIAIiIAIiIAIiIAIiIAIxJCADI4YwlXTIiACIiACIiACIiACIiACIiACIiAC8SEggyM+nNWLCIiACIiACIiACIiACIiACIiACIhADAnI4IghXDUtAiIgAiIgAiIgAiIgAiIgAiIgAiIQHwIyOOLDWb2IgAiIgAiIgAiIgAiIgAiIgAiIgAjEkIAMjhjCVdMiIAIiIAIiIAIiIAIiIAIiIAIiIALxISCDIz6c1YsIiIAIiIAIiIAIiIAIiIAIiIAIiEAMCcjgiCFcNS0CIiACIiACIiACIiACIiACIiACIhAfAjI44sNZvYiACERO4FDgR+Bi4NXIm1MLIiACIiACIiACKURAOiKFkq2hpi4BGRypm3uNPPUIFL6x3ww8AlQHbgCGen9cINIJONkzMOYVC0jCxIUMKQYREAEREIFUJSAdkaqZ17hFwEcEZHD4KFkKVQQiJFBcmDQF5gL3AHdH2Ha0Hr8IeAU4rATTJR0oB+QCedHqUO2IgAiIgAiIgAiEREA6IiRMukkERCCRBGRwJJK++haB+BKItzCpAmwIc4h7MjjCbEq3i4AIiIAIiIAIRJGAdEQUYaopERCB2BCQwREbrmpVBFwkUFSYjPHqWRSPcz5gKzsKr7OAfkBHIAOYCDwMfFDswQLgNeB1b0WIbTWxPqzP+kB/oA/QBKgAzPHut60yhasxbBXJXSWAs3bN+NjdFpVKwB3AmUBD4E/gO+CfgI2n8Cr6vP3u+z9gH2AZ8DTwbxeTpphEQAREQAREwBEC0hE7a4FJRzgyKRWGCBQnIINDc0IEUodAUWFiRsTZwH+Aj4GPPAwbgU+8v98P3A58A3wL5AOneEbDtZ4pUEjPDI7JnoHxAjDV+wf7+9HAs14/s4Es72dHAc8DV3r37gdcA1wBPFCkDXvmt90YHNaWFR7t6Zku9veWwNXAWuBAYJHXfuH4RwJ1gZe8e84DugHnAm+lznTQSEVABERABEQgLALSEUHNIR0R1rTRzSIQXwIyOOLLW72JQCIJhLO0tDMwFngQGFAsaDNADgcaFNmCYgaHXUcAg4vdbys2coDCewr/2UyWc7xVF0u9H+5pi0pJKzgu90wSW1VyS5F+jwO+AN4Azi9mcFhfbYF13s8reis9ZgE9Epkg9S0CIiACIiACDhOQjggaHNIRDk9ShSYCMjg0B0QgdQiEI0weBW70jIDVxRCd6K1+sBUYthXELjMvxgO2NWVPlxUJrQxYwVBb2WEmh7X3eRkNjq8Ai6OWtzWlaN+/Ay2802Js9Unh+G11iK1MKXpZ/2ZuWDu6REAEREAEREAEdiUgHRE0OKQj9OoQAYcJyOBwODkKTQSiTCAcYWLGwTGl9H+BZ1AUGhzve3Uwij+WCdwK2P1W86L4750Lgf+V0eCwrTBmmDQqIVZbvWHbTmw7yooiBsdlnkFT9JFXAYtDvxOjPOnUnAiIgAiIQNIQkI4IGhzSEUkzpTWQZCQgMZ+MWdWYROD/2ztDF6miKA5//4GIySKmDdZFo2LdqmB2kxisFkEMBpOgsFg2GLUZRGxiVBARsRhUBEEwWOzyg3Px8njzcAZmdvfx3TLMMPfd+753Hxx+nHN+4wSWCUxeVGZERI5FlqzpudFKS1qT0ZSYDMeDalT6BMh1IzbE6jVlMPeAq0AEhoxlS1RWETj69dpeFTh8ayQgAQlIQALTBIwj/jUZbXGLcYRvjQQOGQEFjkP2QNyOBNZIYBiYxNHka7mexMGkH02UONM1+5za2pTAEVeTD8CFwQWuVfPRXnBIFkWChovAq8H/x3pwPK9SlxPVMLSf8q5KVI5Xg9RFLiyZo8CxxoPnpSUgAQlIYBYEjCMUOGZxkL2JeRNQ4Jj38/XuJNATGAYmEQV+AQ+BGwNUZ4E35ahyeSSLI2UfP7s5UwJH1vgEnO/+H2vXCBBbgwyOS+WGks/m7NKmTTUZTSZIymDaSOZJymzGmoyaweF7IQEJSEACEliegHGEAsfyp8YZEtgwAQWODQN3OQkcIIFhYJKtfAaOAXdLsPjTNfy8DSSz4yOQ/ho/gJPANrADpGFoG1MCx6Oygn1aDisRR3aBNC+NjWsvOJwCvgDvgT0g+8n3WLKNCRy9TWxKYF5Xn4/rldERoeZ7bdIMjgM8fC4tAQlIQAJHnoBxhALHkT/E3sD8CShwzP8Ze4cSaATGApNzwP1yP2l2qac7ZLFbTXZHhIJkXaR/RgSPZ0CEi/8ROHLdO9WANOJGBId94G0JHsOMipSp3CyhIgLG4+rNsUigyL5uAVfKcvY38LJ++9btUYHDd0ECEpCABCSwOgHjCAWO1U+PMyWwIQIKHBsC7TISkIAEJCABCUhAAhKQgAQkIAEJrI/AX9KZD9volKQ/AAAAAElFTkSuQmCC\" 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"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
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"data": {
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"output_type": "execute_result"
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"data": {
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"data": {
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"execution_count": 13,
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{
"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>]"
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"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x157191c10>]"
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"execution_count": 13,
"metadata": {},
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"data": {
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"data": {
"text/plain": [
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"data": {
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],
"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])"
]
}
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