| { |
| "cells": [ |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "# Inference for CIFAR-10 dataset using predict BYOM\n", |
| "The predict BYOM function allows you to do inference using models that have not been trained with MADlib, but rather imported or created elsewhere. It was added in MADlib 1.17.\n", |
| "\n", |
| "In this workbook we train a model in Python using\n", |
| "https://keras.io/examples/cifar10_cnn/\n", |
| "and run inference on the validation set.\n", |
| "\n", |
| "## Table of contents\n", |
| "\n", |
| "<a href=\"#setup\">1. Setup</a>\n", |
| "\n", |
| "<a href=\"#train_model\">2. Train model in Python</a>\n", |
| "\n", |
| "<a href=\"#load_model\">3. Load model into table</a>\n", |
| "\n", |
| "<a href=\"#load_images\">4. Get validation data set and load into table</a>\n", |
| "\n", |
| "<a href=\"#inference\">5. Inference</a>" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "<a id=\"setup\"></a>\n", |
| "# 1. Setup" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 1, |
| "metadata": {}, |
| "outputs": [], |
| "source": [ |
| "%load_ext sql" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 2, |
| "metadata": {}, |
| "outputs": [], |
| "source": [ |
| "# Greenplum Database 5.x on GCP - via tunnel\n", |
| "%sql postgresql://gpadmin@localhost:8000/madlib\n", |
| " \n", |
| "# PostgreSQL local\n", |
| "#%sql postgresql://fmcquillan@localhost:5432/madlib" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 3, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "1 rows affected.\n" |
| ] |
| }, |
| { |
| "data": { |
| "text/html": [ |
| "<table>\n", |
| " <tr>\n", |
| " <th>version</th>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>MADlib version: 1.18.0-dev, git revision: rel/v1.17.0-89-g9d9f756, cmake configuration time: Thu Mar 4 23:11:53 UTC 2021, build type: release, build system: Linux-3.10.0-1160.11.1.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5</td>\n", |
| " </tr>\n", |
| "</table>" |
| ], |
| "text/plain": [ |
| "[(u'MADlib version: 1.18.0-dev, git revision: rel/v1.17.0-89-g9d9f756, cmake configuration time: Thu Mar 4 23:11:53 UTC 2021, build type: release, build system: Linux-3.10.0-1160.11.1.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5',)]" |
| ] |
| }, |
| "execution_count": 3, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "%sql select madlib.version();\n", |
| "#%sql select version();" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "<a id=\"train_model\"></a>\n", |
| "# 2. Train model in Python\n", |
| "\n", |
| "Train a model in Python using https://keras.io/examples/cifar10_cnn/\n", |
| "\n", |
| "Define model" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 4, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "x_train shape: (50000, 32, 32, 3)\n", |
| "50000 train samples\n", |
| "10000 test samples\n", |
| "WARNING:tensorflow:From /Users/fmcquillan/Library/Python/2.7/lib/python/site-packages/tensorflow/python/ops/init_ops.py:1251: calling __init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n", |
| "Instructions for updating:\n", |
| "Call initializer instance with the dtype argument instead of passing it to the constructor\n" |
| ] |
| } |
| ], |
| "source": [ |
| "from __future__ import print_function\n", |
| "from tensorflow import keras\n", |
| "from tensorflow.keras.datasets import cifar10\n", |
| "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", |
| "from tensorflow.keras.models import Sequential\n", |
| "from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten\n", |
| "from tensorflow.keras.layers import Conv2D, MaxPooling2D\n", |
| "import os\n", |
| "\n", |
| "batch_size = 32\n", |
| "num_classes = 10\n", |
| "epochs = 2\n", |
| "data_augmentation = True\n", |
| "num_predictions = 20\n", |
| "#save_dir = os.path.join(os.getcwd(), 'saved_models')\n", |
| "#model_name = 'keras_cifar10_trained_model.h5'\n", |
| "\n", |
| "# The data, split between train and test sets:\n", |
| "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n", |
| "print('x_train shape:', x_train.shape)\n", |
| "print(x_train.shape[0], 'train samples')\n", |
| "print(x_test.shape[0], 'test samples')\n", |
| "\n", |
| "# Convert class vectors to binary class matrices.\n", |
| "y_train = keras.utils.to_categorical(y_train, num_classes)\n", |
| "y_test = keras.utils.to_categorical(y_test, num_classes)\n", |
| "\n", |
| "model = Sequential()\n", |
| "model.add(Conv2D(32, (3, 3), padding='same',\n", |
| " input_shape=x_train.shape[1:]))\n", |
| "model.add(Activation('relu'))\n", |
| "model.add(Conv2D(32, (3, 3)))\n", |
| "model.add(Activation('relu'))\n", |
| "model.add(MaxPooling2D(pool_size=(2, 2)))\n", |
| "model.add(Dropout(0.25))\n", |
| "\n", |
| "model.add(Conv2D(64, (3, 3), padding='same'))\n", |
| "model.add(Activation('relu'))\n", |
| "model.add(Conv2D(64, (3, 3)))\n", |
| "model.add(Activation('relu'))\n", |
| "model.add(MaxPooling2D(pool_size=(2, 2)))\n", |
| "model.add(Dropout(0.25))\n", |
| "\n", |
| "model.add(Flatten())\n", |
| "model.add(Dense(512))\n", |
| "model.add(Activation('relu'))\n", |
| "model.add(Dropout(0.5))\n", |
| "model.add(Dense(num_classes))\n", |
| "model.add(Activation('softmax'))\n", |
| "\n", |
| "# initiate RMSprop optimizer\n", |
| "opt = keras.optimizers.RMSprop(lr=0.0001, decay=1e-6)\n", |
| "\n", |
| "# Let's train the model using RMSprop\n", |
| "model.compile(loss='categorical_crossentropy',\n", |
| " optimizer=opt,\n", |
| " metrics=['accuracy']);" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 5, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "data": { |
| "text/plain": [ |
| "'{\"class_name\": \"Sequential\", \"keras_version\": \"2.2.4-tf\", \"config\": {\"layers\": [{\"class_name\": \"Conv2D\", \"config\": {\"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"dtype\": \"float32\", \"seed\": null}}, \"name\": \"conv2d\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"linear\", \"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\": {\"dtype\": \"float32\"}}, \"batch_input_shape\": [null, 32, 32, 3], \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"Activation\", \"config\": {\"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"name\": \"activation\"}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"dtype\": \"float32\", \"seed\": null}}, \"name\": \"conv2d_1\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"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\": {\"dtype\": \"float32\"}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"Activation\", \"config\": {\"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"name\": \"activation_1\"}}, {\"class_name\": \"MaxPooling2D\", \"config\": {\"name\": \"max_pooling2d\", \"dtype\": \"float32\", \"trainable\": true, \"data_format\": \"channels_last\", \"pool_size\": [2, 2], \"padding\": \"valid\", \"strides\": [2, 2]}}, {\"class_name\": \"Dropout\", \"config\": {\"name\": \"dropout\", \"dtype\": \"float32\", \"trainable\": true, \"rate\": 0.25, \"seed\": null, \"noise_shape\": null}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"dtype\": \"float32\", \"seed\": null}}, \"name\": \"conv2d_2\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"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\": {\"dtype\": \"float32\"}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"Activation\", \"config\": {\"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"name\": \"activation_2\"}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"dtype\": \"float32\", \"seed\": null}}, \"name\": \"conv2d_3\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"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\": {\"dtype\": \"float32\"}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"Activation\", \"config\": {\"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"name\": \"activation_3\"}}, {\"class_name\": \"MaxPooling2D\", \"config\": {\"name\": \"max_pooling2d_1\", \"dtype\": \"float32\", \"trainable\": true, \"data_format\": \"channels_last\", \"pool_size\": [2, 2], \"padding\": \"valid\", \"strides\": [2, 2]}}, {\"class_name\": \"Dropout\", \"config\": {\"name\": \"dropout_1\", \"dtype\": \"float32\", \"trainable\": true, \"rate\": 0.25, \"seed\": null, \"noise_shape\": null}}, {\"class_name\": \"Flatten\", \"config\": {\"dtype\": \"float32\", \"trainable\": true, \"name\": \"flatten\", \"data_format\": \"channels_last\"}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"dtype\": \"float32\", \"seed\": null}}, \"name\": \"dense\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"linear\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {\"dtype\": \"float32\"}}, \"units\": 512, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Activation\", \"config\": {\"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"name\": \"activation_4\"}}, {\"class_name\": \"Dropout\", \"config\": {\"name\": \"dropout_2\", \"dtype\": \"float32\", \"trainable\": true, \"rate\": 0.5, \"seed\": null, \"noise_shape\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"dtype\": \"float32\", \"seed\": null}}, \"name\": \"dense_1\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"linear\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {\"dtype\": \"float32\"}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Activation\", \"config\": {\"dtype\": \"float32\", \"activation\": \"softmax\", \"trainable\": true, \"name\": \"activation_5\"}}], \"name\": \"sequential\"}, \"backend\": \"tensorflow\"}'" |
| ] |
| }, |
| "execution_count": 5, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "model.to_json()" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 7, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "Using real-time data augmentation.\n", |
| "Epoch 1/2\n", |
| "1563/1563 [==============================] - 107s 69ms/step - loss: 1.8637 - acc: 0.3142 - val_loss: 1.6037 - val_acc: 0.4154\n", |
| "Epoch 2/2\n", |
| "1563/1563 [==============================] - 116s 74ms/step - loss: 1.5880 - acc: 0.4174 - val_loss: 1.4362 - val_acc: 0.4754\n" |
| ] |
| }, |
| { |
| "data": { |
| "text/plain": [ |
| "<tensorflow.python.keras.callbacks.History at 0x14dfc98d0>" |
| ] |
| }, |
| "execution_count": 7, |
| "metadata": {}, |
| "output_type": "execute_result" |
| }, |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "10000/10000 [==============================] - 7s 698us/sample - loss: 1.4365 - acc: 0.4754\n", |
| "Test loss: 1.4364811393737793\n", |
| "Test accuracy: 0.4754\n" |
| ] |
| } |
| ], |
| "source": [ |
| "x_train = x_train.astype('float32')\n", |
| "x_test = x_test.astype('float32')\n", |
| "x_train /= 255\n", |
| "x_test /= 255\n", |
| "\n", |
| "if not data_augmentation:\n", |
| " print('Not using data augmentation.')\n", |
| " model.fit(x_train, y_train,\n", |
| " batch_size=batch_size,\n", |
| " epochs=epochs,\n", |
| " validation_data=(x_test, y_test),\n", |
| " shuffle=True)\n", |
| "else:\n", |
| " print('Using real-time data augmentation.')\n", |
| " # This will do preprocessing and realtime data augmentation:\n", |
| " datagen = ImageDataGenerator(\n", |
| " featurewise_center=False, # set input mean to 0 over the dataset\n", |
| " samplewise_center=False, # set each sample mean to 0\n", |
| " featurewise_std_normalization=False, # divide inputs by std of the dataset\n", |
| " samplewise_std_normalization=False, # divide each input by its std\n", |
| " zca_whitening=False, # apply ZCA whitening\n", |
| " zca_epsilon=1e-06, # epsilon for ZCA whitening\n", |
| " rotation_range=0, # randomly rotate images in the range (degrees, 0 to 180)\n", |
| " # randomly shift images horizontally (fraction of total width)\n", |
| " width_shift_range=0.1,\n", |
| " # randomly shift images vertically (fraction of total height)\n", |
| " height_shift_range=0.1,\n", |
| " shear_range=0., # set range for random shear\n", |
| " zoom_range=0., # set range for random zoom\n", |
| " channel_shift_range=0., # set range for random channel shifts\n", |
| " # set mode for filling points outside the input boundaries\n", |
| " fill_mode='nearest',\n", |
| " cval=0., # value used for fill_mode = \"constant\"\n", |
| " horizontal_flip=True, # randomly flip images\n", |
| " vertical_flip=False, # randomly flip images\n", |
| " # set rescaling factor (applied before any other transformation)\n", |
| " rescale=None,\n", |
| " # set function that will be applied on each input\n", |
| " preprocessing_function=None,\n", |
| " # image data format, either \"channels_first\" or \"channels_last\"\n", |
| " data_format=None,\n", |
| " # fraction of images reserved for validation (strictly between 0 and 1)\n", |
| " validation_split=0.0)\n", |
| "\n", |
| " # Compute quantities required for feature-wise normalization\n", |
| " # (std, mean, and principal components if ZCA whitening is applied).\n", |
| " datagen.fit(x_train)\n", |
| "\n", |
| " # Fit the model on the batches generated by datagen.flow().\n", |
| " model.fit_generator(datagen.flow(x_train, y_train,\n", |
| " batch_size=batch_size),\n", |
| " epochs=epochs,\n", |
| " validation_data=(x_test, y_test),\n", |
| " workers=1)\n", |
| "\n", |
| "# Save model and weights\n", |
| "#if not os.path.isdir(save_dir):\n", |
| "# os.makedirs(save_dir)\n", |
| "#model_path = os.path.join(save_dir, model_name)\n", |
| "#model.save(model_path)\n", |
| "#print('Saved trained model at %s ' % model_path)\n", |
| "\n", |
| "# Score trained model.\n", |
| "scores = model.evaluate(x_test, y_test, verbose=1)\n", |
| "print('Test loss:', scores[0])\n", |
| "print('Test accuracy:', scores[1])" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "<a id=\"load_model\"></a>\n", |
| "# 3. Load model into table\n", |
| "\n", |
| "Load the model architecture and weights into the model architecture table" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 10, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "Done.\n" |
| ] |
| }, |
| { |
| "data": { |
| "text/plain": [ |
| "[]" |
| ] |
| }, |
| "execution_count": 10, |
| "metadata": {}, |
| "output_type": "execute_result" |
| }, |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "1 rows affected.\n" |
| ] |
| }, |
| { |
| "data": { |
| "text/html": [ |
| "<table>\n", |
| " <tr>\n", |
| " <th>model_id</th>\n", |
| " <th>name</th>\n", |
| " <th>description</th>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>1</td>\n", |
| " <td>CIFAR10 model</td>\n", |
| " <td>CNN model with weights trained on CIFAR10.</td>\n", |
| " </tr>\n", |
| "</table>" |
| ], |
| "text/plain": [ |
| "[(1, u'CIFAR10 model', u'CNN model with weights trained on CIFAR10.')]" |
| ] |
| }, |
| "execution_count": 10, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "import psycopg2 as p2\n", |
| "conn = p2.connect('postgresql://gpadmin@localhost:8000/madlib')\n", |
| "#conn = p2.connect('postgresql://fmcquillan@localhost:5432/madlib')\n", |
| "cur = conn.cursor()\n", |
| "\n", |
| "from keras.layers import *\n", |
| "from keras import Sequential\n", |
| "import numpy as np\n", |
| "\n", |
| "# get weights, flatten and serialize\n", |
| "weights = model.get_weights()\n", |
| "weights_flat = [w.flatten() for w in weights]\n", |
| "weights1d = np.concatenate(weights_flat).ravel()\n", |
| "weights_bytea = p2.Binary(weights1d.tostring())\n", |
| "\n", |
| "%sql DROP TABLE IF EXISTS model_arch_library_cifar10;\n", |
| "query = \"SELECT madlib.load_keras_model('model_arch_library_cifar10', %s,%s,%s,%s)\"\n", |
| "cur.execute(query,[model.to_json(), weights_bytea, \"CIFAR10 model\", \"CNN model with weights trained on CIFAR10.\"])\n", |
| "conn.commit()\n", |
| "\n", |
| "# check weights loaded OK\n", |
| "%sql SELECT model_id, name, description FROM model_arch_library_cifar10;" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "<a id=\"load_images\"></a>\n", |
| "# 4. Get validation data set and load into table\n", |
| "\n", |
| "First set up image loader using the script called <em>madlib_image_loader.py</em> located at https://github.com/apache/madlib-site/tree/asf-site/community-artifacts/Deep-learning" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 9, |
| "metadata": {}, |
| "outputs": [], |
| "source": [ |
| "import sys\n", |
| "import os\n", |
| "madlib_site_dir = '/Users/fmcquillan/Documents/Product/MADlib/Demos/data'\n", |
| "sys.path.append(madlib_site_dir)\n", |
| "\n", |
| "# Import image loader module\n", |
| "from madlib_image_loader import ImageLoader, DbCredentials\n", |
| "\n", |
| "# Specify database credentials, for connecting to db\n", |
| "#db_creds = DbCredentials(user='fmcquillan',\n", |
| "# host='localhost',\n", |
| "# port='5432',\n", |
| "# password='')\n", |
| "\n", |
| "# Specify database credentials, for connecting to db\n", |
| "db_creds = DbCredentials(user='gpadmin', \n", |
| " db_name='madlib',\n", |
| " host='localhost',\n", |
| " port='8000',\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": [ |
| "Next load CIFAR-10 data from Keras consisting of 50,000 32x32 color training images, labeled over 10 categories, and 10,000 test images." |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 10, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "Done.\n" |
| ] |
| }, |
| { |
| "data": { |
| "text/plain": [ |
| "[]" |
| ] |
| }, |
| "execution_count": 10, |
| "metadata": {}, |
| "output_type": "execute_result" |
| }, |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "MainProcess: Connected to madlib db.\n", |
| "Executing: CREATE TABLE cifar_10_test_data (id SERIAL, x REAL[], y TEXT)\n", |
| "CREATE TABLE\n", |
| "Created table cifar_10_test_data in madlib db\n", |
| "Spawning 5 workers...\n", |
| "Initializing PoolWorker-1 [pid 95042]\n", |
| "PoolWorker-1: Created temporary directory /tmp/madlib_dTZhEGBDFE\n", |
| "Initializing PoolWorker-2 [pid 95043]\n", |
| "PoolWorker-2: Created temporary directory /tmp/madlib_ctWjbhcjwz\n", |
| "Initializing PoolWorker-3 [pid 95044]\n", |
| "PoolWorker-3: Created temporary directory /tmp/madlib_nx9VuMScrX\n", |
| "Initializing PoolWorker-4 [pid 95045]\n", |
| "PoolWorker-4: Created temporary directory /tmp/madlib_thkphNCw4r\n", |
| "Initializing PoolWorker-5 [pid 95046]\n", |
| "PoolWorker-5: Created temporary directory /tmp/madlib_037luEXgEL\n", |
| "PoolWorker-2: Connected to madlib db.\n", |
| "PoolWorker-3: Connected to madlib db.\n", |
| "PoolWorker-1: Connected to madlib db.\n", |
| "PoolWorker-5: Connected to madlib db.\n", |
| "PoolWorker-4: Connected to madlib db.\n", |
| "PoolWorker-3: Wrote 1000 images to /tmp/madlib_nx9VuMScrX/cifar_10_test_data0000.tmp\n", |
| "PoolWorker-1: Wrote 1000 images to /tmp/madlib_dTZhEGBDFE/cifar_10_test_data0000.tmp\n", |
| "PoolWorker-2: Wrote 1000 images to /tmp/madlib_ctWjbhcjwz/cifar_10_test_data0000.tmp\n", |
| "PoolWorker-5: Wrote 1000 images to /tmp/madlib_037luEXgEL/cifar_10_test_data0000.tmp\n", |
| "PoolWorker-4: Wrote 1000 images to /tmp/madlib_thkphNCw4r/cifar_10_test_data0000.tmp\n", |
| "PoolWorker-3: Loaded 1000 images into cifar_10_test_data\n", |
| "PoolWorker-3: Wrote 1000 images to /tmp/madlib_nx9VuMScrX/cifar_10_test_data0001.tmp\n", |
| "PoolWorker-1: Loaded 1000 images into cifar_10_test_data\n", |
| "PoolWorker-1: Wrote 1000 images to /tmp/madlib_dTZhEGBDFE/cifar_10_test_data0001.tmp\n", |
| "PoolWorker-2: Loaded 1000 images into cifar_10_test_data\n", |
| "PoolWorker-4: Loaded 1000 images into cifar_10_test_data\n", |
| "PoolWorker-5: Loaded 1000 images into cifar_10_test_data\n", |
| "PoolWorker-2: Wrote 1000 images to /tmp/madlib_ctWjbhcjwz/cifar_10_test_data0001.tmp\n", |
| "PoolWorker-4: Wrote 1000 images to /tmp/madlib_thkphNCw4r/cifar_10_test_data0001.tmp\n", |
| "PoolWorker-5: Wrote 1000 images to /tmp/madlib_037luEXgEL/cifar_10_test_data0001.tmp\n", |
| "PoolWorker-3: Loaded 1000 images into cifar_10_test_data\n", |
| "PoolWorker-1: Loaded 1000 images into cifar_10_test_data\n", |
| "PoolWorker-2: Loaded 1000 images into cifar_10_test_data\n", |
| "PoolWorker-4: Loaded 1000 images into cifar_10_test_data\n", |
| "PoolWorker-5: Loaded 1000 images into cifar_10_test_data\n", |
| "PoolWorker-1: Removed temporary directory /tmp/madlib_dTZhEGBDFE\n", |
| "PoolWorker-2: Removed temporary directory /tmp/madlib_ctWjbhcjwz\n", |
| "PoolWorker-3: Removed temporary directory /tmp/madlib_nx9VuMScrX\n", |
| "PoolWorker-5: Removed temporary directory /tmp/madlib_037luEXgEL\n", |
| "PoolWorker-4: Removed temporary directory /tmp/madlib_thkphNCw4r\n", |
| "Done! Loaded 10000 images in 108.267487049s\n", |
| "5 workers terminated.\n" |
| ] |
| } |
| ], |
| "source": [ |
| "from keras.datasets import cifar10\n", |
| "\n", |
| "# Load dataset into np array\n", |
| "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n", |
| "\n", |
| "%sql DROP TABLE IF EXISTS cifar_10_test_data;\n", |
| "\n", |
| "# Save images to temporary directories and load into database\n", |
| "#iloader.load_dataset_from_np(x_train, y_train, 'cifar_10_train_data', append=False)\n", |
| "iloader.load_dataset_from_np(x_test, y_test, 'cifar_10_test_data', append=False)" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "<a id=\"inference\"></a>\n", |
| "# 5. Inference" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 11, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "Done.\n" |
| ] |
| }, |
| { |
| "ename": "InternalError", |
| "evalue": "(psycopg2.errors.InternalError_) plpy.Error: Unable to get number of classes from model architecture. (plpython.c:5038)\nCONTEXT: Traceback (most recent call last):\n PL/Python function \"madlib_keras_predict_byom\", line 23, in <module>\n madlib_keras_predict.PredictBYOM(**globals())\n PL/Python function \"madlib_keras_predict_byom\", line 42, in wrapper\n PL/Python function \"madlib_keras_predict_byom\", line 314, in __init__\n PL/Python function \"madlib_keras_predict_byom\", line 326, in validate_and_set_defaults\n PL/Python function \"madlib_keras_predict_byom\", line 207, in set_default_class_values\n PL/Python function \"madlib_keras_predict_byom\", line 75, in get_num_classes\nPL/Python function \"madlib_keras_predict_byom\"\n\n[SQL: SELECT madlib.madlib_keras_predict_byom('model_arch_library_cifar10', -- model arch table\n 1, -- model arch id\n 'cifar_10_test_data', -- test_table\n 'id', -- id column\n 'x', -- independent var\n 'cifar10_predict_byom', -- output table\n 'response', -- prediction type\n FALSE, -- use gpus\n NULL, -- class values\n 255.0 -- normalizing const\n );]\n(Background on this error at: http://sqlalche.me/e/2j85)", |
| "output_type": "error", |
| "traceback": [ |
| "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", |
| "\u001b[0;31mInternalError\u001b[0m Traceback (most recent call last)", |
| "\u001b[0;32m<ipython-input-11-d7da0ccca3f1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_cell_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mu'sql'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mu''\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mu\"DROP TABLE IF EXISTS cifar10_predict_byom;\\n\\nSELECT madlib.madlib_keras_predict_byom('model_arch_library_cifar10', -- model arch table\\n 1, -- model arch id\\n 'cifar_10_test_data', -- test_table\\n 'id', -- id column\\n 'x', -- independent var\\n 'cifar10_predict_byom', -- output table\\n 'response', -- prediction type\\n FALSE, -- use gpus\\n NULL, -- class values\\n 255.0 -- normalizing const\\n );\\nSELECT * FROM cifar10_predict_byom ORDER BY id LIMIT 10;\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", |
| "\u001b[0;32m/Users/fmcquillan/Library/Python/2.7/lib/python/site-packages/IPython/core/interactiveshell.pyc\u001b[0m in \u001b[0;36mrun_cell_magic\u001b[0;34m(self, magic_name, line, cell)\u001b[0m\n\u001b[1;32m 2115\u001b[0m \u001b[0mmagic_arg_s\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvar_expand\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstack_depth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2116\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2117\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmagic_arg_s\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcell\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2118\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", |
| "\u001b[0;32m</Users/fmcquillan/Library/Python/2.7/lib/python/site-packages/decorator.pyc:decorator-gen-124>\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n", |
| "\u001b[0;32m/Users/fmcquillan/Library/Python/2.7/lib/python/site-packages/IPython/core/magic.pyc\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m 186\u001b[0m \u001b[0;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 187\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 188\u001b[0;31m \u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 189\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 190\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", |
| "\u001b[0;32m</Users/fmcquillan/Library/Python/2.7/lib/python/site-packages/decorator.pyc:decorator-gen-123>\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n", |
| "\u001b[0;32m/Users/fmcquillan/Library/Python/2.7/lib/python/site-packages/IPython/core/magic.pyc\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m 186\u001b[0m \u001b[0;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 187\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 188\u001b[0;31m \u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 189\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 190\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", |
| "\u001b[0;32m/Users/fmcquillan/Library/Python/2.7/lib/python/site-packages/sql/magic.pyc\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[1;32m 135\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 136\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 137\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msql\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparsed\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'sql'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muser_ns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 138\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 139\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumn_local_vars\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", |
| "\u001b[0;32m/Users/fmcquillan/Library/Python/2.7/lib/python/site-packages/sql/run.pyc\u001b[0m in \u001b[0;36mrun\u001b[0;34m(conn, sql, config, user_namespace)\u001b[0m\n\u001b[1;32m 361\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 362\u001b[0m \u001b[0mtxt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msqlalchemy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msql\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstatement\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 363\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtxt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muser_namespace\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 364\u001b[0m \u001b[0m_commit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 365\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeedback\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", |
| "\u001b[0;32m/Users/fmcquillan/Library/Python/2.7/lib/python/site-packages/sqlalchemy/engine/base.pyc\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, object_, *multiparams, **params)\u001b[0m\n\u001b[1;32m 980\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mObjectNotExecutableError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobject_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 981\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 982\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mmeth\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmultiparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 983\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 984\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_execute_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmultiparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", |
| "\u001b[0;32m/Users/fmcquillan/Library/Python/2.7/lib/python/site-packages/sqlalchemy/sql/elements.pyc\u001b[0m in \u001b[0;36m_execute_on_connection\u001b[0;34m(self, connection, multiparams, params)\u001b[0m\n\u001b[1;32m 285\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_execute_on_connection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconnection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmultiparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 286\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msupports_execution\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 287\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mconnection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_execute_clauseelement\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmultiparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 288\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 289\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mObjectNotExecutableError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", |
| "\u001b[0;32m/Users/fmcquillan/Library/Python/2.7/lib/python/site-packages/sqlalchemy/engine/base.pyc\u001b[0m in \u001b[0;36m_execute_clauseelement\u001b[0;34m(self, elem, multiparams, params)\u001b[0m\n\u001b[1;32m 1099\u001b[0m \u001b[0mdistilled_params\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1100\u001b[0m \u001b[0mcompiled_sql\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1101\u001b[0;31m \u001b[0mdistilled_params\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1102\u001b[0m )\n\u001b[1;32m 1103\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_has_events\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_has_events\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", |
| "\u001b[0;32m/Users/fmcquillan/Library/Python/2.7/lib/python/site-packages/sqlalchemy/engine/base.pyc\u001b[0m in \u001b[0;36m_execute_context\u001b[0;34m(self, dialect, constructor, statement, parameters, *args)\u001b[0m\n\u001b[1;32m 1248\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1249\u001b[0m self._handle_dbapi_exception(\n\u001b[0;32m-> 1250\u001b[0;31m \u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcursor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1251\u001b[0m )\n\u001b[1;32m 1252\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", |
| "\u001b[0;32m/Users/fmcquillan/Library/Python/2.7/lib/python/site-packages/sqlalchemy/engine/base.pyc\u001b[0m in \u001b[0;36m_handle_dbapi_exception\u001b[0;34m(self, e, statement, parameters, cursor, context)\u001b[0m\n\u001b[1;32m 1474\u001b[0m \u001b[0mutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_from_cause\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnewraise\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexc_info\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1475\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mshould_wrap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1476\u001b[0;31m \u001b[0mutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_from_cause\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msqlalchemy_exception\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexc_info\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1477\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1478\u001b[0m \u001b[0mutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", |
| "\u001b[0;32m/Users/fmcquillan/Library/Python/2.7/lib/python/site-packages/sqlalchemy/util/compat.pyc\u001b[0m in \u001b[0;36mraise_from_cause\u001b[0;34m(exception, exc_info)\u001b[0m\n\u001b[1;32m 396\u001b[0m \u001b[0mexc_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexc_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexc_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexc_info\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 397\u001b[0m \u001b[0mcause\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexc_value\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mexc_value\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mexception\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 398\u001b[0;31m \u001b[0mreraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexception\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexception\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtb\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexc_tb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcause\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcause\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 399\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 400\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", |
| "\u001b[0;32m/Users/fmcquillan/Library/Python/2.7/lib/python/site-packages/sqlalchemy/engine/base.pyc\u001b[0m in \u001b[0;36m_execute_context\u001b[0;34m(self, dialect, constructor, statement, parameters, *args)\u001b[0m\n\u001b[1;32m 1244\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mevt_handled\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1245\u001b[0m self.dialect.do_execute(\n\u001b[0;32m-> 1246\u001b[0;31m \u001b[0mcursor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1247\u001b[0m )\n\u001b[1;32m 1248\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", |
| "\u001b[0;32m/Users/fmcquillan/Library/Python/2.7/lib/python/site-packages/sqlalchemy/engine/default.pyc\u001b[0m in \u001b[0;36mdo_execute\u001b[0;34m(self, cursor, statement, parameters, context)\u001b[0m\n\u001b[1;32m 579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 580\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdo_execute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcursor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 581\u001b[0;31m \u001b[0mcursor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 582\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 583\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdo_execute_no_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcursor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", |
| "\u001b[0;31mInternalError\u001b[0m: (psycopg2.errors.InternalError_) plpy.Error: Unable to get number of classes from model architecture. (plpython.c:5038)\nCONTEXT: Traceback (most recent call last):\n PL/Python function \"madlib_keras_predict_byom\", line 23, in <module>\n madlib_keras_predict.PredictBYOM(**globals())\n PL/Python function \"madlib_keras_predict_byom\", line 42, in wrapper\n PL/Python function \"madlib_keras_predict_byom\", line 314, in __init__\n PL/Python function \"madlib_keras_predict_byom\", line 326, in validate_and_set_defaults\n PL/Python function \"madlib_keras_predict_byom\", line 207, in set_default_class_values\n PL/Python function \"madlib_keras_predict_byom\", line 75, in get_num_classes\nPL/Python function \"madlib_keras_predict_byom\"\n\n[SQL: SELECT madlib.madlib_keras_predict_byom('model_arch_library_cifar10', -- model arch table\n 1, -- model arch id\n 'cifar_10_test_data', -- test_table\n 'id', -- id column\n 'x', -- independent var\n 'cifar10_predict_byom', -- output table\n 'response', -- prediction type\n FALSE, -- use gpus\n NULL, -- class values\n 255.0 -- normalizing const\n );]\n(Background on this error at: http://sqlalche.me/e/2j85)" |
| ] |
| } |
| ], |
| "source": [ |
| "%%sql\n", |
| "DROP TABLE IF EXISTS cifar10_predict_byom;\n", |
| "\n", |
| "SELECT madlib.madlib_keras_predict_byom('model_arch_library_cifar10', -- model arch table\n", |
| " 1, -- model arch id\n", |
| " 'cifar_10_test_data', -- test_table\n", |
| " 'id', -- id column\n", |
| " 'x', -- independent var\n", |
| " 'cifar10_predict_byom', -- output table\n", |
| " 'response', -- prediction type\n", |
| " FALSE, -- use gpus\n", |
| " NULL, -- class values\n", |
| " 255.0 -- normalizing const\n", |
| " );\n", |
| "SELECT * FROM cifar10_predict_byom ORDER BY id LIMIT 10;" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "Number of missclassifications:" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 5, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "1 rows affected.\n" |
| ] |
| }, |
| { |
| "data": { |
| "text/html": [ |
| "<table>\n", |
| " <tr>\n", |
| " <th>count</th>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>2551</td>\n", |
| " </tr>\n", |
| "</table>" |
| ], |
| "text/plain": [ |
| "[(2551L,)]" |
| ] |
| }, |
| "execution_count": 5, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "%%sql\n", |
| "SELECT COUNT(*) FROM cifar10_predict_byom JOIN cifar_10_test_data USING (id)\n", |
| "WHERE cifar10_predict_byom.estimated_dependent_var != cifar_10_test_data.y;" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "Predict accuracy. From https://keras.io/examples/cifar10_cnn/ accuracy claim is 75% on validation set after 25 epochs. From run above test accuracy: 0.7449. MADlib predict BYOM accuracy matches:" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 6, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "1 rows affected.\n" |
| ] |
| }, |
| { |
| "data": { |
| "text/html": [ |
| "<table>\n", |
| " <tr>\n", |
| " <th>test_accuracy_percent</th>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>74.49</td>\n", |
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| "text/plain": [ |
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| }, |
| "execution_count": 6, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "%%sql\n", |
| "SELECT round(count(*)*100.0/10000.0, 2) as test_accuracy_percent from\n", |
| " (select cifar_10_test_data.y as actual, cifar10_predict_byom.estimated_dependent_var as estimated\n", |
| " from cifar10_predict_byom inner join cifar_10_test_data\n", |
| " on cifar_10_test_data.id=cifar10_predict_byom.id) q\n", |
| "WHERE q.actual=q.estimated;" |
| ] |
| } |
| ], |
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