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@FavioVazquez
Created October 10, 2018 19:08
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Front Page DeepExplainer MNIST Example\n",
"\n",
"A simple example showing how to explain an MNIST CNN trained using Keras with DeepExplainer."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"x_train shape: (60000, 28, 28, 1)\n",
"60000 train samples\n",
"10000 test samples\n",
"Train on 60000 samples, validate on 10000 samples\n",
"Epoch 1/2\n",
"60000/60000 [==============================] - 135s 2ms/step - loss: 0.2570 - acc: 0.9211 - val_loss: 0.0624 - val_acc: 0.9798\n",
"Epoch 2/2\n",
"60000/60000 [==============================] - 132s 2ms/step - loss: 0.0876 - acc: 0.9736 - val_loss: 0.0456 - val_acc: 0.9858\n",
"Test loss: 0.045611984347738325\n",
"Test accuracy: 0.9858\n"
]
}
],
"source": [
"# this is the code from https://github.com/keras-team/keras/blob/master/examples/mnist_cnn.py\n",
"from __future__ import print_function\n",
"import keras\n",
"from keras.datasets import mnist\n",
"from keras.models import Sequential\n",
"from keras.layers import Dense, Dropout, Flatten\n",
"from keras.layers import Conv2D, MaxPooling2D\n",
"from keras import backend as K\n",
"\n",
"batch_size = 128\n",
"num_classes = 10\n",
"epochs = 12\n",
"\n",
"# input image dimensions\n",
"img_rows, img_cols = 28, 28\n",
"\n",
"# the data, split between train and test sets\n",
"(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
"\n",
"if K.image_data_format() == 'channels_first':\n",
" x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)\n",
" x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)\n",
" input_shape = (1, img_rows, img_cols)\n",
"else:\n",
" x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n",
" x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n",
" input_shape = (img_rows, img_cols, 1)\n",
"\n",
"x_train = x_train.astype('float32')\n",
"x_test = x_test.astype('float32')\n",
"x_train /= 255\n",
"x_test /= 255\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, kernel_size=(3, 3),\n",
" activation='relu',\n",
" input_shape=input_shape))\n",
"model.add(Conv2D(64, (3, 3), activation='relu'))\n",
"model.add(MaxPooling2D(pool_size=(2, 2)))\n",
"model.add(Dropout(0.25))\n",
"model.add(Flatten())\n",
"model.add(Dense(128, activation='relu'))\n",
"model.add(Dropout(0.5))\n",
"model.add(Dense(num_classes, activation='softmax'))\n",
"\n",
"model.compile(loss=keras.losses.categorical_crossentropy,\n",
" optimizer=keras.optimizers.Adadelta(),\n",
" metrics=['accuracy'])\n",
"\n",
"model.fit(x_train, y_train,\n",
" batch_size=batch_size,\n",
" epochs=epochs,\n",
" verbose=1,\n",
" validation_data=(x_test, y_test))\n",
"score = model.evaluate(x_test, y_test, verbose=0)\n",
"print('Test loss:', score[0])\n",
"print('Test accuracy:', score[1])"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# ...include code from https://github.com/keras-team/keras/blob/master/examples/mnist_cnn.py\n",
"\n",
"import shap\n",
"import numpy as np\n",
"\n",
"# select a set of background examples to take an expectation over\n",
"background = x_train[np.random.choice(x_train.shape[0], 100, replace=False)]\n",
"\n",
"# explain predictions of the model on three images\n",
"e = shap.DeepExplainer(model, background)\n",
"# ...or pass tensors directly\n",
"# e = shap.DeepExplainer((model.layers[0].input, model.layers[-1].output), background)\n",
"shap_values = e.shap_values(x_test[1:5])"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
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"text/plain": [
"<Figure size 1440x545.455 with 45 Axes>"
]
},
"metadata": {
"image/png": {
"height": 408,
"width": 1140
}
},
"output_type": "display_data"
}
],
"source": [
"# plot the feature attributions\n",
"shap.image_plot(shap_values, -x_test[1:5])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The plot above shows the explanations for each class on four predictions. Note that the explanations are ordered for the classes 0-9 going left to right along the rows."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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