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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Convolutional Neural Network Example\n",
+ "\n",
+ "Build a convolutional neural network with TensorFlow.\n",
+ "\n",
+ "This example is using TensorFlow layers API, see 'convolutional_network_raw' example\n",
+ "for a raw TensorFlow implementation with variables.\n",
+ "\n",
+ "- Author: Aymeric Damien\n",
+ "- Project: https://github.com/aymericdamien/TensorFlow-Examples/"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## MNIST Dataset Overview\n",
+ "\n",
+ "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flattened and converted to a 1-D numpy array of 784 features (28*28).\n",
+ "\n",
+ "\n",
+ "\n",
+ "More info: http://yann.lecun.com/exdb/mnist/\n",
+ "\n",
+ "## CNN Overview\n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Extracting /tmp/data/train-images-idx3-ubyte.gz\n",
+ "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n",
+ "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n",
+ "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n"
+ ]
+ }
+ ],
+ "source": [
+ "from __future__ import division, print_function, absolute_import\n",
+ "\n",
+ "# Import MNIST data\n",
+ "from tensorflow.examples.tutorials.mnist import input_data\n",
+ "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=False)\n",
+ "\n",
+ "import tensorflow as tf\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "# Training Parameters\n",
+ "learning_rate = 0.001\n",
+ "num_steps = 2000\n",
+ "batch_size = 128\n",
+ "\n",
+ "# Network Parameters\n",
+ "num_input = 784 # MNIST data input (img shape: 28*28)\n",
+ "num_classes = 10 # MNIST total classes (0-9 digits)\n",
+ "dropout = 0.25 # Dropout, probability to drop a unit"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "# Create the neural network\n",
+ "def conv_net(x_dict, n_classes, dropout, reuse, is_training):\n",
+ " \n",
+ " # Define a scope for reusing the variables\n",
+ " with tf.variable_scope('ConvNet', reuse=reuse):\n",
+ " # TF Estimator input is a dict, in case of multiple inputs\n",
+ " x = x_dict['images']\n",
+ "\n",
+ " # MNIST data input is a 1-D vector of 784 features (28*28 pixels)\n",
+ " # Reshape to match picture format [Height x Width x Channel]\n",
+ " # Tensor input become 4-D: [Batch Size, Height, Width, Channel]\n",
+ " x = tf.reshape(x, shape=[-1, 28, 28, 1])\n",
+ "\n",
+ " # Convolution Layer with 32 filters and a kernel size of 5\n",
+ " conv1 = tf.layers.conv2d(x, 32, 5, activation=tf.nn.relu)\n",
+ " # Max Pooling (down-sampling) with strides of 2 and kernel size of 2\n",
+ " conv1 = tf.layers.max_pooling2d(conv1, 2, 2)\n",
+ "\n",
+ " # Convolution Layer with 64 filters and a kernel size of 3\n",
+ " conv2 = tf.layers.conv2d(conv1, 64, 3, activation=tf.nn.relu)\n",
+ " # Max Pooling (down-sampling) with strides of 2 and kernel size of 2\n",
+ " conv2 = tf.layers.max_pooling2d(conv2, 2, 2)\n",
+ "\n",
+ " # Flatten the data to a 1-D vector for the fully connected layer\n",
+ " fc1 = tf.contrib.layers.flatten(conv2)\n",
+ "\n",
+ " # Fully connected layer (in tf contrib folder for now)\n",
+ " fc1 = tf.layers.dense(fc1, 1024)\n",
+ " # Apply Dropout (if is_training is False, dropout is not applied)\n",
+ " fc1 = tf.layers.dropout(fc1, rate=dropout, training=is_training)\n",
+ "\n",
+ " # Output layer, class prediction\n",
+ " out = tf.layers.dense(fc1, n_classes)\n",
+ "\n",
+ " return out"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "# Define the model function (following TF Estimator Template)\n",
+ "def model_fn(features, labels, mode):\n",
+ " \n",
+ " # Build the neural network\n",
+ " # Because Dropout have different behavior at training and prediction time, we\n",
+ " # need to create 2 distinct computation graphs that still share the same weights.\n",
+ " logits_train = conv_net(features, num_classes, dropout, reuse=False, is_training=True)\n",
+ " logits_test = conv_net(features, num_classes, dropout, reuse=True, is_training=False)\n",
+ " \n",
+ " # Predictions\n",
+ " pred_classes = tf.argmax(logits_test, axis=1)\n",
+ " pred_probas = tf.nn.softmax(logits_test)\n",
+ " \n",
+ " # If prediction mode, early return\n",
+ " if mode == tf.estimator.ModeKeys.PREDICT:\n",
+ " return tf.estimator.EstimatorSpec(mode, predictions=pred_classes) \n",
+ " \n",
+ " # Define loss and optimizer\n",
+ " loss_op = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(\n",
+ " logits=logits_train, labels=tf.cast(labels, dtype=tf.int32)))\n",
+ " optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n",
+ " train_op = optimizer.minimize(loss_op, global_step=tf.train.get_global_step())\n",
+ " \n",
+ " # Evaluate the accuracy of the model\n",
+ " acc_op = tf.metrics.accuracy(labels=labels, predictions=pred_classes)\n",
+ " \n",
+ " # TF Estimators requires to return a EstimatorSpec, that specify\n",
+ " # the different ops for training, evaluating, ...\n",
+ " estim_specs = tf.estimator.EstimatorSpec(\n",
+ " mode=mode,\n",
+ " predictions=pred_classes,\n",
+ " loss=loss_op,\n",
+ " train_op=train_op,\n",
+ " eval_metric_ops={'accuracy': acc_op})\n",
+ "\n",
+ " return estim_specs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "WARNING:tensorflow:Using temporary folder as model directory: /var/folders/c3/rhg93t4n0cjbgvkb9tp99_h40000gq/T/tmpqhkrjnut\n",
+ "INFO:tensorflow:Using default config.\n",
+ "INFO:tensorflow:Using config: {}\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Build the Estimator (i.e. class for training and evaluating network)\n",
+ "model = tf.estimator.Estimator(model_fn)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Create CheckpointSaverHook.\n",
+ "INFO:tensorflow:Saving checkpoints for 1 into /var/folders/c3/rhg93t4n0cjbgvkb9tp99_h40000gq/T/tmpqhkrjnut/model.ckpt.\n",
+ "INFO:tensorflow:step = 1, loss = 2.32753\n",
+ "INFO:tensorflow:global_step/sec: 12.1422\n",
+ "INFO:tensorflow:step = 101, loss = 0.127041 (8.237 sec)\n",
+ "INFO:tensorflow:global_step/sec: 11.4857\n",
+ "INFO:tensorflow:step = 201, loss = 0.0393114 (8.707 sec)\n",
+ "INFO:tensorflow:global_step/sec: 8.48714\n",
+ "INFO:tensorflow:step = 301, loss = 0.0843611 (11.783 sec)\n",
+ "INFO:tensorflow:global_step/sec: 7.15251\n",
+ "INFO:tensorflow:step = 401, loss = 0.0262136 (13.981 sec)\n",
+ "INFO:tensorflow:global_step/sec: 6.4515\n",
+ "INFO:tensorflow:step = 501, loss = 0.0497303 (15.500 sec)\n",
+ "INFO:tensorflow:global_step/sec: 6.70897\n",
+ "INFO:tensorflow:step = 601, loss = 0.0142919 (14.905 sec)\n",
+ "INFO:tensorflow:global_step/sec: 6.6801\n",
+ "INFO:tensorflow:step = 701, loss = 0.042797 (14.970 sec)\n",
+ "INFO:tensorflow:global_step/sec: 6.33292\n",
+ "INFO:tensorflow:step = 801, loss = 0.0159051 (15.790 sec)\n",
+ "INFO:tensorflow:global_step/sec: 6.51359\n",
+ "INFO:tensorflow:step = 901, loss = 0.0560886 (15.352 sec)\n",
+ "INFO:tensorflow:global_step/sec: 7.00529\n",
+ "INFO:tensorflow:step = 1001, loss = 0.0389398 (14.275 sec)\n",
+ "INFO:tensorflow:global_step/sec: 11.4372\n",
+ "INFO:tensorflow:step = 1101, loss = 0.000826523 (8.743 sec)\n",
+ "INFO:tensorflow:global_step/sec: 11.8734\n",
+ "INFO:tensorflow:step = 1201, loss = 0.0288895 (8.422 sec)\n",
+ "INFO:tensorflow:global_step/sec: 11.4033\n",
+ "INFO:tensorflow:step = 1301, loss = 0.0109369 (8.769 sec)\n",
+ "INFO:tensorflow:global_step/sec: 12.1424\n",
+ "INFO:tensorflow:step = 1401, loss = 0.016869 (8.236 sec)\n",
+ "INFO:tensorflow:global_step/sec: 12.2184\n",
+ "INFO:tensorflow:step = 1501, loss = 0.025425 (8.184 sec)\n",
+ "INFO:tensorflow:global_step/sec: 12.9909\n",
+ "INFO:tensorflow:step = 1601, loss = 0.00609953 (7.698 sec)\n",
+ "INFO:tensorflow:global_step/sec: 12.3233\n",
+ "INFO:tensorflow:step = 1701, loss = 0.0149776 (8.114 sec)\n",
+ "INFO:tensorflow:global_step/sec: 12.452\n",
+ "INFO:tensorflow:step = 1801, loss = 0.0356573 (8.031 sec)\n",
+ "INFO:tensorflow:global_step/sec: 12.6611\n",
+ "INFO:tensorflow:step = 1901, loss = 0.0126303 (7.898 sec)\n",
+ "INFO:tensorflow:Saving checkpoints for 2000 into /var/folders/c3/rhg93t4n0cjbgvkb9tp99_h40000gq/T/tmpqhkrjnut/model.ckpt.\n",
+ "INFO:tensorflow:Loss for final step: 0.0101263.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<tensorflow.python.estimator.estimator.Estimator at 0x124517438>"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Define the input function for training\n",
+ "input_fn = tf.estimator.inputs.numpy_input_fn(\n",
+ " x={'images': mnist.train.images}, y=mnist.train.labels,\n",
+ " batch_size=batch_size, num_epochs=None, shuffle=True)\n",
+ "# Train the Model\n",
+ "model.train(input_fn, steps=num_steps)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Starting evaluation at 2018-02-22-18:15:46\n",
+ "INFO:tensorflow:Restoring parameters from /var/folders/c3/rhg93t4n0cjbgvkb9tp99_h40000gq/T/tmpqhkrjnut/model.ckpt-2000\n",
+ "INFO:tensorflow:Finished evaluation at 2018-02-22-18:15:48\n",
+ "INFO:tensorflow:Saving dict for global step 2000: accuracy = 0.9878, global_step = 2000, loss = 0.0447589\n",
+ "WARNING:tensorflow:Skipping summary for global_step, must be a float or np.float32.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "{'accuracy': 0.9878, 'global_step': 2000, 'loss': 0.044758931}"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Evaluate the Model\n",
+ "# Define the input function for evaluating\n",
+ "input_fn = tf.estimator.inputs.numpy_input_fn(\n",
+ " x={'images': mnist.test.images}, y=mnist.test.labels,\n",
+ " batch_size=batch_size, shuffle=False)\n",
+ "# Use the Estimator 'evaluate' method\n",
+ "model.evaluate(input_fn)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Restoring parameters from /var/folders/c3/rhg93t4n0cjbgvkb9tp99_h40000gq/T/tmpqhkrjnut/model.ckpt-2000\n"
+ ]
+ },
+ {
+ "data": {
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+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x11be4d6d8>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model prediction: 7\n"
+ ]
+ },
+ {
+ "data": {
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+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x11c3670b8>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model prediction: 2\n"
+ ]
+ },
+ {
+ "data": {
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+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x11c2e1be0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model prediction: 1\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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ENgeMtPBpAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x11bd952e8>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model prediction: 0\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Predict single images\n",
+ "n_images = 4\n",
+ "# Get images from test set\n",
+ "test_images = mnist.test.images[:n_images]\n",
+ "# Prepare the input data\n",
+ "input_fn = tf.estimator.inputs.numpy_input_fn(\n",
+ " x={'images': test_images}, shuffle=False)\n",
+ "# Use the model to predict the images class\n",
+ "preds = list(model.predict(input_fn))\n",
+ "\n",
+ "# Display\n",
+ "for i in range(n_images):\n",
+ " plt.imshow(np.reshape(test_images[i], [28, 28]), cmap='gray')\n",
+ " plt.show()\n",
+ " print(\"Model prediction:\", preds[i])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "anaconda-cloud": {},
+ "kernelspec": {
+ "display_name": "Python [conda env:tensorflow]",
+ "language": "python",
+ "name": "conda-env-tensorflow-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.5.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}