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https://github.com/donnemartin/data-science-ipython-notebooks.git
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Add TensorFlow convolutional neural networks notebook.
This commit is contained in:
Donne Martin
parent
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@ -99,6 +99,7 @@ IPython Notebook(s) demonstrating deep learning functionality.
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| [tsf-logistic](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/2_basic_classifiers/logistic_regression.ipynb) | Implement logistic regression in TensorFlow. |
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| [tsf-nn](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/2_basic_classifiers/nearest_neighbor.ipynb) | Implement nearest neighboars in TensorFlow. |
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| [tsf-alex](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/3_neural_networks/alexnet.ipynb) | Implement AlexNet in TensorFlow. |
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| [tsf-cnn](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/3_neural_networks/convolutional_network.ipynb) | Implement convolutional neural networks in TensorFlow. |
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### tensor-flow-exercises
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@ -0,0 +1,321 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Convolutional Neural Network in TensorFlow\n",
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"\n",
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"Credits: Forked from [TensorFlow-Examples](https://github.com/aymericdamien/TensorFlow-Examples) by Aymeric Damien\n",
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"\n",
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"## Setup\n",
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"\n",
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"Refer to the [setup instructions](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/Setup_TensorFlow.md)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Extracting /tmp/data/train-images-idx3-ubyte.gz\n",
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"Extracting /tmp/data/train-labels-idx1-ubyte.gz\n",
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"Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n",
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"Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n"
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]
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}
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],
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"source": [
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"# Import MINST data\n",
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"import input_data\n",
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"mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"import tensorflow as tf"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# Parameters\n",
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"learning_rate = 0.001\n",
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"training_iters = 100000\n",
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"batch_size = 128\n",
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"display_step = 20"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# Network Parameters\n",
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"n_input = 784 # MNIST data input (img shape: 28*28)\n",
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"n_classes = 10 # MNIST total classes (0-9 digits)\n",
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"dropout = 0.75 # Dropout, probability to keep units"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# tf Graph input\n",
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"x = tf.placeholder(tf.types.float32, [None, n_input])\n",
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"y = tf.placeholder(tf.types.float32, [None, n_classes])\n",
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"keep_prob = tf.placeholder(tf.types.float32) #dropout (keep probability)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# Create model\n",
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"def conv2d(img, w, b):\n",
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" return tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(img, w, strides=[1, 1, 1, 1], \n",
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" padding='SAME'),b))\n",
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"\n",
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"def max_pool(img, k):\n",
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" return tf.nn.max_pool(img, ksize=[1, k, k, 1], strides=[1, k, k, 1], padding='SAME')\n",
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"\n",
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"def conv_net(_X, _weights, _biases, _dropout):\n",
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" # Reshape input picture\n",
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" _X = tf.reshape(_X, shape=[-1, 28, 28, 1])\n",
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"\n",
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" # Convolution Layer\n",
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" conv1 = conv2d(_X, _weights['wc1'], _biases['bc1'])\n",
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" # Max Pooling (down-sampling)\n",
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" conv1 = max_pool(conv1, k=2)\n",
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" # Apply Dropout\n",
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" conv1 = tf.nn.dropout(conv1, _dropout)\n",
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"\n",
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" # Convolution Layer\n",
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" conv2 = conv2d(conv1, _weights['wc2'], _biases['bc2'])\n",
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" # Max Pooling (down-sampling)\n",
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" conv2 = max_pool(conv2, k=2)\n",
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" # Apply Dropout\n",
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" conv2 = tf.nn.dropout(conv2, _dropout)\n",
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"\n",
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" # Fully connected layer\n",
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" # Reshape conv2 output to fit dense layer input\n",
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" dense1 = tf.reshape(conv2, [-1, _weights['wd1'].get_shape().as_list()[0]]) \n",
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" # Relu activation\n",
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" dense1 = tf.nn.relu(tf.add(tf.matmul(dense1, _weights['wd1']), _biases['bd1']))\n",
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" # Apply Dropout\n",
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" dense1 = tf.nn.dropout(dense1, _dropout) # Apply Dropout\n",
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"\n",
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" # Output, class prediction\n",
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" out = tf.add(tf.matmul(dense1, _weights['out']), _biases['out'])\n",
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" return out"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# Store layers weight & bias\n",
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"weights = {\n",
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" # 5x5 conv, 1 input, 32 outputs\n",
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" 'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])), \n",
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" # 5x5 conv, 32 inputs, 64 outputs\n",
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" 'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])), \n",
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" # fully connected, 7*7*64 inputs, 1024 outputs\n",
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" 'wd1': tf.Variable(tf.random_normal([7*7*64, 1024])), \n",
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" # 1024 inputs, 10 outputs (class prediction)\n",
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" 'out': tf.Variable(tf.random_normal([1024, n_classes])) \n",
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"}\n",
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"\n",
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"biases = {\n",
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" 'bc1': tf.Variable(tf.random_normal([32])),\n",
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" 'bc2': tf.Variable(tf.random_normal([64])),\n",
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" 'bd1': tf.Variable(tf.random_normal([1024])),\n",
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" 'out': tf.Variable(tf.random_normal([n_classes]))\n",
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"}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# Construct model\n",
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"pred = conv_net(x, weights, biases, keep_prob)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# Define loss and optimizer\n",
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"cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))\n",
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"optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# Evaluate model\n",
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"correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))\n",
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"accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.types.float32))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# Initializing the variables\n",
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"init = tf.initialize_all_variables()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Iter 2560, Minibatch Loss= 26046.011719, Training Accuracy= 0.21094\n",
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"Iter 5120, Minibatch Loss= 10456.769531, Training Accuracy= 0.52344\n",
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"Iter 7680, Minibatch Loss= 6273.207520, Training Accuracy= 0.71875\n",
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"Iter 10240, Minibatch Loss= 6276.231445, Training Accuracy= 0.64062\n",
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"Iter 12800, Minibatch Loss= 4188.221680, Training Accuracy= 0.77344\n",
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"Iter 15360, Minibatch Loss= 2717.077637, Training Accuracy= 0.80469\n",
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"Iter 17920, Minibatch Loss= 4057.120361, Training Accuracy= 0.81250\n",
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"Iter 20480, Minibatch Loss= 1696.550415, Training Accuracy= 0.87500\n",
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"Iter 23040, Minibatch Loss= 2525.317627, Training Accuracy= 0.85938\n",
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"Iter 25600, Minibatch Loss= 2341.906738, Training Accuracy= 0.87500\n",
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"Iter 28160, Minibatch Loss= 4200.535156, Training Accuracy= 0.79688\n",
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"Iter 30720, Minibatch Loss= 1888.964355, Training Accuracy= 0.89062\n",
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"Iter 33280, Minibatch Loss= 2167.645996, Training Accuracy= 0.84375\n",
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"Iter 35840, Minibatch Loss= 1932.107544, Training Accuracy= 0.89844\n",
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"Iter 38400, Minibatch Loss= 1562.430054, Training Accuracy= 0.90625\n",
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"Iter 40960, Minibatch Loss= 1676.755249, Training Accuracy= 0.84375\n",
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"Iter 43520, Minibatch Loss= 1003.626099, Training Accuracy= 0.93750\n",
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"Iter 46080, Minibatch Loss= 1176.615479, Training Accuracy= 0.86719\n",
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"Iter 48640, Minibatch Loss= 1260.592651, Training Accuracy= 0.88281\n",
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"Iter 51200, Minibatch Loss= 1399.667969, Training Accuracy= 0.86719\n",
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"Iter 53760, Minibatch Loss= 1259.961426, Training Accuracy= 0.89844\n",
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"Iter 56320, Minibatch Loss= 1415.800781, Training Accuracy= 0.89062\n",
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"Iter 58880, Minibatch Loss= 1835.365967, Training Accuracy= 0.85156\n",
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"Iter 61440, Minibatch Loss= 1395.168823, Training Accuracy= 0.90625\n",
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"Iter 64000, Minibatch Loss= 973.283569, Training Accuracy= 0.88281\n",
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"Iter 66560, Minibatch Loss= 818.093811, Training Accuracy= 0.92969\n",
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"Iter 69120, Minibatch Loss= 1178.744263, Training Accuracy= 0.92188\n",
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"Iter 71680, Minibatch Loss= 845.889709, Training Accuracy= 0.89844\n",
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"Iter 74240, Minibatch Loss= 1259.505615, Training Accuracy= 0.90625\n",
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"Iter 76800, Minibatch Loss= 738.037109, Training Accuracy= 0.89844\n",
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"Iter 79360, Minibatch Loss= 862.499146, Training Accuracy= 0.93750\n",
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"Iter 81920, Minibatch Loss= 739.704041, Training Accuracy= 0.90625\n",
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"Iter 84480, Minibatch Loss= 652.880310, Training Accuracy= 0.95312\n",
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"Iter 87040, Minibatch Loss= 635.464600, Training Accuracy= 0.92969\n",
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"Iter 89600, Minibatch Loss= 933.166626, Training Accuracy= 0.90625\n",
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"Iter 92160, Minibatch Loss= 213.874893, Training Accuracy= 0.96094\n",
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"Iter 94720, Minibatch Loss= 609.575684, Training Accuracy= 0.91406\n",
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"Iter 97280, Minibatch Loss= 560.208008, Training Accuracy= 0.93750\n",
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"Iter 99840, Minibatch Loss= 963.577148, Training Accuracy= 0.90625\n",
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"Optimization Finished!\n",
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"Testing Accuracy: 0.960938\n"
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]
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}
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],
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"source": [
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"# Launch the graph\n",
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"with tf.Session() as sess:\n",
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" sess.run(init)\n",
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" step = 1\n",
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" # Keep training until reach max iterations\n",
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" while step * batch_size < training_iters:\n",
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" batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n",
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" # Fit training using batch data\n",
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" sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys, keep_prob: dropout})\n",
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" if step % display_step == 0:\n",
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" # Calculate batch accuracy\n",
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" acc = sess.run(accuracy, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})\n",
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" # Calculate batch loss\n",
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" loss = sess.run(cost, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})\n",
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" print \"Iter \" + str(step*batch_size) + \", Minibatch Loss= \" + \\\n",
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" \"{:.6f}\".format(loss) + \", Training Accuracy= \" + \"{:.5f}\".format(acc)\n",
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" step += 1\n",
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" print \"Optimization Finished!\"\n",
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" # Calculate accuracy for 256 mnist test images\n",
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" print \"Testing Accuracy:\", sess.run(accuracy, feed_dict={x: mnist.test.images[:256], \n",
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" y: mnist.test.labels[:256], \n",
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" keep_prob: 1.})"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.4.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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