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Add TensorFlow multilayer perceptrons notebook.
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‎README.md

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@@ -100,6 +100,7 @@ IPython Notebook(s) demonstrating deep learning functionality.
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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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| [tsf-mlp](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/3_neural_networks/multilayer_perceptron.ipynb) | Implement multilayer perceptrons in TensorFlow. |
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### tensor-flow-exercises
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‎deep-learning/tensor-flow-examples/notebooks/3_neural_networks/multilayer_perceptron.ipynb

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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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"# Multilayer Perceptron 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": 4,
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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_epochs = 15\n",
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"batch_size = 100\n",
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"display_step = 1"
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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_hidden_1 = 256 # 1st layer num features\n",
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"n_hidden_2 = 256 # 2nd layer num features\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)"
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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(\"float\", [None, n_input])\n",
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"y = tf.placeholder(\"float\", [None, n_classes])"
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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": 7,
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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 multilayer_perceptron(_X, _weights, _biases):\n",
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" #Hidden layer with RELU activation\n",
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" layer_1 = tf.nn.relu(tf.add(tf.matmul(_X, _weights['h1']), _biases['b1'])) \n",
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" #Hidden layer with RELU activation\n",
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" layer_2 = tf.nn.relu(tf.add(tf.matmul(layer_1, _weights['h2']), _biases['b2'])) \n",
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" return tf.matmul(layer_2, weights['out']) + biases['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": 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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"# Store layers weight & bias\n",
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"weights = {\n",
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" 'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),\n",
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" 'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),\n",
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" 'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes]))\n",
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"}\n",
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"biases = {\n",
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" 'b1': tf.Variable(tf.random_normal([n_hidden_1])),\n",
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" 'b2': tf.Variable(tf.random_normal([n_hidden_2])),\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": 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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"# Construct model\n",
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"pred = multilayer_perceptron(x, weights, biases)"
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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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"# Define loss and optimizer\n",
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"# Softmax loss\n",
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"cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y)) \n",
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"# Adam Optimizer\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": 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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"# 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": 12,
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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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"Epoch: 0001 cost= 160.113980416\n",
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"Epoch: 0002 cost= 38.665780694\n",
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"Epoch: 0003 cost= 24.118004577\n",
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"Epoch: 0004 cost= 16.440921303\n",
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"Epoch: 0005 cost= 11.689460141\n",
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"Epoch: 0006 cost= 8.469423468\n",
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"Epoch: 0007 cost= 6.223237230\n",
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"Epoch: 0008 cost= 4.560174118\n",
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"Epoch: 0009 cost= 3.250516910\n",
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"Epoch: 0010 cost= 2.359658795\n",
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"Epoch: 0011 cost= 1.694081847\n",
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"Epoch: 0012 cost= 1.167997509\n",
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"Epoch: 0013 cost= 0.872986831\n",
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"Epoch: 0014 cost= 0.630616366\n",
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"Epoch: 0015 cost= 0.487381571\n",
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"Optimization Finished!\n",
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"Accuracy: 0.9462\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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"\n",
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" # Training cycle\n",
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" for epoch in range(training_epochs):\n",
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" avg_cost = 0.\n",
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" total_batch = int(mnist.train.num_examples/batch_size)\n",
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" # Loop over all batches\n",
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" for i in range(total_batch):\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})\n",
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" # Compute average loss\n",
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" avg_cost += sess.run(cost, feed_dict={x: batch_xs, y: batch_ys})/total_batch\n",
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" # Display logs per epoch step\n",
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" if epoch % display_step == 0:\n",
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" print \"Epoch:\", '%04d' % (epoch+1), \"cost=\", \"{:.9f}\".format(avg_cost)\n",
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"\n",
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" print \"Optimization Finished!\"\n",
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"\n",
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" # Test model\n",
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" correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))\n",
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" # Calculate accuracy\n",
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" accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n",
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" print \"Accuracy:\", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})"
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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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