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Add TensorFlow logistic regression notebook.
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Donne Martin
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@ -96,6 +96,7 @@ IPython Notebook(s) demonstrating deep learning functionality.
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| [tsf-basics](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/1_intro/basic_operations.ipynb) | Learn basic operations in TensorFlow, a library for various kinds of perceptual and language understanding tasks from Google. |
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| [tsf-linear](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/2_basic_classifiers/linear_regression.ipynb) | Implement linear regression in TensorFlow. |
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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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### tensor-flow-exercises
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@ -0,0 +1,227 @@
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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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"collapsed": true
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},
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"source": [
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"# Logistic Regression 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": 5,
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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": 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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"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": 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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"# Parameters\n",
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"learning_rate = 0.01\n",
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"training_epochs = 25\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": 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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"# tf Graph Input\n",
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"x = tf.placeholder(\"float\", [None, 784]) # mnist data image of shape 28*28=784\n",
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"y = tf.placeholder(\"float\", [None, 10]) # 0-9 digits recognition => 10 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": 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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"# Create model\n",
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"\n",
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"# Set model weights\n",
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"W = tf.Variable(tf.zeros([784, 10]))\n",
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"b = tf.Variable(tf.zeros([10]))"
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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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"activation = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax"
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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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"# Minimize error using cross entropy\n",
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"# Cross entropy\n",
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"cost = -tf.reduce_sum(y*tf.log(activation)) \n",
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"# Gradient Descent\n",
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"optimizer = tf.train.GradientDescentOptimizer(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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"# 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": 13,
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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= 29.860479714\n",
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"Epoch: 0002 cost= 22.080549484\n",
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"Epoch: 0003 cost= 21.237104595\n",
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"Epoch: 0004 cost= 20.460196280\n",
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"Epoch: 0005 cost= 20.185128237\n",
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"Epoch: 0006 cost= 19.940297202\n",
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"Epoch: 0007 cost= 19.645111119\n",
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"Epoch: 0008 cost= 19.507218031\n",
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"Epoch: 0009 cost= 19.389794492\n",
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"Epoch: 0010 cost= 19.177005816\n",
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"Epoch: 0011 cost= 19.082493615\n",
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"Epoch: 0012 cost= 19.072873598\n",
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"Epoch: 0013 cost= 18.938005402\n",
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"Epoch: 0014 cost= 18.891806430\n",
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"Epoch: 0015 cost= 18.839480221\n",
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"Epoch: 0016 cost= 18.769349510\n",
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"Epoch: 0017 cost= 18.590865587\n",
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"Epoch: 0018 cost= 18.623413677\n",
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"Epoch: 0019 cost= 18.546149085\n",
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"Epoch: 0020 cost= 18.432274895\n",
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"Epoch: 0021 cost= 18.358189004\n",
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"Epoch: 0022 cost= 18.380014628\n",
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"Epoch: 0023 cost= 18.499993471\n",
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"Epoch: 0024 cost= 18.386477311\n",
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"Epoch: 0025 cost= 18.258080609\n",
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"Optimization Finished!\n",
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"Accuracy: 0.9048\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(activation, 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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