logistic_regression.py

'''
A logistic regression learning algorithm example using TensorFlow library.
This example is using the MNIST database of handwritten digits
(http://yann.lecun.com/exdb/mnist/)

Author: Aymeric Damien
Project: https://github.com/aymericdamien/TensorFlow-Examples/
'''

from __future__ import print_function

import tensorflow as tf

# Import MNIST data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)

# Parameters
learning_rate = 0.01
training_epochs = 25
batch_size = 100
display_step = 1

with tf.device('/gpu:0'):
    # tf Graph Input
    x = tf.placeholder(tf.float32, [None, 784]) # mnist data image of shape 28*28=784
    y = tf.placeholder(tf.float32, [None, 10]) # 0-9 digits recognition => 10 classes

    # Set model weights
    W = tf.Variable(tf.zeros([784, 10]))
    b = tf.Variable(tf.zeros([10]))

    # Construct model
    pred = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax

    # Minimize error using cross entropy
    cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred), reduction_indices=1))
    # Gradient Descent
    optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

    # Initializing the variables
    init = tf.initialize_all_variables()

    # Launch the graph
    with tf.Session() as sess:
        sess.run(init)

        # Training cycle
        for epoch in range(training_epochs):
            avg_cost = 0.
            total_batch = int(mnist.train.num_examples/batch_size)
            # Loop over all batches
            for i in range(total_batch):
                batch_xs, batch_ys = mnist.train.next_batch(batch_size)
                # Run optimization op (backprop) and cost op (to get loss value)
                _, c = sess.run([optimizer, cost], feed_dict={x: batch_xs,
                                                              y: batch_ys})
                # Compute average loss
                avg_cost += c / total_batch
            # Display logs per epoch step
            if (epoch+1) % display_step == 0:
                print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost))

        print("Optimization Finished!")

        # Test model
        correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
        # Calculate accuracy
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
        print("Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))