autoencoder.py

#!/usr/bin/env python

from __future__ import absolute_import, division, print_function

import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
# Import MINST data
from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets("./mnist/", one_hot=True)

# Parameters
learning_rate = 0.01
training_epochs = 20
batch_size = 256
display_step = 1
examples_to_show = 10

n_hidden_1 = 256  # 1st layer num features
n_hidden_2 = 128  # 2nd layer num features
n_input = 784  # MNIST data input (img shape: 28*28)

X = tf.placeholder("float", [None, n_input])

weights = {
    'encoder_h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),
    'encoder_h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),
    'decoder_h1': tf.Variable(tf.random_normal([n_hidden_2, n_hidden_1])),
    'decoder_h2': tf.Variable(tf.random_normal([n_hidden_1, n_input])),
}
biases = {
    'encoder_b1': tf.Variable(tf.random_normal([n_hidden_1])),
    'encoder_b2': tf.Variable(tf.random_normal([n_hidden_2])),
    'decoder_b1': tf.Variable(tf.random_normal([n_hidden_1])),
    'decoder_b2': tf.Variable(tf.random_normal([n_input])),
}


# Building the encoder
def encoder(x):
    layer_1 = tf.nn.sigmoid(tf.add(
        tf.matmul(x, weights['encoder_h1']), biases['encoder_b1']))
    layer_2 = tf.nn.sigmoid(tf.add(
        tf.matmul(layer_1, weights['encoder_h2']), biases['encoder_b2']))
    return layer_2


# Building the decoder
def decoder(x):
    layer_1 = tf.nn.sigmoid(tf.add(
        tf.matmul(x, weights['decoder_h1']), biases['decoder_b1']))
    layer_2 = tf.nn.sigmoid(tf.add(
        tf.matmul(layer_1, weights['decoder_h2']), biases['decoder_b2']))
    return layer_2

# Construct model
encoder_op = encoder(X)
decoder_op = decoder(encoder_op)
y_pred = decoder_op
# Targets (Labels) are the input data.
y_true = X

loss = tf.reduce_mean(tf.pow(y_true - y_pred, 2))
train_op = tf.train.RMSPropOptimizer(learning_rate).minimize(loss)

init = tf.initialize_all_variables()

# Launch the graph
with tf.Session() as sess:
    sess.run(init)
    total_batch = int(mnist.train.num_examples / batch_size)
    # Training cycle
    for epoch in range(training_epochs):
        # Loop over all batches
        for i in range(total_batch):
            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
            _, c = sess.run([train_op, loss], feed_dict={X: batch_xs})
        # Display logs per epoch step
        if epoch % display_step == 0:
            print("Epoch:", '%04d' % (epoch + 1), "cost=", "{:.9f}".format(c))

    print("Optimization Finished!")

    # Applying encode and decode over test set
    encode_decode = sess.run(
        y_pred, feed_dict={X: mnist.test.images[:examples_to_show]})
    # Compare original images with their reconstructions
    f, a = plt.subplots(2, 10, figsize=(10, 2))
    for i in range(examples_to_show):
        a[0][i].imshow(np.reshape(mnist.test.images[i], (28, 28)))
        a[1][i].imshow(np.reshape(encode_decode[i], (28, 28)))
    f.show()
    plt.draw()
    plt.waitforbuttonpress()