auto_encoder.py

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
learning_rate = 0.01
training_epochs = 10
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])),}


encoder_in = tf.nn.sigmoid(tf.add(tf.matmul(X,weights['encoder_h1']),biases['encoder_b1']))
encoder_out = tf.nn.sigmoid(tf.add(tf.matmul(encoder_in,weights['encoder_h2']),biases['encoder_b2']))

decoder_in = tf.nn.sigmoid(tf.add(tf.matmul(encoder_out,weights['decoder_h1']),biases['decoder_b1']))

decoder_out = tf.nn.sigmoid(tf.add(tf.matmul(decoder_in,weights['decoder_h2']),biases['decoder_b2']))


y_pred = decoder_out
y_true = X
cost = tf.reduce_mean(tf.pow(y_true - y_pred, 2))
optimizer = tf.train.RMSPropOptimizer(learning_rate).minimize(cost)
init = tf.initialize_all_variables()
with tf.Session() as sess:
	sess.run(init)
	total_batch = int(mnist.train.num_examples/batch_size)
	for epoch in range(training_epochs):
		for i in range(total_batch):
			batch_xs, batch_ys =mnist.train.next_batch(batch_size)
			_, c = sess.run([optimizer, cost],feed_dict={X: batch_xs})
		if epoch % display_step == 0:
			print("Epoch:", '%04d' % (epoch+1),"cost=", "{:.9f}".format(c))
	print("Optimization Finished!")
	encode_decode = sess.run(y_pred, feed_dict={X: mnist.test.images[:examples_to_show]})
	f, a = plt.subplots(2, 10, figsize=(50, 50))
	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.show()