Autoencoder.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_epoch = 20
batch_size = 100
n_hidden = 256
n_input = 28*28

X = tf.placeholder(tf.float32, [None, n_input])
W_encode = tf.Variable(tf.random_normal([n_input, n_hidden]))
b_encode = tf.Variable(tf.random_normal([n_hidden]))

encoder = tf.nn.sigmoid(tf.add(tf.matmul(X, W_encode), b_encode))

W_decode = tf.Variable(tf.random_normal([n_hidden, n_input]))
b_decode = tf.Variable(tf.random_normal([n_input]))

decoder = tf.nn.sigmoid(tf.add(tf.matmul(encoder, W_decode), b_decode))

cost = tf.reduce_mean(tf.pow(X - decoder, 2))
optimizer = tf.train.RMSPropOptimizer(learning_rate).minimize(cost)

init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)

total_batch = int(mnist.train.num_examples / batch_size)

for epoch in range(training_epoch):
    total_cost = 0
    
    for i in range(total_batch):
        batch_xs, batch_ys = mnist.train.next_batch(batch_size)
        _, cost_val = sess.run([optimizer, cost], feed_dict = {X: batch_xs})
        total_cost += cost_val
        
    print('Epoch:', '%04d' % (epoch+1), 'Avg. cost=', '{:.4f}'.format(total_cost/ total_batch))
print('최적화 완료!')


sample_size = 10
samples = sess.run(decoder, feed_dict = {X: mnist.test.images[:sample_size]})

fig, ax = plt.subplots(2,sample_size, figsize = (sample_size, 2))
for i in range(sample_size):
    ax[0][i].set_axis_off()
    ax[1][i].set_axis_off()
    ax[0][i].imshow(np.reshape(mnist.test.images[i], (28,28)))
    ax[1][i].imshow(np.reshape(samples[i],(28,28)))
plt.show()