autoencoder

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import matplotlib.pyplot as plt
import numpy as np

tf.set_random_seed(1)

# Hyper Parameters
BATCH_SIZE = 64
LR = 0.002         # learning rate
N_TEST_IMG = 5

mnist = input_data.read_data_sets('./mnist', one_hot=False)
test_x = mnist.test.images[:200]
test_y = mnist.test.labels[:200]

print(mnist.train.images.shape)     # (55000, 28 * 28)
print(mnist.test.labels.shape)     # (55000, 10)

tf_x = tf.placeholder(tf.float32, [None, 28*28])

# encoder
en0 = tf.layers.dense(tf_x, 256, tf.nn.tanh)
en1 = tf.layers.dense(en0, 128, tf.nn.tanh)
en2 = tf.layers.dense(en1, 64, tf.nn.tanh)
encoded = tf.layers.dense(en2, 32)

# decoder
de0 = tf.layers.dense(encoded, 64, tf.nn.tanh)
de1 = tf.layers.dense(de0, 128, tf.nn.tanh)
de2 = tf.layers.dense(de1, 256, tf.nn.tanh)
decoded = tf.layers.dense(de2, 28*28, tf.nn.sigmoid)

loss = tf.losses.mean_squared_error(labels=tf_x, predictions=decoded)
train = tf.train.AdamOptimizer(LR).minimize(loss)

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

# initialize figure
f, a = plt.subplots(2, N_TEST_IMG, figsize=(5, 2))
plt.ion()   # continuously plot

# original data (first row) for viewing
view_data = mnist.test.images[:N_TEST_IMG]
for i in range(N_TEST_IMG):
    a[0][i].imshow(np.reshape(view_data[i], (28, 28)), cmap='gray')
    a[0][i].set_xticks(())
    a[0][i].set_yticks(())

for step in range(5000):
    b_x, b_y = mnist.train.next_batch(BATCH_SIZE)
    _, encoded_, decoded_, loss_ = sess.run(
        [train, encoded, decoded, loss], {tf_x: b_x})

    if step % 100 == 0:     # plotting
        print('train loss: %.4f' % loss_)
        # plotting decoded image (second row)
        decoded_data = sess.run(decoded, {tf_x: view_data})
        for i in range(N_TEST_IMG):
            a[1][i].clear()
            a[1][i].imshow(np.reshape(decoded_data[i], (28, 28)), cmap='gray')
            a[1][i].set_xticks(())
            a[1][i].set_yticks(())
        plt.draw()
        plt.pause(0.01)
plt.ioff()

for index in range(0, 0):
    decoded_data = decoded
    decoded_data_unnormalized = tf.round(decoded_data*255)
    decoded_data_uint8 = tf.cast(decoded_data_unnormalized, tf.uint8)
    decoded_data_uint8_reshaped = tf.reshape(decoded_data_uint8, [28, 28, 1])
    aux = tf.image.encode_jpeg(decoded_data_uint8_reshaped)

    writer = tf.write_file(
        'train\\'+str(mnist.test.labels[index])+'\\'+str(index)+'.jpg', aux)

    if index % 10 == 0:
        print("Image number: {}".format(index))

    sess.run(writer, {tf_x: [mnist.train.images[index]]})

for index in range(0, 500):
    decoded_data = decoded
    decoded_data_unnormalized = tf.round(decoded_data*255)
    decoded_data_uint8 = tf.cast(decoded_data_unnormalized, tf.uint8)
    decoded_data_uint8_reshaped = tf.reshape(decoded_data_uint8, [28, 28, 1])
    aux = tf.image.encode_jpeg(decoded_data_uint8_reshaped)

    writer = tf.write_file(
        'test\\'+str(mnist.test.labels[index])+'\\'+str(index)+'.jpg', aux)
    if index % 10 == 0:
        print("Image number: {}".format(index))

    sess.run(writer, {tf_x: [mnist.test.images[index]]})