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April 6, 2019 15:12
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import tensorflow as tf | |
import matplotlib.pyplot as plt | |
from tensorflow.examples.tutorials.mnist import input_data | |
import numpy as np | |
tf.reset_default_graph() | |
mnist = input_data.read_data_sets('mnist/', one_hot=True) | |
# Test | |
plt.imshow(mnist.train.images[0].reshape(28, 28), cmap='gray') | |
# plt.show() | |
# Simulation | |
image1 = np.arange(0, 784).reshape(28, 28) | |
plt.imshow(image1) | |
# plt.show() | |
image2 = np.random.normal(size=784).reshape(28, 28) | |
plt.imshow(image2) | |
# plt.show() | |
ph_noise = tf.placeholder(tf.float32, [None, 100]) | |
def generator(noise, reuse=None): | |
with tf.VariableScope('generator', reuse=reuse): | |
# 100 -> 128 -> 128 -> 784 | |
hidden_layer1 = tf.nn.relu(tf.layers.dense(inputs=noise, units=128)) | |
hidden_layer2 = tf.nn.relu(tf.layers.dense(inputs=hidden_layer1, units=128)) | |
hidden_layer3 = tf.layers.dense(inputs=hidden_layer2, units=784, activation=tf.nn.tanh) | |
return hidden_layer3 | |
real_image_ph = tf.placeholder(tf.float32, [None, 784]) | |
def discriminator(X, reuse=None): | |
with tf.VariableScope('discriminator', reuse=reuse): | |
# 784 -> 128 -> 128 -> 1 | |
hidden_layer1 = tf.nn.relu(tf.layers.dense(inputs=X, units=128)) | |
hidden_layer2 = tf.nn.relu(tf.layers.dense(inputs=hidden_layer1, units=128)) | |
logits = tf.layers.dense(hidden_layer2, units=1) | |
return logits | |
logits_real_images = discriminator(real_image_ph) | |
logits_noise_images = discriminator(generator(ph_noise), reuse=True) | |
real_discriminator_error = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=logits_real_images, labels=tf.ones_like(logits_real_images) * (0.9))) | |
noise_discriminator_error = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=logits_noise_images, labels=tf.zeros_like(logits_noise_images))) | |
discriminator_error = real_discriminator_error + noise_discriminator_error | |
generator_error = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=logits_noise_images, labels=tf.ones_like(logits_noise_images))) | |
variables = tf.trainable_variables() # To see the variables just use print(variables) | |
discriminator_variables = [v for v in variables if 'discriminator' in v.name] | |
generator_variables = [v for v in variables if 'generator' in v.name] | |
discriminator_training = tf.train.AdamOptimizer(learning_rate=0.001).minimize(discriminator_error, var_list=discriminator_variables) | |
generator_training = tf.train.AdamOptimizer(learning_rate=0.001).minimize(generator_error, var_list=generator_variables) | |
batch_size = 100 | |
test_samples = [] | |
with tf.Session() as sess: | |
sess.run(tf.global_variables_initializer()) | |
for epoch in range(300): | |
batch_num = mnist.train.num_examples // batch_size | |
for i in range(batch_num): | |
batch = mnist.train.next_batch(100) | |
image_batch = batch[0].reshape((100, 784)) | |
image_batch = image_batch * 2 - 1 | |
noise_batch = np.random.uniform(-1, 1, size=(batch_size, 100)) | |
_, discriminator_cost = sess.run([discriminator_training, discriminator_error], feed_dict={real_image_ph: image_batch, ph_noise: noise_batch}) | |
_, generator_cost = sess.run([generator_training, generator_error], feed_dict={ph_noise: noise_batch}) | |
print('Epoch: ' + str(epoch + 1) + ' D error: ' + str(discriminator_cost) + ' G error: ' + str(generator_error)) | |
noise_test = np.random.uniform(-1, 1, size=(1, 100)) | |
generated_image = sess.run(generator(ph_noise, reuse=True), feed_dict={ph_noise: noise_test}) | |
test_samples.append(generated_image) | |
plt.imshow(test_samples[0].reshape(28, 28), cmap='Greys') | |
plt.show() | |
plt.imshow(test_samples[10].reshape(28, 28), cmap='Greys') | |
plt.show() | |
plt.imshow(test_samples[30].reshape(28, 28), cmap='Greys') | |
plt.show() | |
plt.imshow(test_samples[49].reshape(28, 28), cmap='Greys') | |
plt.show() | |
plt.imshow(test_samples[100].reshape(28, 28), cmap='Greys') | |
plt.show() | |
plt.imshow(test_samples[250].reshape(28, 28), cmap='Greys') | |
plt.show() | |
plt.imshow(test_samples[299].reshape(28, 28), cmap='Greys') | |
plt.show() |
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