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MNIST GAN Tutorial
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import tensorflow as tf | |
import tensorflow.contrib.slim as slim | |
import numpy as np | |
from tensorflow.examples.tutorials.mnist import input_data | |
def generator(inputs): | |
with tf.variable_scope("generator"): | |
net = slim.fully_connected(inputs, 256, scope = "fc1") | |
net = slim.fully_connected(net, 784, scope = "fake_images", activation_fn = tf.nn.sigmoid) | |
return net | |
def discriminator(inputs): | |
with tf.variable_scope("discriminator"): | |
net = slim.fully_connected(inputs, 256, scope = "fc1") | |
net = slim.fully_connected(net, 1, scope = "predictions", activation_fn = tf.nn.sigmoid) | |
return net | |
if __name__ == "__main__": | |
mnist_loader = input_data.read_data_sets('MNIST_data') | |
batch_size = 32 | |
z_dim = 100 | |
learning_rate = 0.0002 | |
num_iters = 100000 | |
random_z = tf.placeholder(shape = [batch_size, z_dim], dtype = tf.float32, name = "random_vector") | |
real_images = tf.placeholder(shape = [batch_size, 784], dtype = tf.float32, name = "real_images") | |
fake_images = generator(random_z) | |
predictions = discriminator(tf.concat([real_images, fake_images], axis = 0)) | |
real_preds = tf.slice(predictions, [0, 0], [batch_size, -1]) | |
fake_preds = tf.slice(predictions, [batch_size, 0], [batch_size, -1]) | |
gen_loss = -tf.reduce_mean(tf.log(fake_preds)) | |
dis_loss = -tf.reduce_mean(tf.log(real_preds) + tf.log(1. - fake_preds)) | |
gen_vars = slim.get_variables(scope = "generator") | |
dis_vars = slim.get_variables(scope = "discriminator") | |
optimizer = tf.train.AdamOptimizer(learning_rate) | |
gen_train_op = optimizer.minimize(gen_loss, var_list = gen_vars) | |
dis_train_op = optimizer.minimize(dis_loss, var_list = dis_vars) | |
summaries = [ | |
tf.summary.scalar("gen_loss", gen_loss), | |
tf.summary.scalar("dis_loss", dis_loss), | |
tf.summary.image("real_images", tf.reshape(real_images, [batch_size, 28, 28, 1])), | |
tf.summary.image("fake_images", tf.reshape(fake_images, [batch_size, 28, 28, 1])) | |
] | |
summary_op = tf.summary.merge(summaries) | |
summary_writer = tf.summary.FileWriter("log", graph=tf.get_default_graph()) | |
sess = tf.Session() | |
sess.run(tf.global_variables_initializer()) | |
for iter in xrange(1, num_iters + 1): | |
feed_dict = { | |
random_z: np.random.uniform(-1., 1., size=[batch_size, z_dim]), | |
real_images: mnist_loader.train.next_batch(batch_size=batch_size)[0] | |
} | |
_, _, _gen_loss, _dis_loss, summary = sess.run([gen_train_op, dis_train_op, gen_loss, dis_loss, summary_op], | |
feed_dict = feed_dict) | |
summary_writer.add_summary(summary, iter) | |
if (iter % 50) == 0: | |
print("Iteration [{:06d}/{:06d}]".format(iter, num_iters)) | |
print("\t>> Generator Loss: {}".format(_gen_loss)) | |
print("\t>> Discriminator Loss: {}".format(_dis_loss)) |
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