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GAN.py
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import tensorflow as tf
import numpy as np
import datetime
import matplotlib.pyplot as plt
# Read the dataset
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/")
def discriminator(images, reuse_variables=None):
with tf.variable_scope(tf.get_variable_scope(), reuse=reuse_variables) as scope:
# First convolutional and pool layers
# This finds 32 different 5 x 5 pixel features
d_w1 = tf.get_variable('d_w1', [5, 5, 1, 32], initializer=tf.truncated_normal_initializer(stddev=0.02))
d_b1 = tf.get_variable('d_b1', [32], initializer=tf.constant_initializer(0))
d1 = tf.nn.conv2d(input=images, filter=d_w1, strides=[1, 1, 1, 1], padding='SAME')
d1 = d1 + d_b1
d1 = tf.nn.relu(d1)
d1 = tf.nn.avg_pool(d1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
# Second convolutional and pool layers
# This finds 64 different 5 x 5 pixel features
d_w2 = tf.get_variable('d_w2', [5, 5, 32, 64], initializer=tf.truncated_normal_initializer(stddev=0.02))
d_b2 = tf.get_variable('d_b2', [64], initializer=tf.constant_initializer(0))
d2 = tf.nn.conv2d(input=d1, filter=d_w2, strides=[1, 1, 1, 1], padding='SAME')
d2 = d2 + d_b2
d2 = tf.nn.relu(d2)
d2 = tf.nn.avg_pool(d2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
# First fully connected layer
d_w3 = tf.get_variable('d_w3', [7 * 7 * 64, 1024], initializer=tf.truncated_normal_initializer(stddev=0.02))
d_b3 = tf.get_variable('d_b3', [1024], initializer=tf.constant_initializer(0))
d3 = tf.reshape(d2, [-1, 7 * 7 * 64])
d3 = tf.matmul(d3, d_w3)
d3 = d3 + d_b3
d3 = tf.nn.relu(d3)
# Second fully connected layer
d_w4 = tf.get_variable('d_w4', [1024, 1], initializer=tf.truncated_normal_initializer(stddev=0.02))
d_b4 = tf.get_variable('d_b4', [1], initializer=tf.constant_initializer(0))
d4 = tf.matmul(d3, d_w4) + d_b4
# d4 contains unscaled values
return d4
def generator(z, batch_size, z_dim):
# From z_dim to 56*56 dimension
g_w1 = tf.get_variable('g_w1', [z_dim, 3136], dtype=tf.float32, initializer=tf.truncated_normal_initializer(stddev=0.02))
g_b1 = tf.get_variable('g_b1', [3136], initializer=tf.truncated_normal_initializer(stddev=0.02))
g1 = tf.matmul(z, g_w1) + g_b1
g1 = tf.reshape(g1, [-1, 56, 56, 1])
g1 = tf.contrib.layers.batch_norm(g1, epsilon=1e-5, scope='bn1')
g1 = tf.nn.relu(g1)
# Generate 50 features
g_w2 = tf.get_variable('g_w2', [3, 3, 1, z_dim/2], dtype=tf.float32, initializer=tf.truncated_normal_initializer(stddev=0.02))
g_b2 = tf.get_variable('g_b2', [z_dim/2], initializer=tf.truncated_normal_initializer(stddev=0.02))
g2 = tf.nn.conv2d(g1, g_w2, strides=[1, 2, 2, 1], padding='SAME')
g2 = g2 + g_b2
g2 = tf.contrib.layers.batch_norm(g2, epsilon=1e-5, scope='bn2')
g2 = tf.nn.relu(g2)
g2 = tf.image.resize_images(g2, [56, 56])
# Generate 25 features
g_w3 = tf.get_variable('g_w3', [3, 3, z_dim/2, z_dim/4], dtype=tf.float32, initializer=tf.truncated_normal_initializer(stddev=0.02))
g_b3 = tf.get_variable('g_b3', [z_dim/4], initializer=tf.truncated_normal_initializer(stddev=0.02))
g3 = tf.nn.conv2d(g2, g_w3, strides=[1, 2, 2, 1], padding='SAME')
g3 = g3 + g_b3
g3 = tf.contrib.layers.batch_norm(g3, epsilon=1e-5, scope='bn3')
g3 = tf.nn.relu(g3)
g3 = tf.image.resize_images(g3, [56, 56])
# Final convolution with one output channel
g_w4 = tf.get_variable('g_w4', [1, 1, z_dim/4, 1], dtype=tf.float32, initializer=tf.truncated_normal_initializer(stddev=0.02))
g_b4 = tf.get_variable('g_b4', [1], initializer=tf.truncated_normal_initializer(stddev=0.02))
g4 = tf.nn.conv2d(g3, g_w4, strides=[1, 2, 2, 1], padding='SAME')
g4 = g4 + g_b4
g4 = tf.sigmoid(g4)
# Dimensions of g4: batch_size x 28 x 28 x 1
return g4
""" See the fake image we make """
# Define the plceholder and the graph
z_dimensions = 100
z_placeholder = tf.placeholder(tf.float32, [None, z_dimensions])
# For generator, one image for a batch
generated_image_output = generator(z_placeholder, 1, z_dimensions)
z_batch = np.random.normal(0, 1, [1, z_dimensions])
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
generated_image = sess.run(generated_image_output,
feed_dict={z_placeholder: z_batch})
generated_image = generated_image.reshape([28, 28])
plt.imshow(generated_image, cmap='Greys')
plt.savefig("/img/test_img.png")
""" For Training GAN """
tf.reset_default_graph()
batch_size = 50
z_placeholder = tf.placeholder(tf.float32, [None, z_dimensions], name='z_placeholder')
# z_placeholder is for feeding input noise to the generator
x_placeholder = tf.placeholder(tf.float32, shape = [None,28,28,1], name='x_placeholder')
# x_placeholder is for feeding input images to the discriminator
Gz = generator(z_placeholder, batch_size, z_dimensions)
# Gz holds the generated images
Dx = discriminator(x_placeholder)
# Dx will hold discriminator prediction probabilities
# for the real MNIST images
Dg = discriminator(Gz, reuse_variables=True)
# Dg will hold discriminator prediction probabilities for generated images
# Two Loss Functions for discriminator
d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits = Dx, labels = tf.ones_like(Dx)))
d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits = Dg, labels = tf.zeros_like(Dg)))
# Loss function for generator
g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits = Dg, labels = tf.ones_like(Dg)))
# Get the varaibles for different network
tvars = tf.trainable_variables()
d_vars = [var for var in tvars if 'd_' in var.name]
g_vars = [var for var in tvars if 'g_' in var.name]
print([v.name for v in d_vars])
print([v.name for v in g_vars])
# Train the discriminator
d_trainer_fake = tf.train.AdamOptimizer(0.0003).minimize(d_loss_fake, var_list=d_vars)
d_trainer_real = tf.train.AdamOptimizer(0.0003).minimize(d_loss_real, var_list=d_vars)
# Train the generator
g_trainer = tf.train.AdamOptimizer(0.0001).minimize(g_loss, var_list=g_vars)
# """ For setting TensorBoard """
# From this point forward, reuse variables
tf.get_variable_scope().reuse_variables()
# tf.summary.scalar('Generator_loss', g_loss)
# tf.summary.scalar('Discriminator_loss_real', d_loss_real)
# tf.summary.scalar('Discriminator_loss_fake', d_loss_fake)
# images_for_tensorboard = generator(z_placeholder, batch_size, z_dimensions)
# tf.summary.image('Generated_images', images_for_tensorboard, 5)
# merged = tf.summary.merge_all()
# logdir = "tensorboard/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S") + "/"
# writer = tf.summary.FileWriter(logdir, sess.graph)
""" Start Training Session """
saver = tf.train.Saver()
sess = tf.Session()
sess.run(tf.global_variables_initializer())
# Pre-train discriminator
for i in range(300):
z_batch = np.random.normal(0, 1, size=[batch_size, z_dimensions])
real_image_batch = mnist.train.next_batch(batch_size)[0].reshape([batch_size, 28, 28, 1])
_, __, dLossReal, dLossFake = sess.run([d_trainer_real, d_trainer_fake, d_loss_real, d_loss_fake],
{x_placeholder: real_image_batch, z_placeholder: z_batch})
if(i % 100 == 0):
print("dLossReal:", dLossReal, "dLossFake:", dLossFake)
# Train generator and discriminator together
for i in range(100000):
real_image_batch = mnist.train.next_batch(batch_size)[0].reshape([batch_size, 28, 28, 1])
z_batch = np.random.normal(0, 1, size=[batch_size, z_dimensions])
# Train discriminator on both real and fake images
_, __, dLossReal, dLossFake = sess.run([d_trainer_real, d_trainer_fake, d_loss_real, d_loss_fake],
{x_placeholder: real_image_batch, z_placeholder: z_batch})
# Train generator
z_batch = np.random.normal(0, 1, size=[batch_size, z_dimensions])
_ = sess.run(g_trainer, feed_dict={z_placeholder: z_batch})
# if i % 10 == 0:
# # Update TensorBoard with summary statistics
# z_batch = np.random.normal(0, 1, size=[batch_size, z_dimensions])
# summary = sess.run(merged, {z_placeholder: z_batch, x_placeholder: real_image_batch})
# writer.add_summary(summary, i)
if i % 1000 == 0:
# Save the model every 1000 iteration
save_path = saver.save(sess, "/tmp/model{}.ckpt".format(i))
print("Model saved in file: %s" % save_path)
if i % 100 == 0:
# Every 100 iterations, show a generated image
print("Iteration:", i, "at", datetime.datetime.now())
z_batch = np.random.normal(0, 1, size=[1, z_dimensions])
generated_images = generator(z_placeholder, 1, z_dimensions)
images = sess.run(generated_images, {z_placeholder: z_batch})
plt.imshow(images[0].reshape([28, 28]), cmap='Greys')
plt.savefig("/img/image{}.png".format(i))
# Show discriminator's estimate
im = images[0].reshape([1, 28, 28, 1])
result = discriminator(x_placeholder)
estimate = sess.run(result, {x_placeholder: im})
print("Estimate:", estimate)