gan-script-fast.py

"""
This is a straightforward Python implementation of a generative adversarial network.
The code is derived from the O'Reilly interactive tutorial on GANs
(https://www.oreilly.com/learning/generative-adversarial-networks-for-beginners).

The tutorial's code trades efficiency for clarity in explaining how GANs function;
this script refactors a few things to improve performance, especially on GPU machines.
In particular, it uses a TensorFlow operation to generate random z values and pass them
to the generator; this way, more computations are contained entirely within the
TensorFlow graph.

A version of this model with explanatory notes is also available on GitHub
at https://github.com/jonbruner/generative-adversarial-networks.

This script requires TensorFlow and its dependencies in order to run. Please see
the readme for guidance on installing TensorFlow.

This script won't print summary statistics in the terminal during training;
track progress and see sample images in TensorBoard.
"""

import tensorflow as tf
import datetime

# Load MNIST data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/")

# Define the discriminator network
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

# Define the generator network
def generator(batch_size, z_dim):
    z = tf.random_normal([batch_size, z_dim], mean=0, stddev=1, name='z')
    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='g_b1')
    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='g_b2')
    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='g_b3')
    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

z_dimensions = 100
batch_size = 50

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(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

# Define losses
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)))
g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits = Dg, labels = tf.ones_like(Dg)))

# Define variable lists
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]

# Define the optimizers
# 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)

# From this point forward, reuse variables
tf.get_variable_scope().reuse_variables()

sess = tf.Session()

# Send summary statistics to TensorBoard
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(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)

sess.run(tf.global_variables_initializer())

# Pre-train discriminator
for i in range(300):
    real_image_batch = mnist.train.next_batch(batch_size)[0].reshape([batch_size, 28, 28, 1])
    _, __ = sess.run([d_trainer_real, d_trainer_fake],
                                           {x_placeholder: real_image_batch})

# 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])

    # Train discriminator on both real and fake images
    _, __ = sess.run([d_trainer_real, d_trainer_fake],
                                           {x_placeholder: real_image_batch})

    # Train generator
    _ = sess.run(g_trainer)

    if i % 10 == 0:
        # Update TensorBoard with summary statistics
        summary = sess.run(merged, {x_placeholder: real_image_batch})
        writer.add_summary(summary, i)

# Optionally, uncomment the following lines to update the checkpoint files attached to the tutorial.
# saver = tf.train.Saver()
# saver.save(sess, 'pretrained-model/pretrained_gan.ckpt')