'''TensorFlow implementation of http://arxiv.org/pdf/1511.06434.pdf'''
from __future__ import absolute_import, division, print_function
import math
import numpy as np
from tensorflow.contrib import layers
from tensorflow.contrib import losses
from tensorflow.contrib.framework import arg_scope
import tensorflow as tf
from utils import discriminator, decoder
from generator import Generator
def concat_elu(inputs):
return tf.nn.elu(tf.concat(3, [-inputs, inputs]))
class GAN(Generator):
def __init__(self, hidden_size, batch_size, learning_rate):
self.input_tensor = tf.placeholder(tf.float32, [None, 28 * 28])
with arg_scope([layers.conv2d, layers.conv2d_transpose],
activation_fn=concat_elu,
normalizer_fn=layers.batch_norm,
normalizer_params={'scale': True}):
with tf.variable_scope("model"):
D1 = discriminator(self.input_tensor) # positive examples
D_params_num = len(tf.trainable_variables())
G = decoder(tf.random_normal([batch_size, hidden_size]))
self.sampled_tensor = G
with tf.variable_scope("model", reuse=True):
D2 = discriminator(G) # generated examples
D_loss = self.__get_discrinator_loss(D1, D2)
G_loss = self.__get_generator_loss(D2)
params = tf.trainable_variables()
D_params = params[:D_params_num]
G_params = params[D_params_num:]
# train_discrimator = optimizer.minimize(loss=D_loss, var_list=D_params)
# train_generator = optimizer.minimize(loss=G_loss, var_list=G_params)
global_step = tf.contrib.framework.get_or_create_global_step()
self.train_discrimator = layers.optimize_loss(
D_loss, global_step, learning_rate / 10, 'Adam', variables=D_params, update_ops=[])
self.train_generator = layers.optimize_loss(
G_loss, global_step, learning_rate, 'Adam', variables=G_params, update_ops=[])
self.sess = tf.Session()
self.sess.run(tf.global_variables_initializer())
def __get_discrinator_loss(self, D1, D2):
'''Loss for the discriminator network
Args:
D1: logits computed with a discriminator networks from real images
D2: logits computed with a discriminator networks from generated images
Returns:
Cross entropy loss, positive samples have implicit labels 1, negative 0s
'''
return (losses.sigmoid_cross_entropy(D1, tf.ones(tf.shape(D1))) +
losses.sigmoid_cross_entropy(D2, tf.zeros(tf.shape(D1))))
def __get_generator_loss(self, D2):
'''Loss for the genetor. Maximize probability of generating images that
discrimator cannot differentiate.
Returns:
see the paper
'''
return losses.sigmoid_cross_entropy(D2, tf.ones(tf.shape(D2)))
def update_params(self, inputs):
d_loss_value = self.sess.run(self.train_discrimator, {
self.input_tensor: inputs})
g_loss_value = self.sess.run(self.train_generator)
return g_loss_value
