def _build_GAN(self):
self.initializer = tf.contrib.layers.xavier_initializer
with tf.variable_scope('gan'):
# discriminator input from real data
self._X = self.inputs(self._hps.batch_size, self.s_size)
# tf.placeholder(dtype=tf.float32, name='X',
# shape=[None, self._hps.dis_input_size])
# noise vector (generator input)
self._preZ = tf.random_uniform(
[self._hps.batch_size * 3, self._hps.gen_input_size],
minval=-1.0,
maxval=1.0)
self._Z = tf.random_uniform(
[self._hps.batch_size, self._hps.gen_input_size],
minval=-1.0,
maxval=1.0)
self._Z_sample = tf.random_uniform([20, self._hps.gen_input_size],
minval=-1.0,
maxval=1.0)
self.discriminator_inner = Discriminator(
self._hps, scope='discriminator_inner')
self.discriminator = Discriminator(self._hps)
self.generator = Generator(self._hps)
# Generator
self.G_presample = self.generator.generate(self._preZ)
self.G_sample_test = self.generator.generate(self._Z_sample)
# Inner Discriminator
D_in_fake_presample, D_in_logit_fake_presample = self.discriminator_inner.discriminate(
self.G_presample)
D_in_real, D_in_logit_real = self.discriminator_inner.discriminate(
self._X)
values, indices = tf.nn.top_k(D_in_fake_presample[:, 0],
self._hps.batch_size)
tf.logging.info(indices)
self.G_selected_samples = tf.gather(self.G_presample, indices)
tf.logging.info(self.G_selected_samples)
D_in_fake, D_in_logit_fake = self.discriminator_inner.discriminate(
self.G_selected_samples)
# Discriminator
D_real, D_logit_real = self.discriminator.discriminate(self._X)
D_fake, D_logit_fake = self.discriminator.discriminate(
self.G_selected_samples)
with tf.variable_scope('D_loss'):
D_loss_real = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=D_logit_real, labels=tf.ones_like(D_logit_real)))
D_loss_fake = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=D_logit_fake, labels=tf.zeros_like(D_logit_fake)))
self._D_loss = D_loss_real + D_loss_fake
tf.summary.scalar('D_loss_real', D_loss_real, collections=['Dis'])
tf.summary.scalar('D_loss_fake', D_loss_fake, collections=['Dis'])
tf.summary.scalar('D_loss', self._D_loss, collections=['Dis'])
tf.summary.scalar('D_out',
tf.reduce_mean(D_logit_fake),
collections=['Dis'])
with tf.variable_scope('D_in_loss'):
D_in_loss_fake = tf.reduce_mean(
tf.losses.mean_squared_error(predictions=D_in_logit_fake,
labels=D_logit_fake))
D_in_loss_real = tf.reduce_mean(
tf.losses.mean_squared_error(predictions=D_in_logit_real,
labels=D_logit_real))
self._D_in_loss = D_in_loss_fake + D_in_loss_real
tf.summary.scalar('D_in_loss',
self._D_in_loss,
collections=['Dis_in'])
tf.summary.scalar('D_in_out',
tf.reduce_mean(D_in_logit_fake),
collections=['Dis_in'])
with tf.variable_scope('G_loss'):
self._G_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=D_in_logit_fake,
labels=tf.ones_like(D_in_logit_fake)))
tf.summary.scalar('G_loss', self._G_loss, collections=['Gen'])
with tf.variable_scope('GAN_Eval'):
tf.logging.info(self.G_sample_test.shape)
eval_fake_images = tf.image.resize_images(self.G_sample_test,
[28, 28])
eval_real_images = tf.image.resize_images(self._X[:20], [28, 28])
self.eval_score = util.mnist_score(eval_fake_images,
MNIST_CLASSIFIER_FROZEN_GRAPH)
self.frechet_distance = util.mnist_frechet_distance(
eval_real_images, eval_fake_images,
MNIST_CLASSIFIER_FROZEN_GRAPH)
tf.summary.scalar('MNIST_Score',
self.eval_score,
collections=['All'])
tf.summary.scalar('frechet_distance',
self.frechet_distance,
collections=['All'])
def _build_GAN(self):
self.initializer = tf.contrib.layers.xavier_initializer
self.discriminator = Discriminator(self._hps)
self.generator = Generator(self._hps)
with tf.variable_scope('gan'):
# discriminator input from real data
image_input = self.inputs(self._hps.batch_size, self.s_size)
tf.logging.info("image input")
tf.logging.info(image_input)
self._X = image_input #tf.contrib.layers.flatten(image_input)
# tf.placeholder(dtype=tf.float32, name='X',
# shape=[None, self._hps.dis_input_size])
# noise vector (generator input)
self._Z = tf.placeholder(dtype="float32",
name='Z',
shape=[None, self._hps.gen_input_size])
#tf.random_uniform([self._hps.batch_size, self._hps.gen_input_size], minval=-1.0, maxval=1.0)
#self._Z_sample = tf.random_uniform([20, self._hps.gen_input_size], minval=-1.0, maxval=1.0)
# Generator
self.G_sample = self.generator.generate(self._Z)
D_real, D_logit_real = self.discriminator.discriminate(self._X)
D_fake, D_logit_fake = self.discriminator.discriminate(
self.G_sample)
with tf.variable_scope('D_loss'):
D_loss_real = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=D_logit_real, labels=tf.ones_like(D_logit_real)))
D_loss_fake = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=D_logit_fake, labels=tf.zeros_like(D_logit_fake)))
self._D_loss = D_loss_real + D_loss_fake
tf.summary.scalar('D_loss_real', D_loss_real, collections=['Dis'])
tf.summary.scalar('D_loss_fake', D_loss_fake, collections=['Dis'])
tf.summary.scalar('D_loss', self._D_loss, collections=['Dis'])
with tf.variable_scope('G_loss'):
self._G_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=D_logit_fake, labels=tf.ones_like(D_logit_fake)))
tf.summary.scalar('G_loss', self._G_loss, collections=['Gen'])
with tf.variable_scope('GAN_Eval'):
MNIST_CLASSIFIER_FROZEN_GRAPH = '../../models-master/research/gan/mnist/data/classify_mnist_graph_def.pb'
tf.logging.info(self.G_sample.shape)
eval_images = tf.reshape(self.G_sample, [-1, 28, 28, 1])
tf.logging.info(eval_images.shape)
self.eval_score = util.mnist_score(eval_images,
MNIST_CLASSIFIER_FROZEN_GRAPH)
self.frechet_distance = util.mnist_frechet_distance(
tf.reshape(self._X[:20], [-1, 28, 28, 1]), eval_images,
MNIST_CLASSIFIER_FROZEN_GRAPH)
tf.summary.scalar('MNIST_Score',
self.eval_score,
collections=['All'])
tf.summary.scalar('frechet_distance',
self.frechet_distance,
collections=['All'])
discriminator_optimizer = tf.train.AdamOptimizer(0.0001, beta1=0.5)
gan_train_ops = tfgan.gan_train_ops(gan_model, improved_wgan_loss,
generator_optimizer,
discriminator_optimizer)
num_images_to_eval = 500
MNIST_CLASSIFIER_FROZEN_GRAPH = './mnist/data/classify_mnist_graph_def.pb'
# For variables to load, use the same variable scope as in the train job.
with tf.variable_scope('Generator', reuse=True):
eval_images = gan_model.generator_fn(tf.random_normal(
[num_images_to_eval, noise_dims]),
is_training=False)
# Calculate Inception score.
eval_score = util.mnist_score(eval_images, MNIST_CLASSIFIER_FROZEN_GRAPH)
# Calculate Frechet Inception distance.
with tf.device('/cpu:0'):
real_images, _, _ = data_provider.provide_data('train', num_images_to_eval,
MNIST_DATA_DIR)
frechet_distance = util.mnist_frechet_distance(real_images, eval_images,
MNIST_CLASSIFIER_FROZEN_GRAPH)
# Reshape eval images for viewing.
generated_data_to_visualize = tfgan.eval.image_reshaper(eval_images[:20, ...],
num_cols=10)
train_step_fn = tfgan.get_sequential_train_steps()
global_step = tf.train.get_or_create_global_step()
def train(is_train):
if not tf.gfile.Exists(MNIST_DATA_DIR):
tf.gfile.MakeDirs(MNIST_DATA_DIR)
#download_and_convert_mnist.run(MNIST_DATA_DIR)
tf.reset_default_graph()
# Define our input pipeline. Pin it to the CPU so that the GPU can be reserved
# for forward and backwards propogation.
batch_size = 32
with tf.device('/cpu:0'):
real_images, _, _ = data_provider.provide_data(
'train', batch_size, MNIST_DATA_DIR)
# Sanity check that we're getting images.
#check_real_digits = tfgan.eval.image_reshaper(
# real_images[:20,...], num_cols=10)
#print('visualize_digits')
#visualize_digits(check_real_digits)
#plt.show()
gan_model = tfgan.gan_model(
generator_fn,
discriminator_fn,
real_data=real_images,
generator_inputs=tf.random_normal([batch_size, noise_dims]))
improved_wgan_loss = tfgan.gan_loss(
gan_model,
# We make the loss explicit for demonstration, even though the default is
# Wasserstein loss.
generator_loss_fn=tfgan.losses.wasserstein_generator_loss,
discriminator_loss_fn=tfgan.losses.wasserstein_discriminator_loss,
gradient_penalty_weight=1.0)
# Sanity check that we can evaluate our losses.
print("Sanity check that we can evaluate our losses")
for gan_loss, name in [(improved_wgan_loss, 'improved wgan loss')]:
evaluate_tfgan_loss(gan_loss, name)
#generator_optimizer = tf.train.AdamOptimizer(0.001, beta1=0.5)
#discriminator_optimizer = tf.train.AdamOptimizer(0.0001, beta1=0.5)
generator_optimizer = tf.train.RMSPropOptimizer(0.001)
discriminator_optimizer = tf.train.RMSPropOptimizer(0.0001)
gan_train_ops = tfgan.gan_train_ops(
gan_model,
improved_wgan_loss,
generator_optimizer,
discriminator_optimizer)
# ### Evaluation
num_images_to_eval = 500
MNIST_CLASSIFIER_FROZEN_GRAPH = os.path.join(
RESEARCH_FOLDER,
'gan/mnist/data/classify_mnist_graph_def.pb')
# For variables to load, use the same variable scope as in the train job.
with tf.variable_scope('Generator', reuse=True):
eval_images = gan_model.generator_fn(
tf.random_normal([num_images_to_eval, noise_dims]),
is_training=False)
# Calculate Inception score.
eval_score = util.mnist_score(eval_images, MNIST_CLASSIFIER_FROZEN_GRAPH)
# Calculate Frechet Inception distance.
with tf.device('/cpu:0'):
real_images, _, _ = data_provider.provide_data(
'train', num_images_to_eval, MNIST_DATA_DIR)
frechet_distance = util.mnist_frechet_distance(
real_images, eval_images, MNIST_CLASSIFIER_FROZEN_GRAPH)
# Reshape eval images for viewing.
generated_data_to_visualize = tfgan.eval.image_reshaper(
eval_images[:20,...], num_cols=10)
# This code block should take about **1 minute** to run on a GPU kernel, and about **8 minutes** on CPU.
train_step_fn = tfgan.get_sequential_train_steps()
global_step = tf.train.get_or_create_global_step()
loss_values, mnist_scores, frechet_distances = [], [], []
tf.summary.scalar('dis_loss', gan_loss.discriminator_loss)
tf.summary.scalar('gen_loss', gan_loss.generator_loss)
merged = tf.summary.merge_all()
saver = tf.train.Saver()
saver_hook = tf.train.CheckpointSaverHook(
checkpoint_dir= "./models",
save_steps=1000,
saver=saver)
print("Graph trainable nodes:")
for v in tf.trainable_variables():
print (v.name)
with tf.train.SingularMonitoredSession(hooks=[saver_hook],
checkpoint_dir="./models") as sess:
start_time = time.time()
train_writer = tf.summary.FileWriter("./summary", sess.graph)
if is_train:
for i in xrange(2000):
cur_loss, _ = train_step_fn(
sess, gan_train_ops, global_step, train_step_kwargs={})
loss_values.append((i, cur_loss))
if i % 10 == 0:
merged_val = sess.run(merged)
train_writer.add_summary(merged_val, i)
print("Step:{}".format(i))
mnist_score, f_distance, digits_np = sess.run(
[eval_score, frechet_distance, generated_data_to_visualize])
mnist_scores.append((i, mnist_score))
frechet_distances.append((i, f_distance))
print('Current loss: %f' % cur_loss)
print('Current MNIST score: %f' % mnist_scores[-1][1])
print('Current Frechet distance: %f' % frechet_distances[-1][1])
visualize_training_generator(i, start_time, digits_np)
else: #generate from trained model
generated = sess.run(eval_images)
print("generated[0] shape:{}".format(generated[0].shape))
plt.imshow(np.squeeze(generated[0]), cmap='gray')
plt.show()
