def __init__(self, z_dim, crop_image_size, resized_image_size, batch_size, data_dir):
celebA_dataset = celebA.read_dataset(data_dir)
self.z_dim = z_dim
self.crop_image_size = crop_image_size
self.resized_image_size = resized_image_size
self.batch_size = batch_size
filename_queue = tf.train.string_input_producer(celebA_dataset.train_images)
self.images = self._read_input_queue(filename_queue)
def __init__(self, z_dim, num_cls, crop_image_size, resized_image_size,
batch_size, data_dir):
self.num_cls = num_cls
celebA_dataset = celebA.read_dataset(data_dir)
self.z_dim = z_dim
self.crop_image_size = crop_image_size
self.resized_image_size = resized_image_size
self.batch_size = batch_size
label_dict = celebA.create_label_dict(data_dir)
imgfn_list = label_dict.keys()
label_list = [label_dict[imgfn] for imgfn in imgfn_list]
images = tf.convert_to_tensor(imgfn_list)
labels = tf.convert_to_tensor(label_list, dtype=np.int32)
input_queue = tf.train.slice_input_producer([images, labels],
shuffle=True)
self.images, self.labels = self._read_input_queue(input_queue)
def __init__(self,
z_dim,
crop_image_size,
resized_image_size,
batch_size,
data_dir,
critic_iterations=5,
root_scope_name=''):
celebA_dataset = celebA.read_dataset(data_dir)
self.root_scope_name = root_scope_name
self.summary_collections = None if not root_scope_name else [
root_scope_name
]
self.z_dim = z_dim
self.crop_image_size = crop_image_size
self.resized_image_size = resized_image_size
self.batch_size = batch_size
self.critic_iterations = critic_iterations
filename_queue = tf.train.string_input_producer(
celebA_dataset.train_images)
self.training_batch_images = self._read_input_queue(filename_queue)
def main(argv=None):
print("Setting up image reader...")
train_images, valid_images, test_images = celebA.read_dataset(
FLAGS.data_dir)
filename_queue = tf.train.string_input_producer(train_images)
images = read_input_queue(filename_queue)
train_phase = tf.placeholder(tf.bool)
z_vec = tf.placeholder(tf.float32, [FLAGS.batch_size, FLAGS.z_dim],
name="z")
print("Setting up network model...")
tf.histogram_summary("z", z_vec)
tf.image_summary("image_real", images, max_images=2)
gen_images = generator(z_vec, train_phase)
tf.image_summary("image_generated", gen_images, max_images=2)
with tf.variable_scope("discriminator") as scope:
discriminator_real_prob, logits_real, feature_real = discriminator(
images, train_phase)
utils.add_activation_summary(
tf.identity(discriminator_real_prob, name='disc_real_prob'))
scope.reuse_variables()
discriminator_fake_prob, logits_fake, feature_fake = discriminator(
gen_images, train_phase)
utils.add_activation_summary(
tf.identity(discriminator_fake_prob, name='disc_fake_prob'))
discriminator_loss_real = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits_real,
tf.ones_like(logits_real)))
discrimintator_loss_fake = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits_fake,
tf.zeros_like(logits_fake)))
discriminator_loss = discrimintator_loss_fake + discriminator_loss_real
gen_loss_1 = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits_fake,
tf.ones_like(logits_fake)))
gen_loss_2 = tf.reduce_mean(
tf.nn.l2_loss(feature_real - feature_fake)) / (IMAGE_SIZE * IMAGE_SIZE)
gen_loss = gen_loss_1 + 0.1 * gen_loss_2
tf.scalar_summary("Discriminator_loss_real", discriminator_loss_real)
tf.scalar_summary("Discrimintator_loss_fake", discrimintator_loss_fake)
tf.scalar_summary("Discriminator_loss", discriminator_loss)
tf.scalar_summary("Generator_loss", gen_loss)
train_variables = tf.trainable_variables()
generator_variables = [
v for v in train_variables if v.name.startswith("generator")
]
print(map(lambda x: x.op.name, generator_variables))
discriminator_variables = [
v for v in train_variables if v.name.startswith("discriminator")
]
print(map(lambda x: x.op.name, discriminator_variables))
generator_train_op = train(gen_loss, generator_variables)
discriminator_train_op = train(discriminator_loss, discriminator_variables)
for v in train_variables:
utils.add_to_regularization_and_summary(var=v)
sess = tf.Session()
summary_op = tf.merge_all_summaries()
saver = tf.train.Saver()
summary_writer = tf.train.SummaryWriter(FLAGS.logs_dir, sess.graph)
sess.run(tf.initialize_all_variables())
ckpt = tf.train.get_checkpoint_state(FLAGS.logs_dir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
print("Model restored...")
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess, coord)
try:
for itr in xrange(MAX_ITERATIONS):
batch_z = np.random.uniform(-1.0,
1.0,
size=[FLAGS.batch_size,
FLAGS.z_dim]).astype(np.float32)
feed_dict = {z_vec: batch_z, train_phase: True}
sess.run(discriminator_train_op, feed_dict=feed_dict)
sess.run(generator_train_op, feed_dict=feed_dict)
if itr % 10 == 0:
g_loss_val, d_loss_val, summary_str = sess.run(
[gen_loss, discriminator_loss, summary_op],
feed_dict=feed_dict)
print("Step: %d, generator loss: %g, discriminator_loss: %g" %
(itr, g_loss_val, d_loss_val))
summary_writer.add_summary(summary_str, itr)
if itr % 500 == 0:
saver.save(sess,
FLAGS.logs_dir + "model.ckpt",
global_step=itr)
except tf.errors.OutOfRangeError:
print('Done training -- epoch limit reached')
except KeyboardInterrupt:
print("Ending Training...")
finally:
coord.request_stop()
# Wait for threads to finish.
coord.join(threads)