class AssignmentTraining:
def __init__(self, generator_network=None,
critic_network=None,
dataset=None,
latent=None,
cost=None):
self.dataset = dataset
self.latent = latent
self.critic_network = critic_network
self.generator_network = generator_network
self.cost=cost
self.experiment_name = self.dataset.name + "_"\
+ self.cost + "_" \
+ self.latent.name+ "_" \
+ time.strftime("_%Y-%m-%d_%H-%M-%S_")
self.log_path = os.path.join(os.path.dirname(os.getcwd()), "logs" + os.sep + self.experiment_name)
self.model = Assignment_model(self.dataset, self.latent, self.generator_network, self.critic_network,self.cost)
self.global_step = tf.train.get_or_create_global_step(graph=None)
self.increase_global_step = tf.assign_add(self.global_step, 1)
self.session = Settings.create_session()
self.session.run(tf.initialize_all_variables())
def train(self, n_critic_loops=None, n_main_loops=None):
with self.session as session:
log_writer = tf.summary.FileWriter(self.log_path, session.graph)
self.logger = LoggerFacade(session, log_writer, self.dataset.shape)
global_step = session.run(self.global_step)
for main_loop in tqdm.tqdm(range(global_step, n_main_loops, 1)):
#The ideal number for assigment points is 10-12 times the size of the dataset. However it is convinient
# to increase the size with the iterations because in the beginning of the training, big number of assignments does
#not help that much
self.n_non_assigned = self.dataset.dataset_size
assignment_loops=10
while self.n_non_assigned>10:
assign_arr, latent_sample,real_idx = self.model.find_assignments_critic(session, assign_loops= assignment_loops)
self.model.train_critic(session, assign_arr)
self.n_non_assigned = len(assign_arr) - np.count_nonzero(assign_arr)
print("step 1: Number of non assigned points " + str(self.n_non_assigned) + ", Variance from perfect assignment" + str(np.var(assign_arr)))
assign_arr, latent_sample, real_idx = self.model.find_assignments_critic(session,assign_loops=1000)
latent_sample = np.vstack(tuple(latent_sample))
real_idx = np.vstack(tuple(real_idx)).flatten()
# The smaller the offset the more precisely the generator learns. However very small offset number increases the training a lot and may lead to difficulties of traiing the
# critic because it converges fast to some of the nodes.
self.model.train_generator(session, real_idx,latent_sample,offset=128)
session.run(self.increase_global_step)
# It makes images for Tensorboard
fakes = session.run(self.model.get_fake_tensor(), {self.model.latent_batch_ph: latent_sample[:18]})
reals = self.dataset.data[real_idx[:18]]
self.log_data(main_loop,n_main_loops,session)
self.logger.log_image_grid_fixed(fakes, reals, main_loop, name="real_and_assigned")
latent_points = session.run(self.model.generate_latent_batch)
fake_points = session.run(self.model.get_fake_tensor(), {self.model.latent_batch_ph: latent_points})
self.logger.log_image_grid_fixed(fake_points,fake_points,main_loop,name="same_latent_as_save")
log_writer.close()
def log_data(self, main_loop,max_loop,session):
# accumulate fake samples and dump them into a file
if max_loop-1 == main_loop:
latent_points = session.run(self.model.generate_latent_batch)
fake_points = session.run(self.model.get_fake_tensor(), {self.model.latent_batch_ph: latent_points})
n_fake_samples_to_save = 100000
while(fake_points.shape[0]<n_fake_samples_to_save):
latent_points = session.run(self.model.generate_latent_batch)
fake_points_new = session.run(self.model.get_fake_tensor(), {self.model.latent_batch_ph: latent_points})
fake_points = np.vstack((fake_points, fake_points_new))
np.save(self.log_path + "fakes_" +str(main_loop), fake_points)
class AssignmentTraining:
def __init__(self,
generator_network=None,
critic_network=None,
dataset=None,
latent=None,
cost=None):
self.dataset = dataset
self.latent = latent
self.critic_network = critic_network
self.generator_network = generator_network
self.cost = cost
self.experiment_name = self.dataset.name + "_"\
+ self.cost + "_" \
+ self.latent.name+ "_" \
+ time.strftime("_%Y-%m-%d_%H-%M-%S_")
self.log_path = os.path.join(os.path.dirname(os.getcwd()),
"logs" + os.sep + self.experiment_name)
self.model = Assignment_model(self.dataset, self.latent,
self.generator_network,
self.critic_network, self.cost)
self.global_step = tf.train.get_or_create_global_step(graph=None)
self.increase_global_step = tf.assign_add(self.global_step, 1)
self.session = Settings.create_session()
self.session.run(tf.initialize_all_variables())
def train(self, n_critic_loops=None, n_main_loops=None):
with self.session as session:
log_writer = tf.summary.FileWriter(self.log_path, session.graph)
self.logger = LoggerFacade(session, log_writer, self.dataset.shape)
global_step = session.run(self.global_step)
self.n_non_assigned = self.latent.batch_size
for main_loop in tqdm.tqdm(range(global_step, n_main_loops, 1)):
#The ideal number for assigment points is 10-12 times the size of the dataset. However it is convinient
# to increase the size with the iterations because in the beginning of the training, big number of assignments does
#not help that much
assignment_loops = int(
10 * (self.dataset.dataset_size / self.latent.batch_size) *
np.sqrt(main_loop / n_main_loops)) + 10
with tqdm.tqdm(range(n_critic_loops)) as crit_bar:
for crit_loop in crit_bar:
assign_arr, latent_sample, real_idx = self.model.find_assignments_critic(
session, assign_loops=assignment_loops)
self.model.train_critic(session, assign_arr)
self.n_non_assigned = len(
assign_arr) - np.count_nonzero(assign_arr)
crit_bar.set_description(
"step 1: Number of non assigned points " +
str(self.n_non_assigned) +
", Variance from perfect assignment" +
str(np.var(assign_arr)),
refresh=False)
latent_sample = np.vstack(tuple(latent_sample))
real_idx = np.vstack(tuple(real_idx)).flatten()
# The smaller the offset the more precisely the generator learns. However very small offset number
# increases the training a lot and may lead to difficulties of training the
# critic because it converges fast to some of the nodes.
self.model.train_generator(session,
real_idx,
latent_sample,
offset=16)
session.run(self.increase_global_step)
# It makes images for Tensorboard
if main_loop % 50 == 1:
latent_sample_noisy = latent_sample[:18] + np.random.normal(
loc=0, scale=0.3, size=(18, self.latent.shape))
latent_sample2 = np.copy(latent_sample[:18])
np.random.shuffle(latent_sample2)
int_sample = 0.8 * latent_sample[:18] + 0.2 * latent_sample2
reals = self.dataset.data[real_idx[:18]]
fakes = session.run(
self.model.get_fake_tensor(),
{self.model.latent_batch_ph: latent_sample[:18]})
fakes_noisy = session.run(
self.model.get_fake_tensor(),
{self.model.latent_batch_ph: latent_sample_noisy})
fakes_int = session.run(
self.model.get_fake_tensor(),
{self.model.latent_batch_ph: int_sample})
self.log_data(main_loop, n_main_loops, session)
self.logger.log_image_grid_fixed(fakes,
reals,
main_loop,
name="real_and_assigned")
# This is for the case where one uses multiGaussian as a way to sample the latent space. Since in
# case we have a perfect fit, if one want to see "new" generated points has to move a bit further from
# the Gaussians
#self.logger.log_image_grid_fixed(fakes, fakes_noisy, main_loop, name="Generated_and_neighbours.")
#This is to see how points between two generated points look like
#self.logger.log_image_grid_fixed(fakes, fakes_int, main_loop, name="Interpolations_between_generated")
log_writer.close()
def log_data(self, main_loop, max_loop, session):
# accumulate fake samples and dump them into a file at the end of the training
if max_loop - 1 == main_loop:
n_fake_samples_to_save = 100000
latent_points = session.run(self.model.generate_latent_batch)
fake_points = session.run(
self.model.get_fake_tensor(),
{self.model.latent_batch_ph: latent_points})
while (fake_points.shape[0] < n_fake_samples_to_save):
latent_points = session.run(self.model.generate_latent_batch)
fake_points_new = session.run(
self.model.get_fake_tensor(),
{self.model.latent_batch_ph: latent_points})
fake_points = np.vstack((fake_points, fake_points_new))
np.save(self.log_path + "fakes_" + str(main_loop), fake_points)
class AssignmentTraining:
def __init__(self, generator_network=None,
critic_network=None,
dataset=None,
latent=None,
cost=None):
self.dataset = dataset
self.latent = latent
self.critic_network = critic_network
self.generator_network = generator_network
self.cost=cost
self.experiment_name = self.dataset.name + "_" + self.cost + "_" + self.latent.name+ "_" + time.strftime("_%Y-%m-%d_%H-%M-%S_")
self.log_path = os.path.join(os.path.dirname(os.getcwd()), "logs" + os.sep + self.experiment_name)
self.model = Assignment_model(self.dataset, self.latent, self.generator_network, self.critic_network,self.cost)
self.global_step = tf.train.get_or_create_global_step(graph=None)
self.increase_global_step = tf.assign_add(self.global_step, 1)
self.session = Settings.create_session()
self.session.run(tf.initialize_all_variables())
def train(self, n_critic_loops=None, n_main_loops=None):
with self.session as session:
log_writer = tf.summary.FileWriter(self.log_path, session.graph)
self.logger = LoggerFacade(session, log_writer, self.dataset.shape)
global_step = session.run(self.global_step)
self.n_zeros = self.latent.batch_size
for main_loop in tqdm.tqdm(range(global_step, n_main_loops, 1)):
assignment_loops=int(10*(self.dataset.dataset_size/self.latent.batch_size)*np.sqrt(main_loop/n_main_loops))+10
with tqdm.tqdm(range(n_critic_loops)) as crit_bar:
for crit_loop in crit_bar:
assign_arr, latent_sample,real_idx = self.model.find_assignments_critic(session, assign_loops= assignment_loops)
self.model.train_critic(session, assign_arr)
self.n_zeros = len(assign_arr) - np.count_nonzero(assign_arr)
crit_bar.set_description(
"step 1: # zeros " + str(self.n_zeros) + " Variance" + str(np.var(assign_arr)),
refresh=False)
latent_sample = np.vstack(tuple(latent_sample))
real_idx = np.vstack(tuple(real_idx)).flatten()
self.model.train_generator(session, real_idx,latent_sample,offset=32)
session.run(self.increase_global_step)
# It makes images for Tensorboard
fakes = session.run(self.model.get_fake_tensor(), {self.model.latent_batch_ph: latent_sample[:18]})
reals = self.dataset.data[real_idx[:18]]
self.log_data(main_loop,n_main_loops,session)
self.logger.log_image_grid_fixed(fakes, reals, main_loop, name="real_and_assigned")
latent_points = session.run(self.model.generate_latent_batch)
fake_points = session.run(self.model.get_fake_tensor(), {self.model.latent_batch_ph: latent_points})
self.logger.log_image_grid_fixed(fake_points,fake_points,main_loop,name="same_latent_as_save")
log_writer.close()
def log_data(self, main_loop,max_loop,session):
# accumulate some real and fake samples
if max_loop-1 == main_loop:
latent_points = session.run(self.model.generate_latent_batch)
fake_points = session.run(self.model.get_fake_tensor(), {self.model.latent_batch_ph: latent_points})
n_fake_to_save = 100000
while(fake_points.shape[0]<n_fake_to_save):
latent_points = session.run(self.model.generate_latent_batch)
fake_points_new = session.run(self.model.get_fake_tensor(), {self.model.latent_batch_ph: latent_points})
fake_points = np.vstack((fake_points, fake_points_new))
np.save(self.log_path + "fakes_" +str(main_loop), fake_points)