def train_model(self, fname=None):
time.sleep(1)
try:
self.load(fname)
print("File load successful.")
except Exception:
print("File load failed.")
image_dataset, dataset_size = utils.create_dataset(
batch_size=batch_size)
dataset_size = dataset_size - 1
print("Dataset size is", dataset_size)
print("Total number of images is", dataset_size * batch_size)
record_batch = utils.record_steps(int(dataset_size / 2))
start_time = time.time()
print("Beginning training at", start_time)
for i in range(num_epochs):
print("Starting epoch {}/{}".format(i, num_epochs))
start = time.time()
batch_on = 0
for source in zip(image_dataset.take(int(dataset_size / 2))):
loss_identity, kl_loss = train_step(self, source, optimizer)
if batch_on % record_batch == 0:
print("Beginning batch #" + str(batch_on), 'out of',
int(dataset_size / 2), 'of size', batch_size)
self.loss_identity += [loss_identity]
self.kl_loss += [kl_loss]
batch_on += 1
duration = time.time() - start
print(int(duration / 60), "minutes &", int(duration % 60),
"seconds, for epoch", i)
if i % record_epochs == 0:
self.loss['Identity'] = self.loss_identity
self.loss['KL'] = self.kl_loss
utils.test_model(self,
source,
num=i,
test=True,
name=model_name,
details='identity')
for style in zip(image_dataset.take(1)):
utils.test_model(self,
source,
style,
num=i,
test=True,
name=model_name,
details='transfer')
break
#act = keract.get_activations(self, source)
#keract.display_activations(act)
print('\n')
image_dataset, _ = utils.create_dataset(batch_size=batch_size)
self.save(fname)
time.sleep(.5)
print('Training completed in', int((time.time() - start_time) / 60),
"minutes &", int(duration % 60), "seconds")
def train_model(self, fname=None):
time.sleep(1)
try:
self.load(fname)
print("File load successful.")
except Exception:
print("File load failed.")
model_val = AE_A(training=False)
image_dataset, dataset_size = utils.create_dataset(batch_size=batch_size)
dataset_size = int((dataset_size - 1)/2)
print("Dataset size is", dataset_size)
print("Total number of images is", dataset_size * batch_size)
record_batch = utils.record_steps(dataset_size)
start_time = time.time()
for i in range(num_epochs):
print(f'Starting epoch {i}/{num_epochs}')
start = time.time()
batch_on = 0
for source in zip(image_dataset.take(dataset_size)):
try:
loss_identity, kl_loss = self.train_step(source, optimizer)
if batch_on % record_batch == 0:
print(f'Beginning batch #{batch_on} out of {dataset_size} of size {batch_size}')
self.loss_identity += [loss_identity]
self.kl_loss += [kl_loss]
except Exception:
print(f'Batch #{batch_on} failed. Continuing with next batch.')
batch_on += 1
self.save(fname)
time.sleep(0.5)
duration = time.time() - start
utils.print_time_remaining(i, num_epochs, duration)
if i % display_mod == 0:
model_val.load(model_name, compile=False)
model_val.loss['Identity'] = self.loss_identity
model_val.loss['KL'] = self.kl_loss
utils.test_model(model_val, source, num=i, test=True, name=model_name, details='identity')
for style in zip(image_dataset.take(1)):
utils.test_model(model_val, source, style, num=i, test=True, name=model_name, details='merge')
break
#act = keract.get_activations(self, source)
#keract.display_activations(act)
print('\n')
image_dataset, _ = utils.create_dataset(batch_size=batch_size)
print('Training completed in', int((time.time() - start_time) / 60), "minutes &", int(duration % 60), "seconds")
def train_model(self, fname=None):
time.sleep(1)
try:
self.load(fname)
print("File load successful.")
except Exception:
print("File load failed.")
image_dataset, dataset_size = utils.create_dataset(
batch_size=batch_size)
dataset_size = dataset_size - 1
print("Dataset size is", dataset_size)
print("Total number of images is", dataset_size * batch_size)
record_batch = utils.record_steps(int(dataset_size))
start_time = time.time()
print("Beginning training at", start_time)
for i in range(num_epochs):
print("Starting epoch {}/{}".format(i, num_epochs))
start = time.time()
batch_on = 0
for source in zip(image_dataset.take(int(dataset_size))):
source = utils.get_random_crop(np.array(source), 32, 32)
loss_identity = train_step(self, source, optimizer)
if batch_on % record_batch == 0:
print("Beginning batch #" + str(batch_on), 'out of',
int(dataset_size), 'of size', batch_size)
self.loss_identity += [loss_identity]
batch_on += 1
if i % record_epochs == 0:
self.loss['Identity'] = self.loss_identity
utils.test_model(self,
source,
num=i,
test=True,
name=model_name,
details='identity')
'''for style in zip(image_dataset.take(1)):
style = utils.get_random_crop(style, 32, 32)
utils.test_model(self, source, style, num=i, test=True, name=model_name, details='transfer')
break'''
print('\n')
duration = time.time() - start
utils.print_time_remaining(i, num_epochs, duration)
image_dataset, _ = utils.create_dataset(batch_size=batch_size)
self.save(fname)
time.sleep(.5)
print('Training completed in', int((time.time() - start_time) / 60),
"minutes &", int(duration % 60), "seconds")