def create_main_loop():
model, bn_model, bn_updates = create_models()
ali, = bn_model.top_bricks
discriminator_loss, generator_loss = bn_model.outputs
step_rule = Adam(learning_rate=LEARNING_RATE, beta1=BETA1)
algorithm = ali_algorithm(discriminator_loss,
ali.discriminator_parameters, step_rule,
generator_loss, ali.generator_parameters,
step_rule)
algorithm.add_updates(bn_updates)
streams = create_gaussian_mixture_data_streams(
batch_size=BATCH_SIZE,
monitoring_batch_size=MONITORING_BATCH_SIZE,
means=MEANS,
variances=VARIANCES,
priors=PRIORS)
main_loop_stream, train_monitor_stream, valid_monitor_stream = streams
bn_monitored_variables = ([
v for v in bn_model.auxiliary_variables if 'norm' not in v.name
] + bn_model.outputs)
monitored_variables = (
[v
for v in model.auxiliary_variables if 'norm' not in v.name] +
model.outputs)
extensions = [
Timing(),
FinishAfter(after_n_epochs=NUM_EPOCHS),
DataStreamMonitoring(bn_monitored_variables,
train_monitor_stream,
prefix="train",
updates=bn_updates),
DataStreamMonitoring(monitored_variables,
valid_monitor_stream,
prefix="valid"),
Checkpoint(os.path.join(self._work_dir, "main_loop.tar"),
after_epoch=True,
after_training=True,
use_cpickle=True),
ProgressBar(),
Printing(),
#ModelLogger(folder=self._work_dir, after_epoch=True),
GraphLogger(num_modes=1,
num_samples=2500,
dimension=2,
r=0,
std=1,
folder=self._work_dir,
after_epoch=True,
after_training=True),
MetricLogger(means=MEANS,
variances=VARIANCES,
folder=self._work_dir,
after_epoch=True)
]
main_loop = MainLoop(model=bn_model,
data_stream=main_loop_stream,
algorithm=algorithm,
extensions=extensions)
return main_loop
def create_main_loop(save_path):
model, bn_model, bn_updates = create_models()
gan, = bn_model.top_bricks
discriminator_loss, generator_loss = bn_model.outputs
step_rule = Adam(learning_rate=LEARNING_RATE, beta1=BETA1)
algorithm = ali_algorithm(discriminator_loss, gan.discriminator_parameters,
step_rule, generator_loss,
gan.generator_parameters, step_rule)
algorithm.add_updates(bn_updates)
streams = create_gaussian_mixture_data_streams(
batch_size=BATCH_SIZE, monitoring_batch_size=MONITORING_BATCH_SIZE,
means=MEANS, variances=VARIANCES, priors=PRIORS)
main_loop_stream, train_monitor_stream, valid_monitor_stream = streams
bn_monitored_variables = (
[v for v in bn_model.auxiliary_variables if 'norm' not in v.name] +
bn_model.outputs)
monitored_variables = (
[v for v in model.auxiliary_variables if 'norm' not in v.name] +
model.outputs)
extensions = [
Timing(),
FinishAfter(after_n_epochs=NUM_EPOCHS),
DataStreamMonitoring(
bn_monitored_variables, train_monitor_stream, prefix="train",
updates=bn_updates),
DataStreamMonitoring(
monitored_variables, valid_monitor_stream, prefix="valid"),
Checkpoint(save_path, after_epoch=True, after_training=True,
use_cpickle=True),
ProgressBar(),
Printing(),
]
main_loop = MainLoop(model=bn_model, data_stream=main_loop_stream,
algorithm=algorithm, extensions=extensions)
return main_loop
def get_data(main_loop, n_points=1000):
means = main_loop.data_stream.dataset.means
variances = main_loop.data_stream.dataset.variances
priors = main_loop.data_stream.dataset.priors
_, _, stream = create_gaussian_mixture_data_streams(n_points, n_points,
sources=('features',
'label'),
means=means,
variances=variances,
priors=priors)
originals, labels = next(stream.get_epoch_iterator())
return {'originals': originals,
'labels': labels}