def standard_train(model: Model,
output_location: str,
dataset_hparams: hparams.DatasetHparams,
training_hparams: hparams.TrainingHparams,
start_step: Step = None,
verbose: bool = True,
evaluate_every_epoch: bool = True):
"""Train using the standard callbacks according to the provided hparams."""
# If the model file for the end of training already exists in this location, do not train.
iterations_per_epoch = datasets.registry.iterations_per_epoch(
dataset_hparams)
train_end_step = Step.from_str(training_hparams.training_steps,
iterations_per_epoch)
if (models.registry.exists(output_location, train_end_step)
and get_platform().exists(paths.logger(output_location))):
return
train_loader = datasets.registry.get(dataset_hparams, train=True)
test_loader = datasets.registry.get(dataset_hparams, train=False)
callbacks = standard_callbacks.standard_callbacks(
training_hparams,
train_loader,
test_loader,
start_step=start_step,
verbose=verbose,
evaluate_every_epoch=evaluate_every_epoch)
train(training_hparams,
model,
train_loader,
output_location,
callbacks,
start_step=start_step)
def setUp(self):
super(TestStandardCallbacks, self).setUp()
# Model hparams.
self.hparams = models.registry.get_default_hparams('mnist_lenet_10_10')
self.model = models.registry.get(self.hparams.model_hparams)
# Dataset hparams.
self.hparams.dataset_hparams.subsample_fraction = 0.01
self.hparams.dataset_hparams.batch_size = 50
self.train_loader = datasets.registry.get(self.hparams.dataset_hparams)
self.test_loader = datasets.registry.get(self.hparams.dataset_hparams,
train=False)
# Training hparams.
self.hparams.training_hparams.training_steps = '3ep'
# Get the callbacks.
self.callbacks = standard_callbacks.standard_callbacks(
self.hparams.training_hparams,
self.train_loader,
self.test_loader,
eval_on_train=True,
verbose=False)