def _process_training_inputs(model,
x,
y,
batch_size=None,
epochs=1,
sample_weights=None,
class_weights=None,
steps_per_epoch=None,
validation_split=0.,
validation_data=None,
validation_steps=None,
shuffle=True,
distribution_strategy=None,
max_queue_size=10,
workers=1,
use_multiprocessing=False):
"""Process the data input for fit() with respect to validation_split."""
if validation_split and 0. < validation_split < 1. and validation_data:
raise ValueError('validation_data and validation_split cannot be used '
'at same time.')
adapter_cls = data_adapter.select_data_adapter(x, y)
# Handle validation_split, we want to split the data and get the training
# section before we give it to data adapter.
if validation_split and 0. < validation_split < 1.:
if adapter_cls not in _ADAPTER_FOR_VALIDATION_SPLIT:
raise ValueError(
'`validation_split` argument is not supported when '
'data adapter is {}. Received: x={}, validation_split={}'.format(
adapter_cls, x, validation_split))
# Retrieve the training section from x and y, and then construct dataset
# from it.
x, y, sample_weights = model._standardize_user_data(
x,
y,
sample_weight=sample_weights,
class_weight=class_weights,
batch_size=batch_size,
check_steps=False,
steps=steps_per_epoch)
(x, y, sample_weights,
val_x, val_y,
val_sample_weights) = training_utils.split_training_and_validation_data(
x, y, sample_weights, validation_split)
sample_weight_modes = [
e.sample_weight_mode for e in model._training_endpoints
]
train_adapter = adapter_cls(
x,
y,
batch_size=batch_size,
epochs=epochs,
sample_weights=sample_weights,
sample_weight_modes=sample_weight_modes,
shuffle=shuffle,
distribution_strategy=distribution_strategy)
val_adapter = adapter_cls(
val_x,
val_y,
sample_weights=val_sample_weights,
sample_weight_modes=sample_weight_modes,
batch_size=batch_size,
distribution_strategy=distribution_strategy)
else:
train_adapter = _process_inputs(
model,
ModeKeys.TRAIN,
x,
y,
sample_weights=sample_weights,
batch_size=batch_size,
epochs=epochs,
class_weights=class_weights,
shuffle=shuffle,
steps=steps_per_epoch,
distribution_strategy=distribution_strategy,
max_queue_size=max_queue_size,
workers=workers,
use_multiprocessing=use_multiprocessing)
val_adapter = None
if validation_data:
(val_x, val_y,
val_sample_weights) = training_utils.unpack_validation_data(
validation_data, raise_if_ambiguous=False)
# For eval data, we use a representative batch size of the
# training data if batch_size was unknown.
# This is useful for generator/sequence training data input with numpy
# validation data input.
if not batch_size:
batch_size = train_adapter.representative_batch_size()
val_adapter = _process_inputs(
model,
ModeKeys.TEST,
val_x,
val_y,
sample_weights=val_sample_weights,
batch_size=batch_size,
class_weights=class_weights,
steps=validation_steps,
distribution_strategy=distribution_strategy)
elif validation_steps:
raise ValueError('`validation_steps` should not be specified if '
'`validation_data` is None.')
return train_adapter, val_adapter
def fit(self,
model,
x=None,
y=None,
batch_size=None,
epochs=1,
verbose=1,
callbacks=None,
validation_split=0.,
validation_data=None,
shuffle=True,
class_weight=None,
sample_weight=None,
initial_epoch=0,
steps_per_epoch=None,
validation_steps=None,
validation_freq=1,
**kwargs):
"""Fit loop for Distribution Strategies."""
dist_utils.validate_callbacks(input_callbacks=callbacks,
optimizer=model.optimizer)
dist_utils.validate_inputs(x, y)
batch_size, steps_per_epoch = dist_utils.process_batch_and_step_size(
model._distribution_strategy, x, batch_size, steps_per_epoch,
ModeKeys.TRAIN)
batch_size = model._validate_or_infer_batch_size(
batch_size, steps_per_epoch, x)
dataset = model._distribution_standardize_user_data(
x,
y,
sample_weight=sample_weight,
class_weight=class_weight,
batch_size=batch_size,
validation_split=validation_split,
shuffle=shuffle,
epochs=epochs)
if not dist_utils.is_distributing_by_cloning(model):
with model._distribution_strategy.scope():
(dataset, _, _) = model._standardize_user_data(
dataset,
sample_weight=sample_weight,
class_weight=class_weight,
batch_size=batch_size,
validation_split=validation_split,
shuffle=shuffle)
val_dataset = None
if validation_data:
val_x, val_y, val_sample_weights = training_utils.unpack_validation_data(
validation_data)
dist_utils.validate_inputs(val_x, val_y)
_, validation_steps = dist_utils.process_batch_and_step_size(
model._distribution_strategy, val_x, batch_size,
validation_steps, ModeKeys.TEST)
val_dataset = model._distribution_standardize_user_data(
val_x,
val_y,
sample_weight=val_sample_weights,
class_weight=None,
batch_size=batch_size,
validation_split=validation_split,
shuffle=shuffle,
allow_partial_batch=True)
elif validation_split:
raise ValueError('validation_split argument is not supported with '
'distribution strategies.')
if dist_utils.is_tpu_strategy(model._distribution_strategy):
steps_per_epoch = training_utils.infer_steps_for_dataset(
dataset, steps_per_epoch, epochs, steps_name='steps_per_epoch')
if steps_per_epoch is None:
raise ValueError(
'Number of steps could not be inferred from the data, '
'please pass the steps_per_epoch argument.')
if not context.executing_eagerly():
# Run TPU training in a custom loop in graph mode.
return experimental_tpu_fit_loop(
model,
dataset,
epochs=epochs,
verbose=verbose,
callbacks=callbacks,
val_dataset=val_dataset,
initial_epoch=initial_epoch,
steps_per_epoch=steps_per_epoch,
validation_steps=validation_steps,
validation_freq=validation_freq)
return training_arrays.fit_loop(model,
dataset,
batch_size=batch_size,
epochs=epochs,
verbose=verbose,
callbacks=callbacks,
val_inputs=val_dataset,
shuffle=shuffle,
initial_epoch=initial_epoch,
steps_per_epoch=steps_per_epoch,
validation_steps=validation_steps,
validation_freq=validation_freq,
steps_name='steps_per_epoch')