def _prepare_feed_values(model, inputs, targets, sample_weights, mode):
"""Prepare feed values to the model execution function.
Arguments:
model: Model to prepare feed values for.
inputs: List or dict of model inputs.
targets: Optional list of model targets.
sample_weights: Optional list of sample weight arrays.
mode: One of 'train'/'test'/'predict'.
Returns:
Feed values for the model in the given mode.
"""
if model._distribution_strategy:
return training_distributed._prepare_feed_values(model, inputs, targets,
sample_weights, mode)
inputs = training_utils.ModelInputs(inputs).as_list()
targets = targets or []
sample_weights = sample_weights or []
ins = inputs + targets + sample_weights
if mode == 'train' and not isinstance(K.symbolic_learning_phase(), int):
ins += [True]
return ins
def _prepare_feed_values(model, inputs, targets, sample_weights, mode):
"""Prepare feed values to the model execution function.
Arguments:
model: Model to prepare feed values for.
inputs: List or dict of model inputs.
targets: Optional list of model targets.
sample_weights: Optional list of sample weight arrays.
mode: One of 'train'/'test'/'predict'.
Returns:
Feed values for the model in the given mode.
"""
if model._distribution_strategy:
return training_distributed._prepare_feed_values(
model, inputs, targets, sample_weights, mode)
inputs = training_utils.ModelInputs(inputs).as_list()
targets = targets or []
sample_weights = sample_weights or []
ins = inputs + targets + sample_weights
if mode == 'train' and not isinstance(K.symbolic_learning_phase(), int):
ins += [True]
return ins
def get_distributed_inputs():
return training_distributed._prepare_feed_values(
model, inputs, targets, sample_weights, mode)
def get_distributed_inputs():
return training_distributed._prepare_feed_values(
model, inputs, targets, sample_weights, mode)