def _update_sample_weight_mode(model, mode, inputs):
"""Updates the sample_weight_mode of a given model."""
# Add a quick return to prevent us from calling model._feed_targets that
# accesses certain model properties that may not be set in the `PREDICT` mode.
if mode == ModeKeys.PREDICT:
return
sample_weights = None
# `inputs` is the model's inputs + targets + sample_weights +
# learning phase placeholder if specified. To update the sample_weight_mode
# we need to determine if the user has passed sample weights as part of the
# input.
if not callable(inputs):
sample_weights = inputs[len(model._feed_inputs) +
len(model._feed_targets):]
has_learning_phase_pl = (
mode == ModeKeys.TRAIN
and not isinstance(K.symbolic_learning_phase(), int))
if has_learning_phase_pl:
sample_weights = sample_weights[:-1]
model._update_sample_weight_modes(sample_weights=sample_weights)
# Call the DistributionStrategy specific function to update the
# sample_weight_mode on the model.
if model._distribution_strategy:
distributed_training_utils._update_sample_weight_modes(
model, mode, sample_weights)
def _update_sample_weight_mode(model, mode, adapter):
"""Updates the sample_weight_mode of a given model."""
# Add a quick return to prevent us from calling model._feed_targets that
# accesses certain model properties that may not be set in the `PREDICT` mode.
if mode == ModeKeys.PREDICT:
return
sample_weights = None
# Get some sample inputs from the data_adapter
iterator = _create_dataset_iterator(model._distribution_strategy,
adapter.get_dataset())
inputs = create_batch_inputs(iterator, mode, model,
model._distribution_strategy)
# `inputs` is the model's inputs + targets + sample_weights +
# learning phase placeholder if specified. To update the sample_weight_mode
# we need to determine if the user has passed sample weights as part of the
# input.
if not callable(inputs):
# if not isinstance(inputs, collections.Sequence):
# inputs = (inputs,)
# Note that the batch inputs should be a tuple of 2, 3 or 4 items.
# (input, target, {sample_weights}, {learning_phase})
sample_weights_index = 0
if model._feed_inputs:
sample_weights_index += 1
if model._feed_targets:
sample_weights_index += 1
sample_weights = inputs[sample_weights_index:]
has_learning_phase_pl = (
mode == ModeKeys.TRAIN
and not isinstance(backend.symbolic_learning_phase(), int))
if has_learning_phase_pl:
sample_weights = sample_weights[:-1]
model._update_sample_weight_modes(nest.flatten(sample_weights))
# Call the DistributionStrategy specific function to update the
# sample_weight_mode on the model.
if model._distribution_strategy:
dist_utils._update_sample_weight_modes(model, mode, sample_weights)
# Force delete the iterator.
del iterator
def _update_sample_weight_mode(model, mode, dataset):
"""Updates the sample_weight_mode of a given model."""
# TODO(kaftan): This won't actually do anything right now because
## dist_utils._update_sample_weight_modes only does things when the model
## is distributed by cloning. We will need to revisit if a method here
## is needed at all, and if so how it should look.
# Add a quick return to prevent us from calling model._feed_targets that
# accesses certain model properties that may not be set in the `PREDICT` mode.
if mode == ModeKeys.PREDICT:
return
# Get some sample inputs from the data_adapter
iterator = iter(dataset)
_, _, sample_weights = training_v2_utils._prepare_feed_values(
model, iterator, mode)
# Call the DistributionStrategy specific function to update the
# sample_weight_mode on the model.
dist_utils._update_sample_weight_modes(model, mode, sample_weights)
# Force delete the iterator.
del iterator
