def __call__(self,
y_true,
y_pred,
sample_weight=None,
regularization_losses=None):
"""Computes the overall loss.
Args:
y_true: An arbitrary structure of Tensors representing the ground truth.
y_pred: An arbitrary structure of Tensors representing a Model's outputs.
sample_weight: An arbitrary structure of Tensors representing the
per-sample loss weights. If one Tensor is passed, it is used for all
losses. If multiple Tensors are passed, the structure should match
`y_pred`.
regularization_losses: Additional losses to be added to the total loss.
Returns:
Tuple of `(total_loss, per_output_loss_list)`
"""
y_true = self._conform_to_outputs(y_pred, y_true)
sample_weight = self._conform_to_outputs(y_pred, sample_weight)
if not self._built:
self.build(y_pred)
y_pred = nest.flatten(y_pred)
y_true = nest.flatten(y_true)
sample_weight = nest.flatten(sample_weight)
loss_values = [] # Used for gradient calculation.
loss_metric_values = [] # Used for loss metric calculation.
batch_dim = None
zip_args = (y_true, y_pred, sample_weight, self._losses,
self._loss_weights, self._per_output_metrics)
for y_t, y_p, sw, loss_obj, loss_weight, metric_obj in zip(*zip_args):
if y_t is None or loss_obj is None: # Ok to have no loss for an output.
continue
y_t, y_p, sw = match_dtype_and_rank(y_t, y_p, sw)
sw = apply_mask(y_p, sw, get_mask(y_p))
loss_value = loss_obj(y_t, y_p, sample_weight=sw)
loss_metric_value = loss_value
# Correct for the `Mean` loss metrics counting each replica as a batch.
if loss_obj.reduction == losses_utils.ReductionV2.SUM:
loss_metric_value *= ds_context.get_strategy(
).num_replicas_in_sync
if batch_dim is None:
if tf_utils.is_ragged(y_t):
batch_dim = y_t.nrows()
else:
batch_dim = array_ops.shape(y_t)[0]
if metric_obj is not None:
metric_obj.update_state(loss_metric_value,
sample_weight=batch_dim)
if loss_weight is not None:
loss_value *= loss_weight
loss_metric_value *= loss_weight
if (loss_obj.reduction
== losses_utils.ReductionV2.SUM_OVER_BATCH_SIZE
or loss_obj.reduction == losses_utils.ReductionV2.AUTO):
loss_value = losses_utils.scale_loss_for_distribution(
loss_value)
loss_values.append(loss_value)
loss_metric_values.append(loss_metric_value)
if regularization_losses:
regularization_losses = losses_utils.cast_losses_to_common_dtype(
regularization_losses)
reg_loss = math_ops.add_n(regularization_losses)
loss_metric_values.append(reg_loss)
loss_values.append(
losses_utils.scale_loss_for_distribution(reg_loss))
if loss_values:
loss_metric_values = losses_utils.cast_losses_to_common_dtype(
loss_metric_values)
total_loss_metric_value = math_ops.add_n(loss_metric_values)
self._loss_metric.update_state(total_loss_metric_value,
sample_weight=batch_dim)
loss_values = losses_utils.cast_losses_to_common_dtype(loss_values)
total_loss = math_ops.add_n(loss_values)
return total_loss
else:
# Ok for a model to have no compiled loss.
return array_ops.zeros(shape=())
def __call__(self,
y_true,
y_pred,
sample_weight=None,
regularization_losses=None):
"""Computes the overall loss.
Arguments:
y_true: An arbitrary structure of Tensors representing the ground truth.
y_pred: An arbitrary structure of Tensors representing a Model's outputs.
sample_weight: An arbitrary structure of Tensors representing the
per-sample loss weights. If one Tensor is passed, it is used for all
losses. If multiple Tensors are passed, the structure should match
`y_pred`.
regularization_losses: Additional losses to be added to the total loss.
Returns:
Tuple of `(total_loss, per_output_loss_list)`
"""
y_true = map_to_output_names(y_pred, self._output_names, y_true)
sample_weight = map_to_output_names(y_pred, self._output_names,
sample_weight)
if not self._built:
self._build(y_pred)
y_true = nest.flatten(y_true) if y_true is not None else []
y_pred = nest.flatten(y_pred)
# TODO(omalleyt): Remove ambiguity here.
# This is currently needed to support passing only 1 loss and 1 target
# to a Functional Model with multiple outputs. However, this is
# ambiguous, especially with subclass, and we should reconsider how we
# support this.
if len(y_true) == 1 and len(y_pred) > 1:
y_true = y_true * len(y_pred)
sample_weight = nest.flatten(sample_weight)
# Allows passing one sample-weight array for all outputs.
if len(sample_weight) == 1 and len(y_pred) > 1:
sample_weight = sample_weight * len(y_pred)
loss_values = [] # Used for gradient calculation.
loss_metric_values = [] # Used for loss metric calculation.
zip_args = (y_true, y_pred, sample_weight, self._losses, self._loss_weights,
self._per_output_metrics)
for y_t, y_p, sw, loss_obj, loss_weight, metric_obj in zip(*zip_args):
if loss_obj is None: # Ok to have no loss for an output.
continue
y_t, y_p, sw = match_dtype_and_rank(y_t, y_p, sw)
sw = apply_mask(y_p, sw)
loss_value = loss_obj(y_t, y_p, sample_weight=sw)
loss_metric_value = loss_value
# Correct for the `Mean` loss metrics counting each replica as a batch.
if loss_obj.reduction == losses_utils.ReductionV2.SUM:
loss_metric_value *= ds_context.get_strategy().num_replicas_in_sync
if metric_obj is not None:
metric_obj.update_state(loss_metric_value)
if loss_weight is not None:
loss_value *= loss_weight
loss_metric_value *= loss_weight
if (loss_obj.reduction == losses_utils.ReductionV2.SUM_OVER_BATCH_SIZE or
loss_obj.reduction == losses_utils.ReductionV2.AUTO):
loss_value = losses_utils.scale_loss_for_distribution(loss_value)
loss_values.append(loss_value)
loss_metric_values.append(loss_metric_value)
if regularization_losses:
regularization_losses = losses_utils.cast_losses_to_common_dtype(
regularization_losses)
reg_loss = math_ops.add_n(regularization_losses)
loss_metric_values.append(reg_loss)
loss_values.append(losses_utils.scale_loss_for_distribution(reg_loss))
if loss_values:
loss_metric_values = losses_utils.cast_losses_to_common_dtype(
loss_metric_values)
total_loss_metric_value = math_ops.add_n(loss_metric_values)
self._loss_metric.update_state(total_loss_metric_value)
loss_values = losses_utils.cast_losses_to_common_dtype(loss_values)
total_loss = math_ops.add_n(loss_values)
return total_loss
else:
# Ok for a model to have no compiled loss.
return array_ops.zeros(shape=())