def _model_loss(model, inputs, targets, sample_weights=None, training=False):
"""Calculates the loss for a given model.
Arguments:
model: The model on which metrics are being calculated.
inputs: List of input arrays.
targets: List of target arrays.
sample_weights: Optional list of sample weight arrays.
training: Whether the model should be run in inference or training mode.
Returns:
Returns the model output, total loss and loss value calculated using the
specified loss function. The total loss includes regularization losses and
applies masking and sample weighting to the loss value.
"""
total_loss = 0
if len(inputs) == 1:
if model._expects_training_arg:
outs = model.call(inputs[0], training=training)
else:
outs = model.call(inputs[0])
else:
if model._expects_training_arg:
outs = model.call(inputs, training=training)
else:
outs = model.call(inputs)
if not isinstance(outs, list):
outs = [outs]
if not isinstance(targets, list):
targets = [targets]
loss_metrics = []
with backend.name_scope('loss'):
for i, loss_fn in enumerate(model.loss_functions):
if sample_weights:
weights = sample_weights[i]
else:
weights = None
# TODO(fchollet): support masking; in practice `_keras_mask` is never
# set in this context currently.
mask = outs[i]._keras_mask
weighted_masked_fn = training_utils.weighted_masked_objective(loss_fn)
with backend.name_scope(model.output_names[i] + '_loss'):
output_loss = weighted_masked_fn(
targets[i], outs[i], weights, mask=mask)
# If the number of outputs is 1 then we don't append the loss metric
# associated with each model output. When there are multiple outputs
# associated with a model, each output's loss is calculated and returned
# as part of the loss_metrics.
if len(model.outputs) > 1:
loss_metrics.append(backend.mean(output_loss))
loss_weight = model.loss_weights_list[i]
if total_loss is None:
total_loss = loss_weight * output_loss
else:
total_loss += loss_weight * output_loss
total_loss = backend.mean(total_loss)
# Add regularization losses
custom_losses = []
for layer in model.layers:
if layer.losses:
custom_losses += layer.losses
if custom_losses:
total_loss += sum(custom_losses)
return outs, total_loss, loss_metrics
def _model_loss(model, inputs, targets, sample_weights=None, training=False):
"""Calculates the loss for a given model.
Arguments:
model: The model on which metrics are being calculated.
inputs: List of input arrays.
targets: List of target arrays.
sample_weights: Optional list of sample weight arrays.
training: Whether the model should be run in inference or training mode.
Returns:
Returns the model output, total loss and loss value calculated using the
specified loss function. The total loss includes regularization losses and
applies masking and sample weighting to the loss value.
"""
total_loss = 0
if len(inputs) == 1:
if model._expects_training_arg:
outs = model.call(inputs[0], training=training)
else:
outs = model.call(inputs[0])
else:
if model._expects_training_arg:
outs = model.call(inputs, training=training)
else:
outs = model.call(inputs)
if not isinstance(outs, list):
outs = [outs]
if not isinstance(targets, list):
targets = [targets]
loss_metrics = []
with backend.name_scope('loss'):
for i, loss_fn in enumerate(model.loss_functions):
if sample_weights:
weights = sample_weights[i]
else:
weights = None
# TODO(fchollet): support masking; in practice `_keras_mask` is never
# set in this context currently.
mask = outs[i]._keras_mask
weighted_masked_fn = training_utils.weighted_masked_objective(
loss_fn)
with backend.name_scope(model.output_names[i] + '_loss'):
output_loss = weighted_masked_fn(targets[i],
outs[i],
weights,
mask=mask)
# If the number of outputs is 1 then we don't append the loss metric
# associated with each model output. When there are multiple outputs
# associated with a model, each output's loss is calculated and returned
# as part of the loss_metrics.
if len(model.outputs) > 1:
loss_metrics.append(backend.mean(output_loss))
loss_weight = model.loss_weights_list[i]
if total_loss is None:
total_loss = loss_weight * output_loss
else:
total_loss += loss_weight * output_loss
total_loss = backend.mean(total_loss)
# Add regularization losses
custom_losses = []
for layer in model.layers:
if layer.losses:
custom_losses += layer.losses
if custom_losses:
total_loss += sum(custom_losses)
return outs, total_loss, loss_metrics
