def train_on_batch(model,
inputs,
targets,
sample_weights=None,
output_loss_metrics=None):
"""Calculates the loss and gradient updates for one input batch.
Arguments:
model: Model whose loss has to be calculated.
inputs: Input batch data.
targets: Target batch data.
sample_weights: Sample weight batch data.
output_loss_metrics: List of metrics that are used to aggregated output
loss values.
Returns:
total loss and the loss associated with each output.
"""
if isinstance(inputs, collections.Sequence):
if len(inputs) and tensor_util.is_tensor(inputs[0]):
inputs = training_utils.cast_if_floating_to_model_input_dtypes(
inputs, model)
if targets:
targets = training_utils.cast_if_floating_dtype(targets)
else:
inputs = training_utils.cast_if_floating_to_model_input_dtypes(
[ops.convert_to_tensor(val) for val in inputs], model)
if targets:
targets = training_utils.cast_if_floating_dtype(
[ops.convert_to_tensor(val) for val in targets])
if sample_weights:
sample_weights = [
training_utils.cast_if_floating_dtype(ops.convert_to_tensor(val))
if val is not None else None for val in sample_weights
]
outs, total_loss, output_losses, masks = (_process_single_batch(
model,
inputs,
targets,
sample_weights=sample_weights,
training=True,
output_loss_metrics=output_loss_metrics))
if not isinstance(outs, list):
outs = [outs]
metrics_results = _eager_metrics_fn(model,
outs,
targets,
sample_weights=sample_weights,
masks=masks)
total_loss = nest.flatten(total_loss)
results = total_loss + output_losses + metrics_results
return [_non_none_constant_value(v) for v in results]
def train_on_batch(model,
inputs,
targets,
sample_weights=None,
output_loss_metrics=None):
"""Calculates the loss and gradient updates for one input batch.
Arguments:
model: Model whose loss has to be calculated.
inputs: Input batch data.
targets: Target batch data.
sample_weights: Sample weight batch data.
output_loss_metrics: List of metrics that are used to aggregated output
loss values.
Returns:
total loss and the loss associated with each output.
"""
if isinstance(inputs, collections.Sequence):
if len(inputs) and tensor_util.is_tensor(inputs[0]):
inputs = training_utils.cast_if_floating_to_model_input_dtypes(inputs,
model)
if targets:
targets = training_utils.cast_if_floating_dtype(targets)
else:
inputs = training_utils.cast_if_floating_to_model_input_dtypes(
[ops.convert_to_tensor(val) for val in inputs], model)
if targets:
targets = training_utils.cast_if_floating_dtype(
[ops.convert_to_tensor(val) for val in targets])
if sample_weights:
sample_weights = [
training_utils.cast_if_floating_dtype(ops.convert_to_tensor(val))
if val is not None else None for val in sample_weights
]
outs, total_loss, output_losses, masks = (
_process_single_batch(
model,
inputs,
targets,
sample_weights=sample_weights,
training=True,
output_loss_metrics=output_loss_metrics))
if not isinstance(outs, list):
outs = [outs]
metrics_results = _eager_metrics_fn(
model, outs, targets, sample_weights=sample_weights, masks=masks)
total_loss = nest.flatten(total_loss)
results = total_loss + output_losses + metrics_results
return [_non_none_constant_value(v) for v in results]
