def test_on_batch(model,
inputs,
targets,
sample_weights=None,
output_loss_metrics=None):
"""Calculates the loss for one input batch.
Args:
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:
Dict with three items:
'total_loss': single tensor for overall loss,
'output_losses': list of tensors for loss corresponding to each of the
model output. Could be a empty list when model has only one output.
'metrics': list of tensors for metric specified.
"""
inputs = training_utils_v1.cast_to_model_input_dtypes(inputs, model)
with backend.eager_learning_phase_scope(0):
outs, total_loss, output_losses, masks = _model_loss(
model,
inputs,
targets,
sample_weights=sample_weights,
training=False,
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 = tf.nest.flatten(total_loss)
return {
"total_loss": total_loss,
"output_losses": output_losses,
"metrics": metrics_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:
Dict with three items:
'total_loss': list with a single tensor for overall loss,
'output_losses': list of tensors for loss corresponding to each of the
model output. Could be a empty list when model has only one output.
'metrics': list of tensors for metric specified.
"""
inputs = training_utils_v1.cast_to_model_input_dtypes(inputs, model)
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 = tf.nest.flatten(total_loss)
return {
'total_loss': total_loss,
'output_losses': output_losses,
'metrics': metrics_results
}
