def predict_on_batch(model, x):
"""Returns predictions for a single batch of samples.
Arguments:
model: The model to predict with.
x: Input data. It could be:
- A Numpy array (or array-like), or a list of arrays
(in case the model has multiple inputs).
- A TensorFlow tensor, or a list of tensors
(in case the model has multiple inputs).
- A `tf.data` dataset.
Returns:
Numpy array(s) of predictions.
Raises:
ValueError: In case of mismatch between given number of inputs and
expectations of the model.
"""
# TODO(scottzhu): Standardization should happen in the data handlers,
## not on a per batch basis in the *_on_batch methods
# Validate and standardize user data.
inputs, _, _ = model._standardize_user_data(
x, extract_tensors_from_dataset=True)
# If `model._distribution_strategy` is True, then we are in a replica context
# at this point.
inputs = training_utils.cast_to_model_input_dtypes(inputs, model)
if isinstance(inputs, collections.Sequence):
# Unwrap lists with only one input, as we do when training on batch
if len(inputs) == 1:
inputs = inputs[0]
with backend.eager_learning_phase_scope(0):
return model(inputs) # pylint: disable=not-callable
def test_on_batch(model,
inputs,
targets,
sample_weights=None,
output_loss_metrics=None):
"""Calculates the loss 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': 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.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 = nest.flatten(total_loss)
return {
'total_loss': total_loss,
'output_losses': output_losses,
'metrics': metrics_results
}
def test_on_batch(model,
inputs,
targets,
sample_weights=None,
output_loss_metrics=None):
"""Calculates the loss 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, loss and metrics associated with each output.
"""
if isinstance(inputs, collections.Sequence):
inputs = training_utils.cast_to_model_input_dtypes(inputs, model)
if targets:
targets = training_utils.cast_if_floating_dtype(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
]
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 = nest.flatten(total_loss)
results = total_loss + output_losses + metrics_results
return results
