def _wrapped_model(*args, **kwargs):
"""A concrete tf.function that wraps the model's call function."""
(
args,
kwargs,
) = model._call_spec.set_arg_value("training",
False,
args,
kwargs,
inputs_in_args=True)
with base_layer_utils.call_context().enter(model,
inputs=None,
build_graph=False,
training=False,
saving=True):
outputs = model(*args, **kwargs)
# Outputs always has to be a flat dict.
output_names = model.output_names # Functional Model.
if output_names is None: # Subclassed Model.
from keras.engine import compile_utils
output_names = compile_utils.create_pseudo_output_names(outputs)
outputs = tf.nest.flatten(outputs)
return {name: output for name, output in zip(output_names, outputs)}
def _wrapped_model(*args, **kwargs):
"""A concrete tf.function that wraps the model's call function."""
kwargs['training'] = False
with base_layer_utils.call_context().enter(
model, inputs=None, build_graph=False, training=False, saving=True):
outputs = model(*args, **kwargs)
# Outputs always has to be a flat dict.
output_names = model.output_names # Functional Model.
if output_names is None: # Subclassed Model.
from keras.engine import compile_utils # pylint: disable=g-import-not-at-top
output_names = compile_utils.create_pseudo_output_names(outputs)
outputs = tf.nest.flatten(outputs)
return {name: output for name, output in zip(output_names, outputs)}
def _wrapped_model(*args):
"""A concrete tf.function that wraps the model's call function."""
# When given a single input, Keras models will call the model on the tensor
# rather than a list consisting of the single tensor.
inputs = args[0] if len(input_signature) == 1 else list(args)
with base_layer_utils.call_context().enter(
model, inputs=inputs, build_graph=False, training=False, saving=True):
outputs = model(inputs, training=False)
# Outputs always has to be a flat dict.
output_names = model.output_names # Functional Model.
if output_names is None: # Subclassed Model.
from keras.engine import compile_utils # pylint: disable=g-import-not-at-top
output_names = compile_utils.create_pseudo_output_names(outputs)
outputs = tf.nest.flatten(outputs)
return {name: output for name, output in zip(output_names, outputs)}