def try_build_compiled_arguments(model):
if (not version_utils.is_v1_layer_or_model(model)
and model.outputs is not None):
try:
model.compiled_loss.build(model.outputs)
model.compiled_metrics.build(model.outputs, model.outputs)
except: # pylint: disable=bare-except
logging.warning(
'Compiled the loaded model, but the compiled metrics have yet to '
'be built. `model.compile_metrics` will be empty until you train '
'or evaluate the model.')
def call_and_return_conditional_losses(*args, **kwargs):
"""Returns layer (call_output, conditional losses) tuple."""
call_output = layer_call(*args, **kwargs)
if version_utils.is_v1_layer_or_model(layer):
conditional_losses = layer.get_losses_for(
_filtered_inputs([args, kwargs]))
else:
conditional_losses = [
l for l in layer.losses if not hasattr(l, '_unconditional_loss')
]
return call_output, conditional_losses
def try_build_compiled_arguments(model):
if (not version_utils.is_v1_layer_or_model(model)
and model.outputs is not None):
try:
if not model.compiled_loss.built:
model.compiled_loss.build(model.outputs)
if not model.compiled_metrics.built:
model.compiled_metrics.build(model.outputs, model.outputs)
except: # noqa: E722
logging.warning(
"Compiled the loaded model, but the compiled metrics have "
"yet to be built. `model.compile_metrics` will be empty "
"until you train or evaluate the model.")
