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.")