def _prepare_feed_values(model, inputs, targets, sample_weights, mode): """Prepare feed values to the model execution function. Arguments: model: Model to prepare feed values for. inputs: List or dict of model inputs. targets: Optional list of model targets. sample_weights: Optional list of sample weight arrays. mode: One of 'train'/'test'/'predict'. Returns: Feed values for the model in the given mode. """ if model._distribution_strategy: return training_distributed._prepare_feed_values(model, inputs, targets, sample_weights, mode) inputs = training_utils.ModelInputs(inputs).as_list() targets = targets or [] sample_weights = sample_weights or [] ins = inputs + targets + sample_weights if mode == 'train' and not isinstance(K.symbolic_learning_phase(), int): ins += [True] return ins
def _prepare_feed_values(model, inputs, targets, sample_weights, mode): """Prepare feed values to the model execution function. Arguments: model: Model to prepare feed values for. inputs: List or dict of model inputs. targets: Optional list of model targets. sample_weights: Optional list of sample weight arrays. mode: One of 'train'/'test'/'predict'. Returns: Feed values for the model in the given mode. """ if model._distribution_strategy: return training_distributed._prepare_feed_values( model, inputs, targets, sample_weights, mode) inputs = training_utils.ModelInputs(inputs).as_list() targets = targets or [] sample_weights = sample_weights or [] ins = inputs + targets + sample_weights if mode == 'train' and not isinstance(K.symbolic_learning_phase(), int): ins += [True] return ins
def get_distributed_inputs(): return training_distributed._prepare_feed_values( model, inputs, targets, sample_weights, mode)
def get_distributed_inputs(): return training_distributed._prepare_feed_values( model, inputs, targets, sample_weights, mode)