def _predict_input_fn(params): dataset = table_dataset_test_utils.create_random_dataset( num_examples=params["batch_size"], batch_size=params["batch_size"], repeat=True, generator_kwargs=generator_kwargs) return dataset.take(2)
def _input_fn(params): return table_dataset_test_utils.create_random_dataset( num_examples=params['batch_size'] * 2, batch_size=params['batch_size'], repeat=False, generator_kwargs=self._generator_kwargs( add_aggregation_function_id=do_model_aggregation, add_classification_labels=do_model_classification, ))
def _input_fn(params): return table_dataset_test_utils.create_random_dataset( num_examples=params['batch_size'] * 2, batch_size=params['batch_size'], repeat=False, generator_kwargs=self._generator_kwargs())
def _input_fn(params): return table_dataset_test_utils.create_random_dataset( num_examples=params["batch_size"], batch_size=params["batch_size"], repeat=False, generator_kwargs=generator_kwargs)