def test_load_yaml_file(deeprec_resource_path): data_path = os.path.join(deeprec_resource_path, "xdeepfm") yaml_file = os.path.join(data_path, "xDeepFM.yaml") if not os.path.exists(yaml_file): download_deeprec_resources( "https://recodatasets.z20.web.core.windows.net/deeprec/", data_path, "xdeepfmresources.zip", ) config = load_yaml(yaml_file) assert config is not None
def test_load_yaml_file(resource_path): data_path = os.path.join(resource_path, "..", "resources", "deeprec", "xdeepfm") yaml_file = os.path.join(data_path, "xDeepFM.yaml") if not os.path.exists(yaml_file): download_deeprec_resources( "https://recodatasets.blob.core.windows.net/deeprec/", data_path, "xdeepfmresources.zip", ) config = load_yaml(yaml_file) assert config is not None
def prepare_hparams(yaml_file=None, **kwargs): """Prepare the model hyperparameters and check that all have the correct value. Args: yaml_file (str): YAML file as configuration. Returns: obj: Hyperparameter object in TF (tf.contrib.training.HParams). """ if yaml_file is not None: config = load_yaml(yaml_file) config = flat_config(config) else: config = {} config.update(kwargs) check_nn_config(config) return create_hparams(config)