def from_config(cls, config, custom_objects=None): model = cls() for conf in config: layer = layer_module.deserialize(conf, custom_objects=custom_objects) model.add(layer) return model
def model_from_json(json_string, custom_objects=None): """Parses a JSON model configuration file and returns a model instance. Arguments: json_string: JSON string encoding a model configuration. custom_objects: Optional dictionary mapping names (strings) to custom classes or functions to be considered during deserialization. Returns: A Keras model instance (uncompiled). """ config = json.loads(json_string) return layer_module.deserialize(config, custom_objects=custom_objects)
def model_from_config(config, custom_objects=None): """Instantiates a Keras model from its config. Arguments: config: Configuration dictionary. custom_objects: Optional dictionary mapping names (strings) to custom classes or functions to be considered during deserialization. Returns: A Keras model instance (uncompiled). """ if isinstance(config, list): raise TypeError('`model_fom_config` expects a dictionary, not a list. ' 'Maybe you meant to use ' '`Sequential.from_config(config)`?') return layer_module.deserialize(config, custom_objects=custom_objects)
def model_from_yaml(yaml_string, custom_objects=None): """Parses a yaml model configuration file and returns a model instance. Arguments: yaml_string: YAML string encoding a model configuration. custom_objects: Optional dictionary mapping names (strings) to custom classes or functions to be considered during deserialization. Returns: A Keras model instance (uncompiled). Raises: ImportError: if yaml module is not found. """ if yaml is None: raise ImportError('Requires yaml module installed.') config = yaml.load(yaml_string) return layer_module.deserialize(config, custom_objects=custom_objects)
def from_config(cls, config): model = cls() for conf in config: layer = layer_module.deserialize(conf) model.add(layer) return model