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_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_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 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
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
