def from_config(cls, config, custom_objects=None): globs = globals() if custom_objects: globs = dict(list(globs.items()) + list(custom_objects.items())) function_type = config.pop('function_type') if function_type == 'function': # Simple lookup in custom objects function = deserialize_keras_object( config['function'], custom_objects=custom_objects, printable_module_name='function in Lambda layer') elif function_type == 'lambda': # Unsafe deserialization from bytecode function = func_load(config['function'], globs=globs) else: raise TypeError('Unknown function type:', function_type) # If arguments were numpy array, they have been saved as # list. We need to recover the ndarray if 'arguments' in config: for key in config['arguments']: if isinstance(config['arguments'][key], dict): arg_dict = config['arguments'][key] if 'type' in arg_dict and arg_dict['type'] == 'ndarray': # Overwrite the argument with its numpy translation config['arguments'][key] = np.array(arg_dict['value']) config['function'] = function return cls(**config)
def from_config(cls, config, custom_objects=None): globs = globals() if custom_objects: globs = dict(list(globs.items()) + list(custom_objects.items())) function_type = config.pop('function_type') if function_type == 'function': # Simple lookup in custom objects function = deserialize_keras_object( config['function'], custom_objects=custom_objects, printable_module_name='function in Lambda layer') elif function_type == 'lambda': # Unsafe deserialization from bytecode function = func_load(config['function'], globs=globs) else: raise TypeError('Unknown function type:', function_type) # If arguments were numpy array, they have been saved as # list. We need to recover the ndarray if 'arguments' in config: for key in config['arguments']: if isinstance(config['arguments'][key], dict): arg_dict = config['arguments'][key] if 'type' in arg_dict and arg_dict['type'] == 'ndarray': # Overwrite the argument with its numpy translation config['arguments'][key] = np.array(arg_dict['value']) config['function'] = function return cls(**config)
def from_config(cls, config, custom_objects=None): globs = globals() if custom_objects: globs = dict(list(globs.items()) + list(custom_objects.items())) function_type = config.pop('function_type') if function_type == 'function': # Simple lookup in custom objects function = deserialize_keras_object( config['function'], custom_objects=custom_objects, printable_module_name='function in Lambda layer') elif function_type == 'lambda': # Unsafe deserialization from bytecode function = func_load(config['function'], globs=globs) else: raise TypeError('Unknown function type:', function_type) config['function'] = function return cls(**config)
def from_config(cls, config, custom_objects=None): globs = globals() if custom_objects: globs = dict(list(globs.items()) + list(custom_objects.items())) function_type = config.pop('function_type') if function_type == 'function': # Simple lookup in custom objects function = deserialize_keras_object( config['function'], custom_objects=custom_objects, printable_module_name='function in Lambda layer') elif function_type == 'lambda': # Unsafe deserialization from bytecode function = func_load(config['function'], globs=globs) else: raise TypeError('Unknown function type:', function_type) config['function'] = function return cls(**config)