def get_config(self): if isinstance(self.function, python_types.LambdaType): function = generic_utils.func_dump(self.function) function_type = 'lambda' else: function = self.function.__name__ function_type = 'function' if isinstance(self._output_shape, python_types.LambdaType): output_shape = generic_utils.func_dump(self._output_shape) output_shape_type = 'lambda' elif callable(self._output_shape): output_shape = self._output_shape.__name__ output_shape_type = 'function' else: output_shape = self._output_shape output_shape_type = 'raw' config = { 'function': function, 'function_type': function_type, 'output_shape': output_shape, 'output_shape_type': output_shape_type, 'arguments': self.arguments } base_config = super(Lambda, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): if isinstance(self.function, python_types.LambdaType): function = func_dump(self.function) function_type = 'lambda' else: function = self.function.__name__ function_type = 'function' if isinstance(self._output_shape, python_types.LambdaType): output_shape = func_dump(self._output_shape) output_shape_type = 'lambda' elif callable(self._output_shape): output_shape = self._output_shape.__name__ output_shape_type = 'function' else: output_shape = self._output_shape output_shape_type = 'raw' config = { 'function': function, 'function_type': function_type, 'output_shape': output_shape, 'output_shape_type': output_shape_type, 'arguments': self.arguments } base_config = super(Lambda, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): if isinstance(self.function, python_types.LambdaType): function = func_dump(self.function) function_type = 'lambda' else: function = self.function.__name__ function_type = 'function' config = { 'function': function, 'function_type': function_type, 'arguments': self.arguments } base_config = super(Lambda, self).get_config() return dict(list(base_config.items()) + list(config.items()))
def serialize_function(func): """Serializes function for Keras. (De)serializing Python functions from/to bytecode is unsafe. Therefore we return the function's type as an anonymous function ('lambda') or named function in the Python environment ('function'). In the latter case, this lets us use the Python scope to obtain the function rather than reload it from bytecode. (Note that both cases are brittle!) This serialization mimicks the implementation in `tf.keras.layers.Lambda`. Args: func: Python function to serialize. Returns: (serial, function_type): Serialized object, which is a tuple of its bytecode (if function is anonymous) or name (if function is named), and its function type. """ if isinstance(func, types.LambdaType): return generic_utils.func_dump(func), 'lambda' return func.__name__, 'function'