|---| | [ View source on GitHub](https://github.com/keras-team/keras/tree/v3.15.1/keras/src/layers/core/lambda_layer.py#L12-L241) | Wraps arbitrary expressions as a `Layer` object. Inherits From: [`Layer`](https://www.tensorflow.org/api_docs/python/tf/keras/Layer), [`Operation`](https://www.tensorflow.org/api_docs/python/tf/keras/Operation) tf.keras.layers.Lambda( function, output_shape=None, mask=None, arguments=None, **kwargs ) ### Used in the notebooks | Used in the guide | Used in the tutorials | |---|---| | - [TensorFlow basics](https://www.tensorflow.org/guide/basics) | - [Time series forecasting](https://www.tensorflow.org/tutorials/structured_data/time_series) - [Data augmentation](https://www.tensorflow.org/tutorials/images/data_augmentation) - [Graph regularization for document classification using natural graphs](https://www.tensorflow.org/neural_structured_learning/tutorials/graph_keras_mlp_cora) - [Using TensorFlow Recommenders with TFX](https://www.tensorflow.org/recommenders/examples/ranking_tfx) - [Parametrized Quantum Circuits for Reinforcement Learning](https://www.tensorflow.org/quantum/tutorials/quantum_reinforcement_learning) | The `Lambda` layer exists so that arbitrary expressions can be used as a `Layer` when constructing Sequential and Functional API models. `Lambda` layers are best suited for simple operations or quick experimentation. For more advanced use cases, prefer writing new subclasses of `Layer`. > [!WARNING] > **Warning:** `Lambda` layers have (de)serialization limitations! The main reason to subclass `Layer` instead of using a `Lambda` layer is saving and inspecting a model. `Lambda` layers are saved by serializing the Python bytecode, which is fundamentally non-portable and potentially unsafe. They should only be loaded in the same environment where they were saved. Subclassed layers can be saved in a more portable way by overriding their `get_config()` method. Models that rely on subclassed Layers are also often easier to visualize and reason about. #### Example: # add a x -> x^2 layer model.add(Lambda(lambda x: x ** 2))


| ## Args || |---|---| | `function` | The function to be evaluated. Takes input tensor as first argument. | | `output_shape` | Expected output shape from function. This argument can usually be inferred if not explicitly provided. Can be a tuple or function. If a tuple, it only specifies the first dimension onward; sample dimension is assumed either the same as the input: `output_shape = (input_shape[0], ) + output_shape` or, the input is `None` and the sample dimension is also `None`: `output_shape = (None, ) + output_shape`. If a function, it specifies the entire shape as a function of the input shape: `output_shape = f(input_shape)`. | | `mask` | Either None (indicating no masking) or a callable with the same signature as the `compute_mask` layer method, or a tensor that will be returned as output mask regardless of what the input is. | | `arguments` | Optional dictionary of keyword arguments to be passed to the function. |



| ## Attributes || |---|---| | `input` | Retrieves the input tensor(s) of a symbolic operation.
Only returns the tensor(s) corresponding to the *first time* the operation was called. | | `output` | Retrieves the output tensor(s) of a layer.
Only returns the tensor(s) corresponding to the *first time* the operation was called. |
## Methods ### `from_config` [View source](https://github.com/keras-team/keras/tree/v3.15.1/keras/src/layers/core/lambda_layer.py#L182-L241) @classmethod from_config( config, custom_objects=None, safe_mode=None ) Creates an operation from its config. This method is the reverse of `get_config`, capable of instantiating the same operation from the config dictionary. > [!NOTE] > **Note:** If you override this method, you might receive a serialized dtype config, which is a `dict`. You can deserialize it as follows: if "dtype" in config and isinstance(config["dtype"], dict): policy = dtype_policies.deserialize(config["dtype"])


| Args || |---|---| | `config` | A Python dictionary, typically the output of `get_config`. |



| Returns || |---|---| | An operation instance. ||