tf.keras.layers.Lambda

Wraps arbitrary expressions as a Layer object.

Inherits From: Layer, 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	Time series forecasting Data augmentation Graph regularization for document classification using natural graphs Using TensorFlow Recommenders with TFX Parametrized Quantum Circuits for 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.

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.

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

@classmethod
from_config(
    config, custom_objects=None, safe_mode=None
)

Creates a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

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

Returns
A layer instance.

`symbolic_call`