Lambda layer


Lambda class

keras.layers.Lambda(function, output_shape=None, mask=None, arguments=None, **kwargs)

Wraps arbitrary expressions as a Layer object.

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: 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.


# add a x -> x^2 layer
model.add(Lambda(lambda x: x ** 2))


  • 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.