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.
Example
# add a x -> x^2 layer
model.add(Lambda(lambda x: x ** 2))
Arguments
- 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_shapeor, the input isNoneand the sample dimension is alsoNone: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_masklayer 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.