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Wraps arbitrary expressions as a Layer object.

Inherits From: Layer

Used in the notebooks

Used in the tutorials

The Lambda layer exists so that arbitrary TensorFlow functions can be used when constructing Sequential and Functional API models. Lambda layers are best suited for simple operations or quick experimentation. For more advanced usecases, follow this guide for subclassing tf.keras.layers.Layer.

The main reason to subclass tf.keras.layers.Layer instead of using a Lambda layer is saving and inspecting a Model. Lambda layers are saved by serializing the Python bytecode, whereas subclassed Layers can be saved via overriding their get_config method. Overriding get_config improves the portability of Models. 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))
# add a layer that returns the concatenation
# of the positive part of the input and
# the opposite of the negative part

def antirectifier(x):
    x -= K.mean(x, axis=1, keepdims=True)
    x = K.l2_normalize(x, axis=1)
    pos = K.relu(x)
    neg = K.relu(-x)
    return K.concatenate([pos, neg], axis=1)



While it is possible to use Variables with Lambda layers, this practice is discouraged as it can easily lead to bugs. For instance, consider the following layer:

  scale = tf.Variable(1.)
  scale_layer = tf.keras.layers.Lambda(lambda x: x * scale)

Because scale_layer does not directly track the scale variable, it will not appear in scale_layer.trainable_weights and will therefore not be trained if scale_layer is used in a Model.

A better pattern is to write a subclassed Layer:

  class ScaleLayer(tf.keras.layers.Layer):
    def __init__(self):
      super(ScaleLayer, self).__init__()
      self.scale = tf.Variable(1.)

    def call(self, inputs):
      return inputs * self.scale

In general, Lambda layers can be convenient for simple stateless computation, but anything more complex should use a subclass Layer instead.

function The function to be evaluated. Takes input tensor as first argument.
output_shape Expected output shape from function. This argument can 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 what the input is.
arguments Optional dictionary of keyword arguments to be passed to the function.

Input shape: Arbitrary. Use the keyword argument input_shape (tuple of integers, does not include the samples axis) when using this layer as the first layer in a model. Output shape: Specified by output_shape argument