TensorFlow 1 version | View source on GitHub |
Wraps arbitrary expressions as a Layer
object.
Inherits From: Layer
tf.keras.layers.Lambda(
function, output_shape=None, mask=None, arguments=None, **kwargs
)
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.
Examples:
# 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)
model.add(Lambda(antirectifier))
Variables:
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.
Arguments | |
---|---|
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