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TensorFlow 1 version
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View source on GitHub
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Parametric Rectified Linear Unit.
tf.keras.layers.PReLU(
alpha_initializer='zeros', alpha_regularizer=None,
alpha_constraint=None, shared_axes=None, **kwargs
)
It follows:
f(x) = alpha * x for x < 0
f(x) = x for x >= 0
where alpha is a learned array with the same shape as x.
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:
Same shape as the input.
Arguments | |
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alpha_initializer
|
Initializer function for the weights. |
alpha_regularizer
|
Regularizer for the weights. |
alpha_constraint
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Constraint for the weights. |
shared_axes
|
The axes along which to share learnable
parameters for the activation function.
For example, if the incoming feature maps
are from a 2D convolution
with output shape (batch, height, width, channels),
and you wish to share parameters across space
so that each filter only has one set of parameters,
set shared_axes=[1, 2].
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