tf.keras.layers.PReLU

Parametric Rectified Linear Unit.

Inherits From: Layer, Module

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.

Args
alpha_initializer	Initializer function for the weights.
alpha_regularizer	Regularizer for the weights.
alpha_constraint	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].