PReLU layer
PReLU class
keras.layers.PReLU(
alpha_initializer="Zeros",
alpha_regularizer=None,
alpha_constraint=None,
shared_axes=None,
**kwargs
)
Parametric Rectified Linear Unit activation layer.
Formula:
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.
Arguments
- 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, setshared_axes=[1, 2]. - **kwargs: Base layer keyword arguments, such as
nameanddtype.