PReLU layer
PReLU class
tf_keras.layers.PReLU(
alpha_initializer="zeros",
alpha_regularizer=None,
alpha_constraint=None,
shared_axes=None,
**kwargs
)
Parametric Rectified Linear Unit.
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
- 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].