|TensorFlow 2.0 version||View source on GitHub|
Parametric Rectified Linear Unit.
f(x) = alpha * x for x < 0,
f(x) = x for x >= 0,
alpha is a learned array with the same shape as x.
Arbitrary. Use the keyword argument
(tuple of integers, does not include the samples axis)
when using this layer as the first layer in a model.
Same shape as the input.
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
__init__( alpha_initializer='zeros', alpha_regularizer=None, alpha_constraint=None, shared_axes=None, **kwargs )