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► Keras 2 API documentation / Layers API / Activation layers / PReLU layer

PReLU layer

[source]

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, set shared_axes=[1, 2].
PReLU layer
PReLU class