Parametric Rectified Linear Unit.

It follows: f(x) = alpha * x`` forx < 0,f(x) = xforx >= 0`, where alpha is a learned array with the same shape as x.

layer_activation_parametric_relu(object, alpha_initializer = "zeros",
  alpha_regularizer = NULL, alpha_constraint = NULL, shared_axes = NULL,
  input_shape = NULL, batch_input_shape = NULL, batch_size = NULL,
  dtype = NULL, name = NULL, trainable = NULL, weights = NULL)

Arguments

object

Model or layer object
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=c(1, 2).
input_shape

Input shape (list of integers, does not include the samples axis) which is required when using this layer as the first layer in a model.
batch_input_shape

Shapes, including the batch size. For instance, batch_input_shape=c(10, 32) indicates that the expected input will be batches of 10 32-dimensional vectors. batch_input_shape=list(NULL, 32) indicates batches of an arbitrary number of 32-dimensional vectors.
batch_size

Fixed batch size for layer
dtype

The data type expected by the input, as a string (float32, float64, int32...)
name

An optional name string for the layer. Should be unique in a model (do not reuse the same name twice). It will be autogenerated if it isn't provided.
trainable

Whether the layer weights will be updated during training.
weights

Initial weights for layer.

See also

Delving Deep into Rectifiers: Surpassing Human-Level Performance onImageNet Classification.

Other activation layers: layer_activation_elu, layer_activation_leaky_relu, layer_activation_relu, layer_activation_softmax, layer_activation_thresholded_relu, layer_activation