layer_activation_parametric_relu: Parametric Rectified Linear Unit.
In dfalbel/keras: R Interface to 'Keras'

It follows: f(x) = alpha * x`` for x < 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
)

`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.