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.


  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 Description
object What to compose the new Layer instance with. Typically a Sequential model or a Tensor (e.g., as returned by layer_input()). The return value depends on object. If object is:
- missing or NULL, the Layer instance is returned.
- a Sequential model, the model with an additional layer is returned.
- a Tensor, the output tensor from layer_instance(object) is returned.
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 on ImageNet Classification. Other activation layers: layer_activation_elu(), layer_activation_leaky_relu(), layer_activation_relu(), layer_activation_selu(), layer_activation_softmax(), layer_activation_thresholded_relu(), layer_activation()