R/layersactivations.R
layer_activation_parametric_relu.Rd
It follows: f(x) = alpha * x`` for
x < 0,
f(x) = xfor
x >= 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_size  Fixed batch size for layer 
dtype  The data type expected by the input, as a string ( 
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. 
Delving Deep into Rectifiers: Surpassing HumanLevel 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()