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
)Model or layer object
Initializer function for the weights.
Regularizer for the weights.
Constraint for the weights.
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 (list of integers, does not include the samples axis) which is required when using this layer as the first layer in a model.
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.
Fixed batch size for layer
The data type expected by the input, as a string (float32,
float64, int32...)
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.
Whether the layer weights will be updated during training.
Initial weights for layer.
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()