layer_activation_elu: Exponential Linear Unit.

In keras: R Interface to 'Keras'

Exponential Linear Unit.

Description

It follows: f(x) = alpha * (exp(x) - 1.0) for x < 0, f(x) = x for x >= 0.

Usage

layer_activation_elu(
  object,
  alpha = 1,
  input_shape = NULL,
  batch_input_shape = NULL,
  batch_size = NULL,
  dtype = NULL,
  name = NULL,
  trainable = NULL,
  weights = NULL
)

Arguments

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	Scale for the negative factor.
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

Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs).

Other activation layers: layer_activation(), layer_activation_leaky_relu(), layer_activation_parametric_relu(), layer_activation_relu(), layer_activation_selu(), layer_activation_softmax(), layer_activation_thresholded_relu()

keras documentation built on May 29, 2024, 3:20 a.m.