Exponential Linear Unit.

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

= 0`.

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

Model or layer object
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_leaky_relu, layer_activation_parametric_relu, layer_activation_relu, layer_activation_softmax, layer_activation_thresholded_relu, layer_activation