Scaled Exponential Linear Unit.


SELU is equal to: scale * elu(x, alpha), where alpha and scale are pre-defined constants.


  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.
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.


The values of alpha and scale are chosen so that the mean and variance of the inputs are preserved between two consecutive layers as long as the weights are initialized correctly (see initializer_lecun_normal) and the number of inputs is “large enough” (see article for more information). Note:

  • To be used together with the initialization “lecun_normal”.

  • To be used together with the dropout variant “AlphaDropout”.

See Also

Self-Normalizing Neural Networks, initializer_lecun_normal, layer_alpha_dropout Other activation layers: layer_activation_elu(), layer_activation_leaky_relu(), layer_activation_parametric_relu(), layer_activation_relu(), layer_activation_softmax(), layer_activation_thresholded_relu(), layer_activation()