SELU is equal to: scale * elu(x, alpha), where alpha and scale
are pre-defined constants.
layer_activation_selu(
object,
input_shape = NULL,
batch_input_shape = NULL,
batch_size = NULL,
dtype = NULL,
name = NULL,
trainable = NULL,
weights = NULL
)What to call the new Layer instance with. Typically a keras
Model, another Layer, or a tf.Tensor/KerasTensor. If object is
missing, the Layer instance is returned, otherwise, layer(object) is
returned.
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.
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".
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()