layer_activation_softmax {keras} | R Documentation |
It follows: f(x) = alpha * (exp(x) - 1.0)
for x < 0
, f(x) = x
for 'x
= 0'.
layer_activation_softmax(object, axis = -1, input_shape = NULL, batch_input_shape = NULL, batch_size = NULL, dtype = NULL, name = NULL, trainable = NULL, weights = NULL)
object |
Model or layer object |
axis |
Integer, axis along which the softmax normalization is applied. |
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_size |
Fixed batch size for layer |
dtype |
The data type expected by the input, as a string ( |
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. |
Other activation layers: layer_activation_elu
,
layer_activation_leaky_relu
,
layer_activation_parametric_relu
,
layer_activation_relu
,
layer_activation_selu
,
layer_activation_thresholded_relu
,
layer_activation