Module activation

The activation module.

Functions

gelu

Applies the Gaussian Error Linear Units function as described in the paper Gaussian Error Linear Units (GELUs).

hard_sigmoid

Applies the hard sigmoid function element-wise.

leaky_relu

Applies the leaky rectified linear unit function element-wise.

log_sigmoid

Applies the log sigmoid function element-wise.

log_softmax

Applies the log softmax function on the input tensor along the given dimension.

mish

Applies the Mish function as described in the paper in Mish: A Self Regularized Non-Monotonic Neural Activation Function.

prelu

Applies Parametric ReLu activation function as described in the paper Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification.

quiet_softmax

Applies the “quiet softmax” function on the input tensor along the given dimension.

relu

Applies the rectified linear unit function element-wise as described in the paper Deep Learning using Rectified Linear Units (ReLU).

sigmoid

Applies the sigmoid function element-wise.

silu

Applies the SiLU function (also known as the swish function) element-wise.

softmax

Applies the softmax function on the input tensor along the given dimension.

softmin

Applies the softmin function on the input tensor along the given dimension.

softplus

Applies the SoftPlus function element-wise.

tanh

Applies the tanh function element-wise.