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
• leaky_relu
  Applies the leaky rectified linear unit function.
• log_sigmoid
  Applies the log sigmoid function.
• 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. PReLu(x) = max(0,x) + \alpha * min(0,x) tensor is assumed to be of shape [batch_size, channels, …] alpha is assumed to be of shape [channels] or [1]
• quiet_softmax
  Applies the “quiet softmax” function on the input tensor along the given dimension. This function is similar to the softmax function, but it allows for “no selection”, e.g., all outputs can tend to zero.
• relu
  Applies the rectified linear unit function as described in the paper Deep Learning using Rectified Linear Units (ReLU).
• sigmoid
  Applies the sigmoid function.
• silu
  Applies the silu function
• 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
• tanh
  Applies the tanh function