tf.nn.silu
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Computes the SiLU or Swish activation function: x * sigmoid(x).
tf.nn.silu(
features
)
The SiLU activation function was introduced in "Gaussian Error Linear Units
(GELUs)" Hendrycks et al. 2016 and
"Sigmoid-Weighted Linear Units for Neural Network Function Approximation in
Reinforcement Learning"
Elfwing et al. 2017 and was independently
discovered (and called swish) in "Searching for Activation Functions"
Ramachandran et al. 2017
Args |
|---|
features
|
A Tensor representing preactivation values.
|
Returns |
|---|
|
The activation value.
|
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Last updated 2022-11-04 UTC.
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View source on GitHub