tf.compat.v1.nn.silu | TensorFlow v2.16.1 (original) (raw)
tf.compat.v1.nn.silu
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Computes the SiLU or Swish activation function: x * sigmoid(beta * x).
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tf.compat.v1.nn.silu(
features, beta=1.0
)
beta : Hyperparameter for Swish activation function. Default value 1.0.
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. |
| beta | A 'Tensor' representing value of beta hyperparameter. |
| Returns |
|---|
| The activation value. |
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Last updated 2024-04-26 UTC.