|---| | [ View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.21.0/tensorflow/python/ops/nn_impl.py#L431-L484) | Computes the SiLU or Swish activation function: `x * sigmoid(beta * x)`. #### View aliases **Main aliases** [`tf.nn.swish`](https://www.tensorflow.org/api_docs/python/tf/nn/silu)
tf.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](https://arxiv.org/abs/1606.08415) and "Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning" [Elfwing et al. 2017](https://arxiv.org/abs/1702.03118) and was independently discovered (and called swish) in "Searching for Activation Functions" [Ramachandran et al. 2017](https://arxiv.org/abs/1710.05941)


| ## Args || |---|---| | `features` | A `Tensor` representing preactivation values. | | `beta` | A 'Tensor' representing value of beta hyperparameter. |



| ## Returns || |---|---| | The activation value. ||