# tf.nn.selu

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Computes scaled exponential linear: `scale * alpha * (exp(features) - 1)`

#### View aliases


**Compat aliases for migration**

See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.

[`tf.compat.v1.nn.selu`](https://www.tensorflow.org/api_docs/python/tf/nn/selu)

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    tf.nn.selu(
        features: Annotated[Any, TV_Selu_T], name=None
    ) -> Annotated[Any, TV_Selu_T]

if \< 0, `scale * features` otherwise.

To be used together with
`initializer = tf.variance_scaling_initializer(factor=1.0, mode='FAN_IN')`.
For correct dropout, use `tf.contrib.nn.alpha_dropout`.

See [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)

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| ## Args ||
|---|---|
| `features` | A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`, `float64`. |
| `name` | A name for the operation (optional). |

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| ## Returns ||
|---|---|
| A `Tensor`. Has the same type as `features`. ||

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