# tf.keras.activations.selu

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### Aliases:

• `tf.compat.v1.keras.activations.selu`
• `tf.compat.v2.keras.activations.selu`
``````tf.keras.activations.selu(x)
``````

The Scaled Exponential Linear Unit (SELU) activation function is: `scale * x` if `x > 0` and `scale * alpha * (exp(x) - 1)` if `x < 0` where `alpha` and `scale` are pre-defined constants (`alpha = 1.67326324` and `scale = 1.05070098`). The SELU activation function multiplies `scale` > 1 with the `[elu](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/activations/elu)`大牛时代配资 (Exponential Linear Unit (ELU)) to ensure a slope larger than one for positive net inputs.

The values of `alpha` and `scale` are chosen so that the mean and variance of the inputs are preserved between two consecutive layers as long as the weights are initialized correctly (see [`lecun_normal`大牛时代配资 initialization] ()) and the number of inputs is "large enough" (see references for more information). (Courtesy: Blog on Towards DataScience at )

#### Example Usage:

``````n_classes = 10 #10-class problem
model = models.Sequential()
input_shape=(28, 28, 1))))
``````

#### Arguments:

• `x`: A tensor or variable to compute the activation function for.

#### Returns:

The scaled exponential unit activation: `scale * elu(x, alpha)`.

# Note

``````- To be used together with the initialization "[lecun_normal]
(https://www.tensorflow.org/api_docs/python/tf/keras/initializers/lecun_normal)".
- To be used together with the dropout variant "[AlphaDropout]