tf.keras.activations.selu

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Scaled Exponential Linear Unit (SELU).

tf.keras.activations.selu(
    x
)

The Scaled Exponential Linear Unit (SELU) activation function is defined as:

• scale * x if x > 0
• scale * alpha * (exp(x) - 1) if x < 0

where alpha and scale are pre-defined constants (alpha=1.67326324 and scale=1.05070098).

Basically, the SELU activation function multiplies scale (> 1) with the output of the keras.activations.elu function to ensure a slope larger than one for positive 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 keras.initializers.LecunNormal initializer) and the number of input units is "large enough" (see reference paper for more information).

Notes:

• To be used together with the keras.initializers.LecunNormal initializer.
• To be used together with the dropout variant keras.layers.AlphaDropout (rather than regular dropout).

Reference:

• Klambauer et al., 2017