tf.keras.activations.selu

Scaled Exponential Linear Unit (SELU).

tf.keras.activations.selu(
    x
)

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

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

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 tf.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 tf.keras.initializers.LecunNormal initializer) and the number of input units is "large enough" (see reference paper for more information).

Example Usage:

num_classes = 10  # 10-class problem
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(64, kernel_initializer='lecun_normal',
                                activation='selu'))
model.add(tf.keras.layers.Dense(32, kernel_initializer='lecun_normal',
                                activation='selu'))
model.add(tf.keras.layers.Dense(16, kernel_initializer='lecun_normal',
                                activation='selu'))
model.add(tf.keras.layers.Dense(num_classes, activation='softmax'))

Notes:

• To be used together with the tf.keras.initializers.LecunNormal initializer.
• To be used together with the dropout variant tf.keras.layers.AlphaDropout (not regular dropout).

References:

• Klambauer et al., 2017