tf.keras.activations.elu

Exponential Linear Unit.

tf.keras.activations.elu(
    x, alpha=1.0
)

The exponential linear unit (ELU) with alpha > 0 is: x if x > 0 and alpha * (exp(x) - 1) if x < 0 The ELU hyperparameter alpha controls the value to which an ELU saturates for negative net inputs. ELUs diminish the vanishing gradient effect.

ELUs have negative values which pushes the mean of the activations closer to zero. Mean activations that are closer to zero enable faster learning as they bring the gradient closer to the natural gradient. ELUs saturate to a negative value when the argument gets smaller. Saturation means a small derivative which decreases the variation and the information that is propagated to the next layer.

Example Usage:

import tensorflow as tf
model = tf.keras.Sequential()
model.add(tf.keras.layers.Conv2D(32, (3, 3), activation='elu',
         input_shape=(28, 28, 1)))
model.add(tf.keras.layers.MaxPooling2D((2, 2)))
model.add(tf.keras.layers.Conv2D(64, (3, 3), activation='elu'))
model.add(tf.keras.layers.MaxPooling2D((2, 2)))
model.add(tf.keras.layers.Conv2D(64, (3, 3), activation='elu'))

x Input tensor.
alpha A scalar, slope of negative section. alpha controls the value to which an ELU saturates for negative net inputs.

The exponential linear unit (ELU) activation function: x if x > 0 and alpha * (exp(x) - 1) if x < 0.

Reference:

Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) (Clevert et al, 2016)