Exponential Linear Unit.
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Compat aliases for migration
See
Migration guide for
more details.
`tf.compat.v1.keras.activations.elu`
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'))
Args |
x
|
Input tensor.
|
alpha
|
A scalar, slope of negative section. alpha controls the value
to which an ELU saturates for negative net inputs.
|
Returns |
The exponential linear unit (ELU) activation function: x if x > 0
and alpha * (exp(x) - 1) if x < 0 .
|