Computes scaled exponential linear: `scale * alpha * (exp(features) - 1)`
if < 0, `scale * features` otherwise.
To be used together with `initializer = tf.variance_scaling_initializer(factor=1.0, mode='FAN_IN')`. For correct dropout, use `tf.contrib.nn.alpha_dropout`.
See [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)
Constants
String | OP_NAME | The name of this op, as known by TensorFlow core engine |
Public Methods
Inherited Methods
boolean |
equals(Object arg0)
|
final Class<?> |
getClass()
|
int |
hashCode()
|
final void |
notify()
|
final void |
notifyAll()
|
String |
toString()
|
final void |
wait(long arg0, int arg1)
|
final void |
wait(long arg0)
|
final void |
wait()
|
abstract ExecutionEnvironment |
env()
Return the execution environment this op was created in.
|
abstract Operation |
Constants
public static final String OP_NAME
The name of this op, as known by TensorFlow core engine
Public Methods
public Output<T> asOutput ()
Returns the symbolic handle of the tensor.
Inputs to TensorFlow operations are outputs of another TensorFlow operation. This method is used to obtain a symbolic handle that represents the computation of the input.