Compute the Gaussian Error Linear Unit (GELU) activation function.
tf.nn.gelu(
features, approximate=False, name=None
)
Gaussian error linear unit (GELU) computes
x * P(X <= x)
, where P(X) ~ N(0, 1)
.
The (GELU) nonlinearity weights inputs by their value, rather than gates
inputs by their sign as in ReLU.
For example:
x = tf.constant([-3.0, -1.0, 0.0, 1.0, 3.0], dtype=tf.float32)
y = tf.nn.gelu(x)
y.numpy()
array([-0.00404951, -0.15865529, 0. , 0.8413447 , 2.9959507 ],
dtype=float32)
y = tf.nn.gelu(x, approximate=True)
y.numpy()
array([-0.00363752, -0.15880796, 0. , 0.841192 , 2.9963627 ],
dtype=float32)
Args |
features
|
A float Tensor representing preactivation values.
|
approximate
|
An optional bool . Defaults to False . Whether to enable
approximation.
|
name
|
A name for the operation (optional).
|
Returns |
A Tensor with the same type as features .
|
Raises |
ValueError
|
if features is not a floating point Tensor .
|