original ) (raw )Compute the Gaussian Error Linear Unit (GELU) activation function.
tf.nn.gelu(
features, approximate=False, name=None
)
Gaussian error linear unit (GELU) computesx * 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.
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.