|---| | [ View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.21.0/tensorflow/python/ops/nn_ops.py#L3702-L3758) | 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`. |



| ## References || |---|---| | [Gaussian Error Linear Units (GELUs)](https://arxiv.org/abs/1606.08415). ||