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# tf.keras.activations.gelu

Applies the Gaussian error linear unit (GELU) activation function.

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.keras.activations.gelu(x)`
`y.numpy()`
`array([-0.00404951, -0.15865529,  0.        ,  0.8413447 ,  2.9959507 ],`
`    dtype=float32)`
`y = tf.keras.activations.gelu(x, approximate=True)`
`y.numpy()`
`array([-0.00363752, -0.15880796,  0.        ,  0.841192  ,  2.9963627 ],`
`    dtype=float32)`
```

`x` Input tensor.
`approximate` A `bool`, whether to enable approximation.

The gaussian error linear activation: `0.5 * x * (1 + tanh(sqrt(2 / pi) * (x + 0.044715 * x^3)))` if `approximate` is `True` or `x * P(X <= x) = 0.5 * x * (1 + erf(x / sqrt(2)))`, where `P(X) ~ N(0, 1)`, if `approximate` is `False`.