tf.keras.ops.gelu
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Gaussian Error Linear Unit (GELU) activation function.
tf.keras.ops.gelu(
x, approximate=True
)
If approximate is True, it is defined as:
f(x) = 0.5 * x * (1 + tanh(sqrt(2 / pi) * (x + 0.044715 * x^3)))
Or if approximate is False, it is defined as:
f(x) = x * P(X <= x) = 0.5 * x * (1 + erf(x / sqrt(2))),
where P(X) ~ N(0, 1).
Args |
|---|
x
|
Input tensor.
|
approximate
|
Approximate version of GELU activation. Defaults to True.
|
Returns |
|---|
A tensor with the same shape as x.
|
Example:
x = np.array([-1., 0., 1.])
x_gelu = keras.ops.gelu(x)
print(x_gelu)
array([-0.15865525, 0., 0.84134475], shape=(3,), dtype=float64)
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Last updated 2024-06-07 UTC.
[null,null,["Last updated 2024-06-07 UTC."],[],[],null,["# tf.keras.ops.gelu\n\n\u003cbr /\u003e\n\n|--------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/keras-team/keras/tree/v3.3.3/keras/src/ops/nn.py#L470-L498) |\n\nGaussian Error Linear Unit (GELU) activation function.\n\n#### View aliases\n\n\n**Main aliases**\n\n[`tf.keras.ops.nn.gelu`](https://www.tensorflow.org/api_docs/python/tf/keras/ops/gelu)\n\n\u003cbr /\u003e\n\n tf.keras.ops.gelu(\n x, approximate=True\n )\n\nIf `approximate` is `True`, it is defined as:\n`f(x) = 0.5 * x * (1 + tanh(sqrt(2 / pi) * (x + 0.044715 * x^3)))`\n\nOr if `approximate` is `False`, it is defined as:\n`f(x) = x * P(X \u003c= x) = 0.5 * x * (1 + erf(x / sqrt(2)))`,\nwhere `P(X) ~ N(0, 1)`.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|---------------|-------------------------------------------------------------|\n| `x` | Input tensor. |\n| `approximate` | Approximate version of GELU activation. Defaults to `True`. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| A tensor with the same shape as `x`. ||\n\n\u003cbr /\u003e\n\n#### Example:\n\n x = np.array([-1., 0., 1.])\n x_gelu = keras.ops.gelu(x)\n print(x_gelu)\n array([-0.15865525, 0., 0.84134475], shape=(3,), dtype=float64)"]]
View source on GitHub