mindspore.nn.GELU

class mindspore.nn.GELU(approximate=True)[source]

Gaussian error linear unit activation function.

Applies GELU function to each element of the input. The input is a Tensor with any valid shape.

GELU is defined as:

\[GELU(x_i) = x_i*P(X < x_i),\]

where \(P\) is the cumulative distribution function of standard Gaussian distribution and \(x_i\) is the element of the input.

The picture about GELU looks like this GELU.

Parameters

approximate (bool) –

Whether to enable approximation. Default: True.

If approximate is True, The gaussian error linear activation is:

\(0.5 * x * (1 + tanh(sqrt(2 / pi) * (x + 0.044715 * x^3)))\)

else, it is:

\(x * P(X <= x) = 0.5 * x * (1 + erf(x / sqrt(2)))\), where P(X) ~ N(0, 1).

Inputs:

x (Tensor) - The input of GELU with data type of float16 or float32. The shape is \((N,*)\) where \(*\) means, any number of additional dimensions.

Outputs:

Tensor, with the same type and shape as the x.

Raises: TypeError – If dtype of x is neither float16 nor float32.

Supported Platforms:: Ascend GPU CPU

Examples

>>> x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]]), mindspore.float32)
>>> gelu = nn.GELU()
>>> output = gelu(x)
>>> print(output)
[[-1.5880802e-01  3.9999299e+00 -3.1077917e-21]
 [ 1.9545976e+00 -2.2918017e-07  9.0000000e+00]]
>>> gelu = nn.GELU(approximate=False)
>>> # CPU not support "approximate=False", using "approximate=True" instead
>>> output = gelu(x)
>>> print(output)
[[-1.5865526e-01  3.9998732e+00 -0.0000000e+00]
 [ 1.9544997e+00 -1.4901161e-06  9.0000000e+00]]