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mindspore.ops.GLU

class mindspore.ops.GLU(axis=- 1)

Computes GLU (Gated Linear Unit activation function) of the input tensor.

$$GLU(a,b)=a\otimes \sigma (b)$$

where $a$ is the first half of the x Tensor after x is split and $b$ is the second half.

Here $\sigma$ is the sigmoid function, and $\otimes$ is the Hadamard product. See Language Modeling with Gated Convluational Networks .

Parameters

axis (int, optional) – Axis to split the input x. The value range is [-r, r) where r is the number of dimensions of x. Default: -1 , the last dimension in x.

Inputs:

• x (Tensor) - Tensor to be calculated. Dtype is floating point and the shape is $({\ast }_1,N,{\ast }_2)$ where * means, any number of additional dimensions. $N$ is required to be an even number, where $N$ is the size of x on the dimension selected by axis.

Outputs:

Tensor, the same dtype as x, with the shape $({\ast }_1,M,{\ast }_2)$ where $M=N/2$.

Raises

• TypeError – If x is not a Tensor or axis is not an int.

• IndexError – If the value of axis is out of the range of [-r, r), where r is the number of dimensions of x.

• RuntimeError – If dtype of x is not supported.

• RuntimeError – If the length of x in the dimension selected by axis is not even.

Supported Platforms:

Ascend CPU

Examples

>>> from mindspore import ops, Tensor
>>> from mindspore import dtype as mstype
>>> import numpy as np
>>> axis = 0
>>> x = Tensor(np.array([0.3220, 0.9545, 0.7879, 0.0975, 0.3698,
...                            0.5135, 0.5740, 0.3435, 0.1895, 0.8764,
...                            0.4980, 0.9673, 0.9879, 0.6988, 0.9022,
...                            0.9304, 0.1558, 0.0153, 0.1559, 0.9852]).reshape([2, 2, 5]), mstype.float32)
>>> glu = ops.GLU(axis=axis)
>>> y = glu(x)
>>> print(y)
[[[0.20028052 0.6916126  0.57412136 0.06512236 0.26307625]
  [0.3682598  0.3093122  0.17306386 0.10212085 0.63814086]]]