torch:LayerNorm

import torch.nn as nn
import torch


# input = torch.randn(20, 5, 10, 10)
# # With Learnable Parameters
# m = nn.LayerNorm(input.size()[1:])
# # Without Learnable Parameters
# m = nn.LayerNorm(input.size()[1:], elementwise_affine=False)
# # Normalize over last two dimensions
# m = nn.LayerNorm([10, 10])
# # Normalize over last dimension of size 10
# m = nn.LayerNorm(10)
# # Activating the module
# output = m(input)


# input = torch.randn(10)
input = torch.tensor([ 0.2618,  0.2526,  0.3785,  0.5963, -0.0758, -0.9603, -0.5442,  0.2270, -1.6566,  2.0631])
print(input.size())
m = nn.LayerNorm(input.size())
# m = nn.LayerNorm(input.size(), elementwise_affine=False)
output = m(input)
print(input)
print(output)

E:新脚本主文件夹训练测试项目venv3Scriptspython.exe E:/新脚本主文件夹/训练测试项目/test_torch/LayerNorm.py
torch.Size([10])
tensor([ 0.2618,  0.2526,  0.3785,  0.5963, -0.0758, -0.9603, -0.5442,  0.2270,
        -1.6566,  2.0631])
tensor([ 0.2203,  0.2105,  0.3441,  0.5753, -0.1380, -1.0767, -0.6351,  0.1834,
        -1.8157,  2.1320], grad_fn=<NativeLayerNormBackward>)

Process finished with exit code 0