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\venv3\Scripts\python.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
