本文主要是介绍torch.mean()的使用方法，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
对一个三维数组的每一维度进行操作
1，dim=0
a = torch.Tensor([0, 1, 2, 3, 4, 5,6,7]).view(2, 2, 2)
print(a)
mean = torch.mean(a, 0)
print(mean, mean.shape)输出结果：
tensor([[[0., 1.],
[2., 3.]],
[[4., 5.],
[6., 7.]]])
tensor([[2., 3.],
[4., 5.]]) torch.Size([2, 2])
2，dim=1
a = torch.Tensor([0, 1, 2, 3, 4, 5,6,7]).view(2, 2, 2)
print(a)
mean = torch.mean(a, 1)
print(mean, mean.shape)输出结果
tensor(
[[[0., 1.],
[2., 3.]],
[[4., 5.],
[6., 7.]]])
tensor(
[[1., 2.],
[5., 6.]]) torch.Size([2, 2])
3，dim=2
a = torch.Tensor([0, 1, 2, 3, 4, 5,6,7]).view(2, 2, 2)
print(a)
mean = torch.mean(a, 2)
print(mean, mean.shape)输出结果
tensor(
[[[0., 1.],
[2., 3.]],
[[4., 5.],
[6., 7.]]])
tensor(
[[0.5000, 2.5000],
[4.5000, 6.5000]]) torch.Size([2, 2])
补充，如果在函数中添加了True,表示要和原来数的维度一致，不够的用维度1来添加，如下
a = torch.Tensor([0, 1, 2, 3, 4, 5,6,7]).view(2, 2, 2)
print(a)
mean = torch.mean(a, 2, True)
print(mean, mean.shape)
tensor([[[0., 1.],[2., 3.]],[[4., 5.],[6., 7.]]])
tensor([[[0.5000],[2.5000]],[[4.5000],[6.5000]]]) torch.Size([2, 2, 1])补充多维度变化
a = torch.Tensor([0, 1, 2, 3, 4, 5,6,7,8,9,10,11,12,13,14,15]).view(2, 2, 2,2)
print(a)
mean = torch.mean(a, 0, True)
print(mean, mean.shape)
tensor([[[[ 0., 1.],[ 2., 3.]],[[ 4., 5.],[ 6., 7.]]],[[[ 8., 9.],[10., 11.]],[[12., 13.],[14., 15.]]]])
tensor([[[[ 4., 5.],[ 6., 7.]],[[ 8., 9.],[10., 11.]]]]) torch.Size([1, 2, 2, 2])
a = torch.Tensor([0, 1, 2, 3, 4, 5,6,7,8,9,10,11,12,13,14,15]).view(2, 2, 2,2)
print(a)
mean = torch.mean(a, 1, True)
print(mean, mean.shape)
tensor([[[[ 0., 1.],[ 2., 3.]],[[ 4., 5.],[ 6., 7.]]],[[[ 8., 9.],[10., 11.]],[[12., 13.],[14., 15.]]]])
tensor([[[[ 2., 3.],[ 4., 5.]]],[[[10., 11.],[12., 13.]]]]) torch.Size([2, 1, 2, 2])a = torch.Tensor([0, 1, 2, 3, 4, 5,6,7,8,9,10,11,12,13,14,15]).view(2, 2, 2,2)
print(a)
mean = torch.mean(a, 2, True)
print(mean, mean.shape)tensor([[[[ 0., 1.],[ 2., 3.]],[[ 4., 5.],[ 6., 7.]]],[[[ 8., 9.],[10., 11.]],[[12., 13.],[14., 15.]]]])
tensor([[[[ 1., 2.]],[[ 5., 6.]]],[[[ 9., 10.]],[[13., 14.]]]]) torch.Size([2, 2, 1, 2])
a = torch.Tensor([0, 1, 2, 3, 4, 5,6,7,8,9,10,11,12,13,14,15]).view(2, 2, 2,2)
print(a)
mean = torch.mean(a, 3, True)
print(mean, mean.shape)
tensor([[[[ 0., 1.],[ 2., 3.]],[[ 4., 5.],[ 6., 7.]]],[[[ 8., 9.],[10., 11.]],[[12., 13.],[14., 15.]]]])
tensor([[[[ 0.5000],[ 2.5000]],[[ 4.5000],[ 6.5000]]],[[[ 8.5000],[10.5000]],[[12.5000],[14.5000]]]]) torch.Size([2, 2, 2, 1])
a = torch.Tensor([0, 1, 2, 3, 4, 5,6,7,8,9,10,11,12,13,14,15,0, 1, 2, 3, 4, 5,6,7,8,9,10,11,12,13,14,15]).view(2, 2, 2,2,2)
print(a)
mean = torch.mean(a, 3, True)
print(mean, mean.shape)
tensor([[[[[ 0., 1.],[ 2., 3.]],[[ 4., 5.],[ 6., 7.]]],[[[ 8., 9.],[10., 11.]],[[12., 13.],[14., 15.]]]],[[[[ 0., 1.],[ 2., 3.]],[[ 4., 5.],[ 6., 7.]]],[[[ 8., 9.],[10., 11.]],[[12., 13.],[14., 15.]]]]])
tensor([[[[[ 1., 2.]],[[ 5., 6.]]],[[[ 9., 10.]],[[13., 14.]]]],[[[[ 1., 2.]],[[ 5., 6.]]],[[[ 9., 10.]],[[13., 14.]]]]]) torch.Size([2, 2, 2, 1, 2])这篇关于torch.mean()的使用方法的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！
