【机器学习】Pytorch常用的方法
目录
-
- arrange
- permute
- range
- repeat
- tensor
arrange
torch.arange(start=0, end, step=1, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor
返回一个元素个数为(end-start)/step，维度是1*D的tensor。元素里面不包含end
permute
permute(*dims) → Tensor
对任意高维矩阵进行转置。
>>> x = torch.randn(2,3,5)
>>> x.size()
torch.Size([2, 3, 5])
>>> x
tensor([[[-0.5745, 1.1764, 0.7518, 0.9021, 2.4685],
[ 1.2288, -0.1018, 1.2049, -0.1429, 1.2599],
[ 1.9483, -0.6745, 0.9937, -0.0432, -1.2532]],
[[ 0.7589, 0.6292, -0.2349, -1.0436, 0.4462],
[-0.3739, 0.9542, 0.4136, -0.8185, 1.0435],
[-1.8790, -0.4924, -0.5502, -2.4309, 1.0902]]])
>>> x.permute(2,0,1)
tensor([[[-0.5745, 1.2288, 1.9483],
[ 0.7589, -0.3739, -1.8790]],
[[ 1.1764, -0.1018, -0.6745],
[ 0.6292, 0.9542, -0.4924]],
[[ 0.7518, 1.2049, 0.9937],
[-0.2349, 0.4136, -0.5502]],
[[ 0.9021, -0.1429, -0.0432],
[-1.0436, -0.8185, -2.4309]],
[[ 2.4685, 1.2599, -1.2532],
[ 0.4462, 1.0435, 1.0902]]])
>>> x.permute(2,0,1).size()
torch.Size([5, 2, 3])
注意：
1、没有torch.permute这样的用法。
2、不会修改x自身。
range
torch.range(start=0, end, step=1, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor
返回一个元素个数为(end-start)/step，维度是1*D的tensor。元素里面包含end
repeat
repeat(*sizes) → Tensor
根据特定维度复制扩展Tensor
>>> x=torch.tensor([1, 2, 3])
>>> x.repeat(4, 2)
tensor([[1, 2, 3, 1, 2, 3],
[1, 2, 3, 1, 2, 3],
[1, 2, 3, 1, 2, 3],
[1, 2, 3, 1, 2, 3]])
>>> x.repeat(4, 2, 1)
tensor([[[1, 2, 3],
[1, 2, 3]],
[[1, 2, 3],
[1, 2, 3]],
[[1, 2, 3],
[1, 2, 3]],
[[1, 2, 3],
[1, 2, 3]]])
tensor
torch.tensor(data, dtype=None, device=None, requires_grad=False, pin_memory=False) → Tensor
使用data构建一个tensor
>>> import torch
>>> torch.tensor([[0.1, 1.2], [2.2, 3.1], [4.9, 5.2]])
tensor([[0.1000, 1.2000],
[2.2000, 3.1000],
[4.9000, 5.2000]])
>>> torch.tensor([[0.11111, 0.222222, 0.3333333]]).shape
torch.Size([1, 3])
>>> torch.tensor(3.14159) #0维Tensor
tensor(3.1416)