flatten函数就是对tensor类型进行扁平化处理，就是在不同维度上进行堆叠操作

a.flatten(m)，这个意思是将a这个tensor，从第m(m取值从0开始)维度开始堆叠，一直堆叠到最后一个维度

import torcha=torch.rand(2,3,2,3)print(a)x = a.flatten(0)
print(x)
print(x.size())y = a.flatten(1)
print(y)
print(y.size())z = a.flatten(2)
print(z)
print(z.size())#a.flatten()这个括号里面的参数也不只是只有一个，在官方文档里面的说法，这个里面可以是两个参数
#start_dim (int) – the first dim to flatten
#end_dim (int) – the last dim to flatten
#将a的0维度和1维度合并u = a.flatten(0,1)
print(u)
print(u.size())输出：
tensor([[[[0.9807, 0.8278, 0.2853],[0.2290, 0.3709, 0.6642]],[[0.2521, 0.0556, 0.3562],[0.3926, 0.9639, 0.3037]],[[0.9804, 0.7069, 0.8673],[0.0434, 0.5438, 0.7231]]],[[[0.7031, 0.2287, 0.0640],[0.5223, 0.0660, 0.5081]],[[0.2562, 0.4229, 0.8700],[0.1164, 0.5058, 0.2986]],[[0.6062, 0.2247, 0.4474],[0.2376, 0.5606, 0.5911]]]])
tensor([0.9807, 0.8278, 0.2853, 0.2290, 0.3709, 0.6642, 0.2521, 0.0556, 0.3562,0.3926, 0.9639, 0.3037, 0.9804, 0.7069, 0.8673, 0.0434, 0.5438, 0.7231,0.7031, 0.2287, 0.0640, 0.5223, 0.0660, 0.5081, 0.2562, 0.4229, 0.8700,0.1164, 0.5058, 0.2986, 0.6062, 0.2247, 0.4474, 0.2376, 0.5606, 0.5911])
torch.Size([36])tensor([[0.9807, 0.8278, 0.2853, 0.2290, 0.3709, 0.6642, 0.2521, 0.0556, 0.3562,0.3926, 0.9639, 0.3037, 0.9804, 0.7069, 0.8673, 0.0434, 0.5438, 0.7231],[0.7031, 0.2287, 0.0640, 0.5223, 0.0660, 0.5081, 0.2562, 0.4229, 0.8700,0.1164, 0.5058, 0.2986, 0.6062, 0.2247, 0.4474, 0.2376, 0.5606, 0.5911]])
torch.Size([2, 18])tensor([[[0.9807, 0.8278, 0.2853, 0.2290, 0.3709, 0.6642],[0.2521, 0.0556, 0.3562, 0.3926, 0.9639, 0.3037],[0.9804, 0.7069, 0.8673, 0.0434, 0.5438, 0.7231]],[[0.7031, 0.2287, 0.0640, 0.5223, 0.0660, 0.5081],[0.2562, 0.4229, 0.8700, 0.1164, 0.5058, 0.2986],[0.6062, 0.2247, 0.4474, 0.2376, 0.5606, 0.5911]]])
torch.Size([2, 3, 6])tensor([[[0.9807, 0.8278, 0.2853],[0.2290, 0.3709, 0.6642]],[[0.2521, 0.0556, 0.3562],[0.3926, 0.9639, 0.3037]],[[0.9804, 0.7069, 0.8673],[0.0434, 0.5438, 0.7231]],[[0.7031, 0.2287, 0.0640],[0.5223, 0.0660, 0.5081]],[[0.2562, 0.4229, 0.8700],[0.1164, 0.5058, 0.2986]],[[0.6062, 0.2247, 0.4474],[0.2376, 0.5606, 0.5911]]])
torch.Size([6, 2, 3])

transpose是Tensor类的一个重要方法，同时它也是torch模块中的一个函数

返回一个张量，它是输入张量的转置版本，其中将给定的维度dim0和dim1交换

import random
import torch#二维数据情况
arr = torch.rand(2,3)
print(arr)
print(arr.size())a = arr.transpose(1, 0)
print(a)
print(a.size())#三维数据情况
arr = torch.rand(2,3,4)
print(arr)
print(arr.size())a = arr.transpose(1, 0)
print(a)
print(a.size())b = arr.transpose(1, 2)
print(b)
print(b.size())输出：
tensor([[0.3193, 0.1526, 0.0878],[0.2070, 0.5021, 0.0383]])
torch.Size([2, 3])tensor([[0.3193, 0.2070],[0.1526, 0.5021],[0.0878, 0.0383]])
torch.Size([3, 2])tensor([[[0.9428, 0.8610, 0.7115, 0.2870],[0.0846, 0.5500, 0.8890, 0.6003],[0.2907, 0.1275, 0.9961, 0.9360]],[[0.3068, 0.2193, 0.6061, 0.3032],[0.3735, 0.1232, 0.4352, 0.2763],[0.5179, 0.7830, 0.1859, 0.1262]]])
torch.Size([2, 3, 4])tensor([[[0.9428, 0.8610, 0.7115, 0.2870],[0.3068, 0.2193, 0.6061, 0.3032]],[[0.0846, 0.5500, 0.8890, 0.6003],[0.3735, 0.1232, 0.4352, 0.2763]],[[0.2907, 0.1275, 0.9961, 0.9360],[0.5179, 0.7830, 0.1859, 0.1262]]])
torch.Size([3, 2, 4])tensor([[[0.6059, 0.7055, 0.8131],[0.3136, 0.1284, 0.1374],[0.8604, 0.0243, 0.3363],[0.5041, 0.0764, 0.0649]],[[0.6565, 0.1308, 0.7233],[0.6803, 0.9431, 0.8020],[0.2651, 0.7857, 0.4266],[0.4035, 0.1960, 0.8238]]])
torch.Size([2, 4, 3])