1 # coding=utf-8
 2 
 3 import torch
 4 
 5 
 6 # 特别注明：任何可以改变tensor内容的操作都会在方法名后加一个下划线‘_‘
 7 # 例如：x.copy_(y), x.t_(), 这俩都会改变x的值。
 8 
 9 
10 ‘‘‘初始化操作‘‘‘
11 
12 x = torch.Tensor(5,3)      # 未初始化的张量
13 print(x)
14 x = torch.rand(5,3)        # 随机初始化的张量
15 print(x)
16 print(x.size())            # 查看张量的尺寸
17                            # 这是一个tuple

-4.5163e+29  4.5806e-41 -4.5163e+29
 4.5806e-41 -8.3134e+23  4.5806e-41
-8.3134e+23  4.5806e-41 -8.3134e+23
 4.5806e-41 -8.3134e+23  4.5806e-41
-8.3134e+23  4.5806e-41 -8.3134e+23
[torch.FloatTensor of size 5x3]


 0.3349  0.7324  0.7708
 0.2973  0.1546  0.5236
 0.2481  0.8018  0.3527
 0.2328  0.2714  0.5307
 0.3042  0.5355  0.2955
[torch.FloatTensor of size 5x3]

torch.Size([5, 3])

 1 ‘‘‘加法操作‘‘‘
 2 
 3 a = torch.ones(2,2)
 4 b = torch.zeros(2,2)
 5 
 6 # 语法一
 7 print(a+b)
 8 # 语法二
 9 print(torch.add(a,b))
10 # 语法三
11 print(b.add_(a))
12 # 语法三
13 c = torch.Tensor(2,2)
14 torch.add(a,b,out=c)
15 print(c)

 1  1
 1  1
[torch.FloatTensor of size 2x2]


 1  1
 1  1
[torch.FloatTensor of size 2x2]


 1  1
 1  1
[torch.FloatTensor of size 2x2]


 2  2
 2  2
[torch.FloatTensor of size 2x2]

 1 ‘‘‘Numpy桥‘‘‘
 2 
 3 a = torch.ones(3,2)
 4 
 5 a = torch.ones(5)
 6 b = a.numpy()     # tensor转换为array
 7 a.add_(1)
 8 print(b)          # a,b共用内存，属于浅拷贝
 9 
10 import numpy as np
11 
12 a = np.ones(5)
13 b = torch.from_numpy(a)   # array转换为tensor
14 np.add(a,1,out=a)
15 print(b)                  # a,b共用内存
16 
17 # 另外除了CharTensor之外，所有的tensor都可以在CPU运算和GPU预算之间相互转换
18 # 使用CUDA函数来将Tensor移动到GPU上
19 # 当CUDA可用时会进行GPU的运算
20 x = torch.ones(2,2)
21 y = torch.eye(2,2)
22 if torch.cuda.is_available():
23     x = x.cuda()
24     y = y.cuda()
25 print(x,y)
26 # print(x+y)       # 我的设备没配置好会出错，不过显卡太渣，以后有服务器玩再说吧

[ 2.  2.  2.  2.  2.]

 2
 2
 2
 2
 2
[torch.DoubleTensor of size 5]


 1  1
 1  1
[torch.cuda.FloatTensor of size 2x2 (GPU 0)]
 
 1  0
 0  1
[torch.cuda.FloatTensor of size 2x2 (GPU 0)]


Process finished with exit code 0