torch学习——持续补充

torch和numpy的转换

重点1

torch和numpy互相转换的时候，内存是共享的！因此改一个，必将修改另一个

import numpy as np
a = np.random.rand(4,3)
print(a)
array([[0.53097097, 0.39967826, 0.2925655 ],
       [0.41214625, 0.85092555, 0.88813825],
       [0.74869623, 0.20646449, 0.83864256],
       [0.80688304, 0.89462968, 0.12394582]])
b = torch.from_numpy(a)
print(b)
0.5310  0.3997  0.2926
 0.4121  0.8509  0.8881
 0.7487  0.2065  0.8386
 0.8069  0.8946  0.1239
[torch.DoubleTensor of size 4x3]
b.mul_(2)
print(b)
print(a)
1.0619  0.7994  0.5851
 0.8243  1.7019  1.7763
 1.4974  0.4129  1.6773
 1.6138  1.7893  0.2479
[torch.DoubleTensor of size 4x3]
array([[1.06194194, 0.79935653, 0.58513101],
       [0.8242925 , 1.7018511 , 1.7762765 ],
       [1.49739246, 0.41292898, 1.67728512],
       [1.61376609, 1.78925936, 0.24789164]])

---

重点2

当一个变量(numpy和torch都是)被重新赋值的时候，其内存会被重新分配！

a=np.random.rand(3,2)
print(id(a))
139997338436912
a=a*2
print(id(a))
139997338438112

---