pytorch使用学习

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优点

多 GPU 支持，自定义数据加载器，极简的预处理过程

模块

PyTorch 张量

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torch.Tensor(5, 3)

---------------------------------------

2.4878e+04  4.5692e-41  2.4878e+04

4.5692e-41 -2.9205e+19  4.5691e-41

1.2277e-02  4.5692e-41 -4.0170e+19

4.5691e-41  1.2277e-02  4.5692e-41

0.0000e+00  0.0000e+00  0.0000e+00

[torch.FloatTensor of size 5x3]



torch.Tensor(5, 3).uniform_(-1, 1)

---------------------------------------------

-0.2767 -0.1082 -0.1339

-0.6477  0.3098  0.1642

-0.1125 -0.2104  0.8962

-0.6573  0.9669 -0.3806

0.8008 -0.3860  0.6816

[torch.FloatTensor of size 5x3]


>>> torch.FloatTensor([[1, 2, 3], [4, 5, 6]])

1  2  3

4  5  6

[torch.FloatTensor of size 2x3]

>>> print(x[1][2])

6.0

>>> x[0][1] = 8

>>> print(x)

1  8  3

4  5  6

[torch.FloatTensor of size 2x3]

cpu 2 gpu

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x = torch.FloatTensor(5, 3).uniform_(-1, 1)

print(x)

x = x.cuda(device=0)

print(x)

x = x.cpu()

print(x)

数学运算，自动求导模块，最优化模块，神经网络模块

请作者喝一杯咖啡☕️