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