相关疑难解决方法(0)

了解重塑张量时的顺序

对于张量:

x = torch.tensor([
    [
        [[0.4495, 0.2356],
          [0.4069, 0.2361],
          [0.4224, 0.2362]],
                   
         [[0.4357, 0.6762],
          [0.4370, 0.6779],
          [0.4406, 0.6663]]
    ],    
    [
        [[0.5796, 0.4047],
          [0.5655, 0.4080],
          [0.5431, 0.4035]],
         
         [[0.5338, 0.6255],
          [0.5335, 0.6266],
          [0.5204, 0.6396]]
    ]
])
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首先想将其分成 2 个 (x.shape[0]) 张量,然后将它们连接起来。在这里,只要获得正确的输出,我实际上并不需要将其拆分,但在视觉上将其拆分然后将它们连接在一起对我来说更有意义。

例如:

# the shape of the splits are always the same
split1 = torch.tensor([
    [[0.4495, 0.2356],
    [0.4069, 0.2361],
    [0.4224, 0.2362]],

    [[0.4357, 0.6762],
    [0.4370, 0.6779],
    [0.4406, 0.6663]]
])
split2 = torch.tensor([
    [[0.5796, 0.4047],
    [0.5655, 0.4080],
    [0.5431, 0.4035]],

    [[0.5338, 0.6255],
    [0.5335, 0.6266],
    [0.5204, 0.6396]] …
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python numpy reshape pytorch tensor

1
推荐指数
1
解决办法
6423
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标签 统计

numpy ×1

python ×1

pytorch ×1

reshape ×1

tensor ×1