python - 让两个输入之间的欧几里德距离在一个循环内填充一个矩阵

Question

我采用两个维度张量(批量大小，D1、D2、D3)并将它们展平为(批量大小，D1)。然后，我尝试获取一个(火车)张量的每一行与第二个张量(测试)的每一行之间的欧几里德距离。我无法理解如何将张量之间的距离组合填充到第 i 列的每个行元素中。

  # Flatten
  train = x_train.view(num_train, x_train[1].view(1, -1).shape[1])
  test = x_test.view(num_test, x_test[1].view(1, -1).shape[1])

  # 1 Loop
  for i in range(num_test):
    dists[:,i] = torch.sqrt(torch.sum(torch.square(train-test[i])))

在一个循环中执行此操作，每个第 i 列都将填充相同的标量值。我正在努力做到这一点

[1,i] = Euclidean distance between 1st image of train and 1st image of test ...
[2,i] = Euclidean distance between 1st image of train and 2nd image of test ...
[3,i] = Euclidean distance between 1st image of train and 3rd image of test
...
[(last element),i] = Euclidean distance between 1st image of train and (last element) image of test ...
[1,i+1] = Euclidean distance between 2nd image of train and 1st image of test

Answer 1

好吧，我在 torch.sum() 中遗漏了一个非常重要的参数，我不知道它可以解决这个问题。添加 1，使其看起来像 torch.sqrt(torch.sum(torch.square(train-test[i]), 1)) 输出到我想要的内容。