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pass
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pass
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yes,pass
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first dimension:2
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first dimension
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not match!
A = torch.arange(20, dtype = torch.float32).reshape(5, 4)
A / A.sum(axis=1)
RuntimeError: The size of tensor a (4) must match the size of tensor b (5) at non-singleton dimension 1
It will be fine.
B = torch.arange(25, dtype = torch.float32).reshape(5, 5)
B / B.sum(axis=1)
tensor([[0.0000, 0.0286, 0.0333, 0.0353, 0.0364],
[0.5000, 0.1714, 0.1167, 0.0941, 0.0818],
[1.0000, 0.3143, 0.2000, 0.1529, 0.1273],
[1.5000, 0.4571, 0.2833, 0.2118, 0.1727],
[2.0000, 0.6000, 0.3667, 0.2706, 0.2182]])
- Walk:Manhattan’s distance.the â„“1 norm
# distances of avenues and streets
dist_ave = 30.0
dist_str = 40.0
dis_2pt = torch.tensor([dist_ave, dist_str])
torch.abs(dis_2pt).sum()
Can. Fly straightly and diagonally.the â„“2 norm
torch.norm(dis_2pt)
tensor(50.)
- The shape is just the shape of the original tensor that deleted the axis required.
X.sum(axis = 0).size() torch.Size([3, 4])
X.sum(axis = 1).size() torch.Size([2, 4])
X.sum(axis = 2).size() torch.Size([2, 3])
- $|\mathbf{x}|{2}=\sqrt{\sum{i=1}^{n} x_{i}^{2}}$
Y= torch.arange(24,dtype = torch.float32).reshape(2, 3, 4)
torch.norm(Y)
tensor(65.7571)
i = 0
for j in range(24):
i += j**2
j += 1
import math
print(math.sqrt(i))
65.75712889109438
The numbers are same.
For more:
The matrix should be indexed via nm instead of mn, since the original matrix is mn and this is the transposed version.
Hey @goldpiggy, the link given includes a # query parameter which directly links to “the matrix”. Does that work for you?
Hi @manuel-arno-korfmann, you mean the matirx index like $a_12$ and $a_21$? The indexes’ location is flipped, while they have to keep the original values. Ultimately, $a_mn$ and $a_nm$ have different values at the original matrix
