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Consider I have 2D Tensor, index_in_batch * diag_ele.
How can I get a 3D Tensor index_in_batch * Matrix (who is a diagonal matrix, construct by drag_ele)?
The torch.diag() construct diagonal matrix only when input is 1D, and return diagonal element when input is 2D.
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Creating Tensors Tensors can be created from Python lists with the torch. tensor() function. tensor([1., 2., 3.]) tensor([[1., 2., 3.], [4., 5., 6.]])
mul() method is used to perform element-wise multiplication on tensors in PyTorch. It multiplies the corresponding elements of the tensors. We can multiply two or more tensors. We can also multiply scalar and tensors.
To create an identity matrix, we use the torch. The number of columns are by default set to the number of rows. You may change the number of rows by providing it as a parameter. This method returns a 2D tensor (matrix) whose diagonals are 1's and all other elements are 0.
import torch
a = torch.rand(2, 3)
print(a)
b = torch.eye(a.size(1))
c = a.unsqueeze(2).expand(*a.size(), a.size(1))
d = c * b
print(d)
Output
0.5938 0.5769 0.0555
0.9629 0.5343 0.2576
[torch.FloatTensor of size 2x3]
(0 ,.,.) =
0.5938 0.0000 0.0000
0.0000 0.5769 0.0000
0.0000 0.0000 0.0555
(1 ,.,.) =
0.9629 0.0000 0.0000
0.0000 0.5343 0.0000
0.0000 0.0000 0.2576
[torch.FloatTensor of size 2x3x3]
Use torch.diag_embed:
>>> a = torch.randn(2, 3)
>>> torch.diag_embed(a)
tensor([[[ 1.5410, 0.0000, 0.0000],
[ 0.0000, -0.2934, 0.0000],
[ 0.0000, 0.0000, -2.1788]],
[[ 0.5684, 0.0000, 0.0000],
[ 0.0000, -1.0845, 0.0000],
[ 0.0000, 0.0000, -1.3986]]])
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