A Tensor is the fondamental data type in torch, (similar to numpy for tensorflow), it's a potentially multi-dimensional matrix.
See doc: https://github.com/torch/torch7/blob/master/doc/tensor.md
basic ops
-
Indicate shape in constructor:
th> x = torch.Tensor(3,4) [0.0000s] th> x 3.7366e+193 9.4775e+170 3.3018e+180 4.8950e-85 1.3808e+267 7.6859e+261 3.7512e-81 1.4692e+195 9.7016e+189 6.9641e+252 9.1815e+170 4.5239e+217 [torch.DoubleTensor of size 3x4]
By default the elements of a newly allocated memory are not initialized, might contain arbitrary numbers !
x:dim(): return nb of dimensionsx:nElement(): return nb of elements ("size")x:size(): return "shape", shortcut is:#xx:resize(sz1, sz2, ...): will not throw exception when total size is inconsistent!
th> #x
3
4
[torch.LongStorage of size 2]
[0.0001s]
th> x:size()
3
4
[torch.LongStorage of size 2]
[0.0001s]
th> x:dim()
2
th> x = torch.Tensor(3,4)
[0.0000s]
th> x:resize(2,6)
1.7479e+270 7.0981e+194 7.4861e-114 1.7479e+270 8.2791e-114 3.6822e+180
4.8548e-27 6.9204e-72 8.8289e+199 1.1567e+247 4.8548e-27 7.7700e-109
[torch.DoubleTensor of size 2x6]
[0.0002s]
th> x:resize(2,7)
Columns 1 to 6
1.7479e+270 7.0981e+194 7.4861e-114 1.7479e+270 8.2791e-114 3.6822e+180
6.9204e-72 8.8289e+199 1.1567e+247 4.8548e-27 7.7700e-109 6.9006e-72
Columns 7 to 7
4.8548e-27
1.0240e-259
[torch.DoubleTensor of size 2x7]
[0.0001s]
th> x:resize(2,4)
1.7479e+270 7.0981e+194 7.4861e-114 1.7479e+270
8.2791e-114 3.6822e+180 4.8548e-27 6.9204e-72
[torch.DoubleTensor of size 2x4]
-
fill with constant value:
th> x:fill(1) 1 1 1 1 1 1 1 1 1 1 1 1 [torch.DoubleTensor of size 3x4]
-
other constructors:
th> y = torch.Tensor(x) -- note: y is just a reference of x!!!
[0.0001s]
th> y = torch.Tensor({ {2,3,4},{1,2,3} })
[0.0000s]
th> y
2 3 4
1 2 3
[torch.DoubleTensor of size 2x3]
Storage
One could say that a
Tensoris a particular way of viewing aStorage: aStorageonly represents a chunk of memory, while theTensorinterprets this chunk of memory as having dimensions.
th> s = x:storage() -- 'flatten' version of x
[0.0000s]
th> for i=1,#s do s[i]=i end
[0.0000s]
th> x
1 2 3 4
5 6 7 8
9 10 11 12
[torch.DoubleTensor of size 3x4]
short for range: torch.range(1,5)
Slicing
https://github.com/torch/torch7/blob/master/doc/tensor.md#extracting-sub-tensors
- Slicing using
sub()andselect().
th> x
1 2 3 4
5 6 7 8
9 10 11 12
[torch.DoubleTensor of size 3x4]
th> print(x:sub(2,3,2,4)) -- x[2:3, 2:4] slicing
6 7 8
10 11 12
[torch.DoubleTensor of size 2x3]
th> print(x:select(2,2)) -- select(dim, index), dim=1 for rows, =2 for cols
2
6
10
[torch.DoubleTensor of size 3]
- Or use slicing/indexing shortcut:
[{ {dim1start, dim1end},...}] [dim1, dim2,...]
th> x
1 2 3 4 5 6
7 8 9 10 11 12
13 14 15 16 17 18
19 20 21 22 23 24
25 26 27 28 29 30
[torch.DoubleTensor of size 5x6]
[0.0002s]
th> x[2][3]
9
[0.0000s]
th> x[{2,3}]
9
[0.0000s]
th> x[{2,{2,5}}]
8
9
10
11
[torch.DoubleTensor of size 4]
[0.0001s]
th> x[{ {}, 3}]
3
9
15
21
27
[torch.DoubleTensor of size 5]
All are references
All tensor operations in this class do not make any memory copy. All these methods transform the existing tensor, or return a new tensor referencing the same storage.
th> y = torch.Tensor(x)
[0.0000s]
th> y
1 2 3 4
5 6 7 8
9 10 11 12
[torch.DoubleTensor of size 3x4]
[0.0001s]
th> y:select(1,1):zero() -- x will be effected
[0.0001s]
th> y
0 0 0 0
5 6 7 8
9 10 11 12
[torch.DoubleTensor of size 3x4]
[0.0001s]
th> x
0 0 0 0
5 6 7 8
9 10 11 12
[torch.DoubleTensor of size 3x4]
If don't want to modify original tensor, use clone():
th> y = x:clone()
[0.0000s]
th> y:sub(1,3,1,2):fill(-1)
-1 -1
-1 -1
-1 -1
[torch.DoubleTensor of size 3x2]
[0.0001s]
th> y
-1 -1 0 0
-1 -1 7 8
-1 -1 11 12
[torch.DoubleTensor of size 3x4]
[0.0001s]
th> x
0 0 0 0
5 6 7 8
9 10 11 12
[torch.DoubleTensor of size 3x4]
Matrix ops
Some matrix operations
- random matrix:
th> torch.manualSeed(1)
[0.0000s]
th> A = torch.rand(3,4)
[0.0001s]
th> print(A)
0.4170 0.9972 0.7203 0.9326
0.0001 0.1281 0.3023 0.9990
0.1468 0.2361 0.0923 0.3966
[torch.DoubleTensor of size 3x4]
- transpose
th> At = A:t()
[0.0000s]
th> At
0.4170 0.0001 0.1468
0.9972 0.1281 0.2361
0.7203 0.3023 0.0923
0.9326 0.9990 0.3966
[torch.DoubleTensor of size 4x3]
- matrix mul is just
*
th> A * At
2.5568 1.2773 0.7330
1.2773 1.1059 0.4544
0.7330 0.4544 0.2431
[torch.DoubleTensor of size 3x3]
- inner product:
dot()
th> A[1]:dot(At:select(2,1))
2.5568154905493
- inverse:
torch.inverse(sq_mat)
More operations
can be found at:
https://github.com/torch/torch7/blob/master/doc/maths.md
example:
torch.ones()/eye()/zeros()torch.cat(): concat tensors
th> torch.cat(torch.ones(3, 2), torch.zeros(2, 2), 1)
1 1
1 1
1 1
0 0
0 0
[torch.DoubleTensor of size 5x2]
torch.conv2()
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