PyTorch - multiplying tensor with scalar results in zero vector
I have no idea why the result is all 0 with tensor. Anything wrong here?
>>> import torch
>>> import numpy as np
>>> import math
>>> torch.__version__
'0.4.1'
>>> np.__version__
'1.15.4'
>>> torch.arange(0, 10, 2) *-(math.log(10000.0) / 10)
tensor([0, 0, 0, 0, 0])
>>> np.arange(0, 10, 2) *-(math.log(10000.0) / 10)
array([-0. , -1.84206807, -3.68413615, -5.52620422, -7.3682723 ])
>>> torch.arange(0, 10, 2)
tensor([0, 2, 4, 6, 8])
>>> np.arange(0, 10, 2)
array([0, 2, 4, 6, 8])
python numpy pytorch
edited Nov 25 '18 at 20:43
blue-phoenox
4,291101745
asked Nov 25 '18 at 11:33
KuoKuo
287
add a comment |
I have no idea why the result is all 0 with tensor. Anything wrong here?
>>> import torch
>>> import numpy as np
>>> import math
>>> torch.__version__
'0.4.1'
>>> np.__version__
'1.15.4'
>>> torch.arange(0, 10, 2) *-(math.log(10000.0) / 10)
tensor([0, 0, 0, 0, 0])
>>> np.arange(0, 10, 2) *-(math.log(10000.0) / 10)
array([-0. , -1.84206807, -3.68413615, -5.52620422, -7.3682723 ])
>>> torch.arange(0, 10, 2)
tensor([0, 2, 4, 6, 8])
>>> np.arange(0, 10, 2)
array([0, 2, 4, 6, 8])
python numpy pytorch
edited Nov 25 '18 at 20:43
blue-phoenox
4,291101745
asked Nov 25 '18 at 11:33
KuoKuo
287
Results intensor([-0.0000, -1.8421, -3.6841, -5.5262, -7.3683])for me, v0.4.0a0+3749c58
– blue-phoenox
Nov 25 '18 at 11:46
Python version 3.7.1
– Kuo
Nov 25 '18 at 11:54
add a comment |
I have no idea why the result is all 0 with tensor. Anything wrong here?
>>> import torch
>>> import numpy as np
>>> import math
>>> torch.__version__
'0.4.1'
>>> np.__version__
'1.15.4'
>>> torch.arange(0, 10, 2) *-(math.log(10000.0) / 10)
tensor([0, 0, 0, 0, 0])
>>> np.arange(0, 10, 2) *-(math.log(10000.0) / 10)
array([-0. , -1.84206807, -3.68413615, -5.52620422, -7.3682723 ])
>>> torch.arange(0, 10, 2)
tensor([0, 2, 4, 6, 8])
>>> np.arange(0, 10, 2)
array([0, 2, 4, 6, 8])
python numpy pytorch
edited Nov 25 '18 at 20:43
blue-phoenox
4,291101745
asked Nov 25 '18 at 11:33
KuoKuo
287
I have no idea why the result is all 0 with tensor. Anything wrong here?
>>> import torch
>>> import numpy as np
>>> import math
>>> torch.__version__
'0.4.1'
>>> np.__version__
'1.15.4'
>>> torch.arange(0, 10, 2) *-(math.log(10000.0) / 10)
tensor([0, 0, 0, 0, 0])
>>> np.arange(0, 10, 2) *-(math.log(10000.0) / 10)
array([-0. , -1.84206807, -3.68413615, -5.52620422, -7.3682723 ])
>>> torch.arange(0, 10, 2)
tensor([0, 2, 4, 6, 8])
>>> np.arange(0, 10, 2)
array([0, 2, 4, 6, 8])
python numpy pytorch
python numpy pytorch
edited Nov 25 '18 at 20:43
blue-phoenox
4,291101745
asked Nov 25 '18 at 11:33
KuoKuo
287
edited Nov 25 '18 at 20:43
blue-phoenox
4,291101745
asked Nov 25 '18 at 11:33
KuoKuo
287
edited Nov 25 '18 at 20:43
blue-phoenox
4,291101745
edited Nov 25 '18 at 20:43
blue-phoenox
4,291101745
edited Nov 25 '18 at 20:43
blue-phoenox
4,291101745
4,291101745
asked Nov 25 '18 at 11:33
KuoKuo
287
asked Nov 25 '18 at 11:33
KuoKuo
287
asked Nov 25 '18 at 11:33
KuoKuo
287
287
Results intensor([-0.0000, -1.8421, -3.6841, -5.5262, -7.3683])for me, v0.4.0a0+3749c58
– blue-phoenox
Nov 25 '18 at 11:46
Python version 3.7.1
– Kuo
Nov 25 '18 at 11:54
add a comment |
Results intensor([-0.0000, -1.8421, -3.6841, -5.5262, -7.3683])for me, v0.4.0a0+3749c58
– blue-phoenox
Nov 25 '18 at 11:46
Python version 3.7.1
– Kuo
Nov 25 '18 at 11:54
Results in
tensor([-0.0000, -1.8421, -3.6841, -5.5262, -7.3683]) for me, v0.4.0a0+3749c58– blue-phoenox
Nov 25 '18 at 11:46
Results in
tensor([-0.0000, -1.8421, -3.6841, -5.5262, -7.3683]) for me, v0.4.0a0+3749c58– blue-phoenox
Nov 25 '18 at 11:46
Python version 3.7.1
– Kuo
Nov 25 '18 at 11:54
Python version 3.7.1
– Kuo
Nov 25 '18 at 11:54
add a comment |
1 Answer
1
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As written in the comment when using 0.4.0 get the same results as with numpy:
tensor([-0.0000, -1.8421, -3.6841, -5.5262, -7.3683])
However with 0.4.1 I'm getting a zero vector too.
The reason for this is that torch.arange(0, 10, 2) returns a tensor of type float for 0.4.0 while it returns a tensor of type long for 0.4.1.
So casting your tensor to float should work for you:
torch.arange(0, 10, 2).float() *-(math.log(10000.0) / 10)
Multiplying long and float works by heavy rounding, as the result is still a tensor of type long. So when converting a FloatTensor to a LongTensor values between -1 and 1 will be rounded to 0.
Since -(math.log(10000.0) / 10) results in -0.9210340371976183 your result is 0. So effectively -0.9210340371976183 is converted to type long before multiplying. But when converting it will be round down to 0, see this example:
t = torch.tensor((-(math.log(10000.0) / 10)))
print('FloatTensor:', t)
print('Converted to Long:', t.long())
Outout:
FloatTensor: tensor(-0.9210)
Converted to Long: tensor(0)
Thus:
torch.arange(0, 10, 2).float() *-(math.log(10000.0) / 10)
becomes:
torch.arange(0, 10, 2).float() * 0
Therefore you get a tensor of zeros as result.
Some more examples:
If you multiply it with a value between 1 and 2, lets say 1.7, it will always been rounded down to 1:
t = torch.tensor(range(5), dtype=torch.long)
print(t)
print(t * 1.7)
Output:
tensor([ 0, 1, 2, 3, 4])
tensor([ 0, 1, 2, 3, 4])
And similarly when multiplying with 2.7 results in an effective multiplication of 2:
t = torch.tensor(range(5), dtype=torch.long)
print(t)
print(t * 2.7)
Output:
tensor([ 0, 1, 2, 3, 4])
tensor([ 0, 2, 4, 6, 8])
answered Nov 25 '18 at 11:54
blue-phoenoxblue-phoenox
4,291101745
Yes. Can you elaborate a bit more on why is that? Without .float(), I add/minus 1,2... to (math.log(10000.0) / 10) and will get something like tensor([ 0, -2, -4, -6, -8]), tensor([0, 0, 0, 0, 0]), tensor([0, 2, 4, 6, 8])...
– Kuo
Nov 25 '18 at 12:08
@Kuo Thanks, I added some more examples to make the behaviour of multiplicationlong*floatmore clear!
– blue-phoenox
Nov 25 '18 at 12:19
add a comment |
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As written in the comment when using 0.4.0 get the same results as with numpy:
tensor([-0.0000, -1.8421, -3.6841, -5.5262, -7.3683])
However with 0.4.1 I'm getting a zero vector too.
The reason for this is that torch.arange(0, 10, 2) returns a tensor of type float for 0.4.0 while it returns a tensor of type long for 0.4.1.
So casting your tensor to float should work for you:
torch.arange(0, 10, 2).float() *-(math.log(10000.0) / 10)
Multiplying long and float works by heavy rounding, as the result is still a tensor of type long. So when converting a FloatTensor to a LongTensor values between -1 and 1 will be rounded to 0.
Since -(math.log(10000.0) / 10) results in -0.9210340371976183 your result is 0. So effectively -0.9210340371976183 is converted to type long before multiplying. But when converting it will be round down to 0, see this example:
t = torch.tensor((-(math.log(10000.0) / 10)))
print('FloatTensor:', t)
print('Converted to Long:', t.long())
Outout:
FloatTensor: tensor(-0.9210)
Converted to Long: tensor(0)
Thus:
torch.arange(0, 10, 2).float() *-(math.log(10000.0) / 10)
becomes:
torch.arange(0, 10, 2).float() * 0
Therefore you get a tensor of zeros as result.
Some more examples:
If you multiply it with a value between 1 and 2, lets say 1.7, it will always been rounded down to 1:
t = torch.tensor(range(5), dtype=torch.long)
print(t)
print(t * 1.7)
Output:
tensor([ 0, 1, 2, 3, 4])
tensor([ 0, 1, 2, 3, 4])
And similarly when multiplying with 2.7 results in an effective multiplication of 2:
t = torch.tensor(range(5), dtype=torch.long)
print(t)
print(t * 2.7)
Output:
tensor([ 0, 1, 2, 3, 4])
tensor([ 0, 2, 4, 6, 8])
answered Nov 25 '18 at 11:54
blue-phoenoxblue-phoenox
4,291101745
Yes. Can you elaborate a bit more on why is that? Without .float(), I add/minus 1,2... to (math.log(10000.0) / 10) and will get something like tensor([ 0, -2, -4, -6, -8]), tensor([0, 0, 0, 0, 0]), tensor([0, 2, 4, 6, 8])...
– Kuo
Nov 25 '18 at 12:08
@Kuo Thanks, I added some more examples to make the behaviour of multiplicationlong*floatmore clear!
– blue-phoenox
Nov 25 '18 at 12:19
add a comment |
As written in the comment when using 0.4.0 get the same results as with numpy:
tensor([-0.0000, -1.8421, -3.6841, -5.5262, -7.3683])
However with 0.4.1 I'm getting a zero vector too.
The reason for this is that torch.arange(0, 10, 2) returns a tensor of type float for 0.4.0 while it returns a tensor of type long for 0.4.1.
So casting your tensor to float should work for you:
torch.arange(0, 10, 2).float() *-(math.log(10000.0) / 10)
Multiplying long and float works by heavy rounding, as the result is still a tensor of type long. So when converting a FloatTensor to a LongTensor values between -1 and 1 will be rounded to 0.
Since -(math.log(10000.0) / 10) results in -0.9210340371976183 your result is 0. So effectively -0.9210340371976183 is converted to type long before multiplying. But when converting it will be round down to 0, see this example:
t = torch.tensor((-(math.log(10000.0) / 10)))
print('FloatTensor:', t)
print('Converted to Long:', t.long())
Outout:
FloatTensor: tensor(-0.9210)
Converted to Long: tensor(0)
Thus:
torch.arange(0, 10, 2).float() *-(math.log(10000.0) / 10)
becomes:
torch.arange(0, 10, 2).float() * 0
Therefore you get a tensor of zeros as result.
Some more examples:
If you multiply it with a value between 1 and 2, lets say 1.7, it will always been rounded down to 1:
t = torch.tensor(range(5), dtype=torch.long)
print(t)
print(t * 1.7)
Output:
tensor([ 0, 1, 2, 3, 4])
tensor([ 0, 1, 2, 3, 4])
And similarly when multiplying with 2.7 results in an effective multiplication of 2:
t = torch.tensor(range(5), dtype=torch.long)
print(t)
print(t * 2.7)
Output:
tensor([ 0, 1, 2, 3, 4])
tensor([ 0, 2, 4, 6, 8])
answered Nov 25 '18 at 11:54
blue-phoenoxblue-phoenox
4,291101745
Yes. Can you elaborate a bit more on why is that? Without .float(), I add/minus 1,2... to (math.log(10000.0) / 10) and will get something like tensor([ 0, -2, -4, -6, -8]), tensor([0, 0, 0, 0, 0]), tensor([0, 2, 4, 6, 8])...
– Kuo
Nov 25 '18 at 12:08
@Kuo Thanks, I added some more examples to make the behaviour of multiplicationlong*floatmore clear!
– blue-phoenox
Nov 25 '18 at 12:19
add a comment |
As written in the comment when using 0.4.0 get the same results as with numpy:
tensor([-0.0000, -1.8421, -3.6841, -5.5262, -7.3683])
However with 0.4.1 I'm getting a zero vector too.
The reason for this is that torch.arange(0, 10, 2) returns a tensor of type float for 0.4.0 while it returns a tensor of type long for 0.4.1.
So casting your tensor to float should work for you:
torch.arange(0, 10, 2).float() *-(math.log(10000.0) / 10)
Multiplying long and float works by heavy rounding, as the result is still a tensor of type long. So when converting a FloatTensor to a LongTensor values between -1 and 1 will be rounded to 0.
Since -(math.log(10000.0) / 10) results in -0.9210340371976183 your result is 0. So effectively -0.9210340371976183 is converted to type long before multiplying. But when converting it will be round down to 0, see this example:
t = torch.tensor((-(math.log(10000.0) / 10)))
print('FloatTensor:', t)
print('Converted to Long:', t.long())
Outout:
FloatTensor: tensor(-0.9210)
Converted to Long: tensor(0)
Thus:
torch.arange(0, 10, 2).float() *-(math.log(10000.0) / 10)
becomes:
torch.arange(0, 10, 2).float() * 0
Therefore you get a tensor of zeros as result.
Some more examples:
If you multiply it with a value between 1 and 2, lets say 1.7, it will always been rounded down to 1:
t = torch.tensor(range(5), dtype=torch.long)
print(t)
print(t * 1.7)
Output:
tensor([ 0, 1, 2, 3, 4])
tensor([ 0, 1, 2, 3, 4])
And similarly when multiplying with 2.7 results in an effective multiplication of 2:
t = torch.tensor(range(5), dtype=torch.long)
print(t)
print(t * 2.7)
Output:
tensor([ 0, 1, 2, 3, 4])
tensor([ 0, 2, 4, 6, 8])
answered Nov 25 '18 at 11:54
blue-phoenoxblue-phoenox
4,291101745
As written in the comment when using 0.4.0 get the same results as with numpy:
tensor([-0.0000, -1.8421, -3.6841, -5.5262, -7.3683])
However with 0.4.1 I'm getting a zero vector too.
The reason for this is that torch.arange(0, 10, 2) returns a tensor of type float for 0.4.0 while it returns a tensor of type long for 0.4.1.
So casting your tensor to float should work for you:
torch.arange(0, 10, 2).float() *-(math.log(10000.0) / 10)
Multiplying long and float works by heavy rounding, as the result is still a tensor of type long. So when converting a FloatTensor to a LongTensor values between -1 and 1 will be rounded to 0.
Since -(math.log(10000.0) / 10) results in -0.9210340371976183 your result is 0. So effectively -0.9210340371976183 is converted to type long before multiplying. But when converting it will be round down to 0, see this example:
t = torch.tensor((-(math.log(10000.0) / 10)))
print('FloatTensor:', t)
print('Converted to Long:', t.long())
Outout:
FloatTensor: tensor(-0.9210)
Converted to Long: tensor(0)
Thus:
torch.arange(0, 10, 2).float() *-(math.log(10000.0) / 10)
becomes:
torch.arange(0, 10, 2).float() * 0
Therefore you get a tensor of zeros as result.
Some more examples:
If you multiply it with a value between 1 and 2, lets say 1.7, it will always been rounded down to 1:
t = torch.tensor(range(5), dtype=torch.long)
print(t)
print(t * 1.7)
Output:
tensor([ 0, 1, 2, 3, 4])
tensor([ 0, 1, 2, 3, 4])
And similarly when multiplying with 2.7 results in an effective multiplication of 2:
t = torch.tensor(range(5), dtype=torch.long)
print(t)
print(t * 2.7)
Output:
tensor([ 0, 1, 2, 3, 4])
tensor([ 0, 2, 4, 6, 8])
answered Nov 25 '18 at 11:54
blue-phoenoxblue-phoenox
4,291101745
edited Nov 25 '18 at 16:21
answered Nov 25 '18 at 11:54
blue-phoenoxblue-phoenox
4,291101745
answered Nov 25 '18 at 11:54
blue-phoenoxblue-phoenox
4,291101745
answered Nov 25 '18 at 11:54
blue-phoenoxblue-phoenox
4,291101745
4,291101745
Yes. Can you elaborate a bit more on why is that? Without .float(), I add/minus 1,2... to (math.log(10000.0) / 10) and will get something like tensor([ 0, -2, -4, -6, -8]), tensor([0, 0, 0, 0, 0]), tensor([0, 2, 4, 6, 8])...
– Kuo
Nov 25 '18 at 12:08
@Kuo Thanks, I added some more examples to make the behaviour of multiplicationlong*floatmore clear!
– blue-phoenox
Nov 25 '18 at 12:19
add a comment |
Yes. Can you elaborate a bit more on why is that? Without .float(), I add/minus 1,2... to (math.log(10000.0) / 10) and will get something like tensor([ 0, -2, -4, -6, -8]), tensor([0, 0, 0, 0, 0]), tensor([0, 2, 4, 6, 8])...
– Kuo
Nov 25 '18 at 12:08
@Kuo Thanks, I added some more examples to make the behaviour of multiplicationlong*floatmore clear!
– blue-phoenox
Nov 25 '18 at 12:19
Yes. Can you elaborate a bit more on why is that? Without .float(), I add/minus 1,2... to (math.log(10000.0) / 10) and will get something like tensor([ 0, -2, -4, -6, -8]), tensor([0, 0, 0, 0, 0]), tensor([0, 2, 4, 6, 8])...
– Kuo
Nov 25 '18 at 12:08
Yes. Can you elaborate a bit more on why is that? Without .float(), I add/minus 1,2... to (math.log(10000.0) / 10) and will get something like tensor([ 0, -2, -4, -6, -8]), tensor([0, 0, 0, 0, 0]), tensor([0, 2, 4, 6, 8])...
– Kuo
Nov 25 '18 at 12:08
@Kuo Thanks, I added some more examples to make the behaviour of multiplication
long * float more clear!– blue-phoenox
Nov 25 '18 at 12:19
@Kuo Thanks, I added some more examples to make the behaviour of multiplication
long * float more clear!– blue-phoenox
Nov 25 '18 at 12:19
add a comment |
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Results in
tensor([-0.0000, -1.8421, -3.6841, -5.5262, -7.3683])for me, v0.4.0a0+3749c58– blue-phoenox
Nov 25 '18 at 11:46
Python version 3.7.1
– Kuo
Nov 25 '18 at 11:54