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Inplace arithmetic operation versus normal arithmetic operation in PyTorch Tensors


Inplace arithmetic operation versus normal arithmetic operation in PyTorch Tensors

By : siti farida
Date : October 25 2020, 07:10 PM
To fix the issue you can do I am trying to build Linear regression using Pytorch framework and while implementing Gradient Descent, I observed two different outputs based on how I use arithmetic operation in Python code. Below is the code: , I think the reason is simple. When you do:
code :
w = w - lr * w.grad
b = b - lr * b.grad


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How time consuming is TImeSpan arithmetic compared to normal arithmetic?

How time consuming is TImeSpan arithmetic compared to normal arithmetic?


By : Theme Squared
Date : March 29 2020, 07:55 AM
wish helps you That would be unwise, Timespan.TotalMilliseconds is a property of type double with a unit of one millisecond. Which is highly unrelated to the underlying structure value, Ticks is a property getter for the underlying field of type long with a unit of 100 nanoseconds. The TotalMilliseconds property getter goes through some gymnastics to convert the long to a double, it makes sure that converting back and forth produces the same number.
Which is a problem for TimeSpan, it can cover 10,000 years with a precision of 100 nanoseconds. A double however has 15 significant digits, that's not enough to cover that many years with that kind of precision. The TotalMilliseconds property performs rounding, not just conversion, it makes sure the returned value is accurate to one millisecond. Not 100 nanoseconds. So converting it back and forth always produces the same value.
Pytorch sum tensors doing an operation within each set of numbers

Pytorch sum tensors doing an operation within each set of numbers


By : Emre CAN
Date : March 29 2020, 07:55 AM
should help you out I have the following Pytorch tensor: , You can do
torch.abs(V1[:, 1]- V1[:, 0])
Unable to figure out inplace operation in the pytorch code?

Unable to figure out inplace operation in the pytorch code?


By : user3092260
Date : March 29 2020, 07:55 AM
hope this fix your issue I have the following implementation in PyTorch for learning using LSTM: , I think the issue is with the following line:
code :
global_loss_list.append(global_loss.detach_())
Pytorch specific operation for the finding dimension wise mean for a list of tensors

Pytorch specific operation for the finding dimension wise mean for a list of tensors


By : user3425066
Date : March 29 2020, 07:55 AM
With these it helps With some help from pytorch discussion forum, I could solve the problem. Link to discussion
The relevant code is:
code :
for item in embeddingLists:       
    tempItem = [stuff.unsqueeze(0)  for stuff in item] #convert a 1x300 tensor
    coomn = torch.cat(tempItem)     # Convert to a 12x300 tensor        
    temMean = torch.mean(coomn,dim=0)
    meanVects.append(temMean)
    temVar = torch.var(coomn,dim=0)
    varVects.append(temVar)
How is it possible that BITWISE AND operation to take more CPU clocks than ARITHMETIC ADDITION operation in a C program?

How is it possible that BITWISE AND operation to take more CPU clocks than ARITHMETIC ADDITION operation in a C program?


By : Skammers
Date : March 29 2020, 07:55 AM
Hope this helps I wanted to test if bitwise operations really are faster to execute than arithmetic operation. I thought they were. , ok, let's take this "measuring" and blow it up, 100k is a bit few
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