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# how to calculate cross entropy in 3d image pytorch?

By : user2185342
Date : November 21 2020, 04:01 AM
I wish did fix the issue. Exactly the same way as with any other image. Use binary_cross_entropy(left, right). Note that
Both have to be of torch.float32 dtype so you may need to first convert right using right.to(torch.float32). If your left tensor contains logits instead of probabilities it is better to call binary_cross_entropy_with_logits(left, right) than to call binary_cross_entropy(torch.sigmoid(left), right)
code :

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## Cross entropy loss in pytorch nn.CrossEntropyLoss()

By : Александр Яковлв
Date : March 29 2020, 07:55 AM
hope this fix your issue maybe someone is able to help me here. I am trying to compute the cross entropy loss of a given output of my network , Please check this code
code :
``````import torch
import torch.nn as nn

output = Variable(torch.rand(1,10))
target = Variable(torch.LongTensor([1]))

criterion = nn.CrossEntropyLoss()
loss = criterion(output, target)
print(loss)
``````
``````Variable containing:
2.4498
[torch.FloatTensor of size 1]
``````

## Cross Entropy in PyTorch

By : Leonardo A. Garza
Date : March 29 2020, 07:55 AM
hope this fix your issue In your example you are treating output [0,0,0,1] as probabilities as required by the mathematical definition of cross entropy. But PyTorch treats them as outputs, that don’t need to sum to 1, and need to be first converted into probabilities for which it uses the softmax function.
So H(p,q) becomes:

## Custom cross-entropy loss in pytorch

By : user2892869
Date : March 29 2020, 07:55 AM
will be helpful for those in need If you need just cross entropy you can take the advantage PyTorch defined that.
code :
``````import torch.nn.functional as F
loss_func = F.cross_entropy
``````
``````def log_softmax(x):
return x - x.exp().sum(-1).log().unsqueeze(-1)
``````
``````nll_loss(log_softmax(input, 1), target, weight, None, ignore_index, None, reduction)
``````

## Semantic Segmentation — Usage of Categorical Cross-Entropy in spite of Binary Cross-Entropy for Binary Image Segmentatio

By : user3417310
Date : March 29 2020, 07:55 AM
around this issue The answer is provided at the following link Binary cross entropy Vs categorical cross entropy with 2 classes.
It is stated that from a mathematical viewpoint(result, not computational overhead), softmax on two classes is exactly the same like in case of BCE, the same answer as @f4f.

## How do I calculate cross-entropy from probabilities in PyTorch?

By : user3675524
Date : March 29 2020, 07:55 AM
Hope that helps There is a reduction parameter for all loss functions in the PyTorch. As you can see from the documentation default reduction parameter is 'mean' which divides the sum with number of elements in the batch. To get a summation behavior (0.4338) as you want, you should give the reduction parameter as following:
code :
``````F.nll_loss(torch.log(probs), labels,reduction='sum')
``````