PyTorch: CrossEntropyLoss, changing class weight does not change the computed loss
According to Doc for cross entropy loss, the weighted loss is calculated by multiplying the weight for each class and the original loss.
However, in the pytorch implementation, the class weight seems to have no effect on the final loss value unless it is set to zero. Following is the code:
from torch import nn
import torch
logits = torch.FloatTensor([
[0.1, 0.9],
])
label = torch.LongTensor([0])
criterion = nn.CrossEntropyLoss(weight=torch.FloatTensor([1, 1]))
loss = criterion(logits, label)
print(loss.item()) # result: 1.1711
# Change class weight for the first class to 0.1
criterion = nn.CrossEntropyLoss(weight=torch.FloatTensor([0.1, 1]))
loss = criterion(logits, label)
print(loss.item()) # result: 1.1711, should be 0.11711
# Change weight for first class to 0
criterion = nn.CrossEntropyLoss(weight=torch.FloatTensor([0, 1]))
loss = criterion(logits, label)
print(loss.item()) # result: 0
As illustrated in the code, the class weight seems to have no effect unless it is set to 0, this behavior contradicts to the documentation.
Updates
I implemented a version of weighted cross entropy which is in my eyes the "correct" way to do it.
import torch
from torch import nn
def weighted_cross_entropy(logits, label, weight=None):
assert len(logits.size()) == 2
batch_size, label_num = logits.size()
assert (batch_size == label.size(0))
if weight is None:
weight = torch.ones(label_num).float()
assert (label_num == weight.size(0))
x_terms = -torch.gather(logits, 1, label.unsqueeze(1)).squeeze()
log_terms = torch.log(torch.sum(torch.exp(logits), dim=1))
weights = torch.gather(weight, 0, label).float()
return torch.mean((x_terms+log_terms)*weights)
logits = torch.FloatTensor([
[0.1, 0.9],
[0.0, 0.1],
])
label = torch.LongTensor([0, 1])
neg_weight = 0.1
weight = torch.FloatTensor([neg_weight, 1])
criterion = nn.CrossEntropyLoss(weight=weight)
loss = criterion(logits, label)
print(loss.item()) # results: 0.69227
print(weighted_cross_entropy(logits, label, weight).item()) # results: 0.38075
What I did is to multiply each instance in the batch with its associated class weight. The result is still different from the original pytorch implementation, which makes me wonder how pytorch actually implement this.
machine-learning pytorch
asked Jan 19 at 9:22
AveryLiuAveryLiu
17210
add a comment |
According to Doc for cross entropy loss, the weighted loss is calculated by multiplying the weight for each class and the original loss.
However, in the pytorch implementation, the class weight seems to have no effect on the final loss value unless it is set to zero. Following is the code:
from torch import nn
import torch
logits = torch.FloatTensor([
[0.1, 0.9],
])
label = torch.LongTensor([0])
criterion = nn.CrossEntropyLoss(weight=torch.FloatTensor([1, 1]))
loss = criterion(logits, label)
print(loss.item()) # result: 1.1711
# Change class weight for the first class to 0.1
criterion = nn.CrossEntropyLoss(weight=torch.FloatTensor([0.1, 1]))
loss = criterion(logits, label)
print(loss.item()) # result: 1.1711, should be 0.11711
# Change weight for first class to 0
criterion = nn.CrossEntropyLoss(weight=torch.FloatTensor([0, 1]))
loss = criterion(logits, label)
print(loss.item()) # result: 0
As illustrated in the code, the class weight seems to have no effect unless it is set to 0, this behavior contradicts to the documentation.
Updates
I implemented a version of weighted cross entropy which is in my eyes the "correct" way to do it.
import torch
from torch import nn
def weighted_cross_entropy(logits, label, weight=None):
assert len(logits.size()) == 2
batch_size, label_num = logits.size()
assert (batch_size == label.size(0))
if weight is None:
weight = torch.ones(label_num).float()
assert (label_num == weight.size(0))
x_terms = -torch.gather(logits, 1, label.unsqueeze(1)).squeeze()
log_terms = torch.log(torch.sum(torch.exp(logits), dim=1))
weights = torch.gather(weight, 0, label).float()
return torch.mean((x_terms+log_terms)*weights)
logits = torch.FloatTensor([
[0.1, 0.9],
[0.0, 0.1],
])
label = torch.LongTensor([0, 1])
neg_weight = 0.1
weight = torch.FloatTensor([neg_weight, 1])
criterion = nn.CrossEntropyLoss(weight=weight)
loss = criterion(logits, label)
print(loss.item()) # results: 0.69227
print(weighted_cross_entropy(logits, label, weight).item()) # results: 0.38075
What I did is to multiply each instance in the batch with its associated class weight. The result is still different from the original pytorch implementation, which makes me wonder how pytorch actually implement this.
machine-learning pytorch
asked Jan 19 at 9:22
AveryLiuAveryLiu
17210
2
discuss.pytorch.org/t/… Here is an explanation of how it works, cheers, sorry for confusion.
– Szymon Maszke
Jan 19 at 12:57
2
It turns out that changing the final line of theweighted_cross_entropytoreturn torch.sum((x_terms+log_terms)*weights)/torch.sum(weights)gives the correct behavior. Thanks for your efforts!
– AveryLiu
Jan 19 at 14:00
Though in the zero weight case the denominator would become zero, giving nan.
– AveryLiu
Jan 19 at 14:08
Implementation probably checks for this age case and returns zero.
– Szymon Maszke
Jan 19 at 14:11
yep, the pytorch implementation is correct. I commented on my own implementation.
– AveryLiu
Jan 19 at 14:13
add a comment |
According to Doc for cross entropy loss, the weighted loss is calculated by multiplying the weight for each class and the original loss.
However, in the pytorch implementation, the class weight seems to have no effect on the final loss value unless it is set to zero. Following is the code:
from torch import nn
import torch
logits = torch.FloatTensor([
[0.1, 0.9],
])
label = torch.LongTensor([0])
criterion = nn.CrossEntropyLoss(weight=torch.FloatTensor([1, 1]))
loss = criterion(logits, label)
print(loss.item()) # result: 1.1711
# Change class weight for the first class to 0.1
criterion = nn.CrossEntropyLoss(weight=torch.FloatTensor([0.1, 1]))
loss = criterion(logits, label)
print(loss.item()) # result: 1.1711, should be 0.11711
# Change weight for first class to 0
criterion = nn.CrossEntropyLoss(weight=torch.FloatTensor([0, 1]))
loss = criterion(logits, label)
print(loss.item()) # result: 0
As illustrated in the code, the class weight seems to have no effect unless it is set to 0, this behavior contradicts to the documentation.
Updates
I implemented a version of weighted cross entropy which is in my eyes the "correct" way to do it.
import torch
from torch import nn
def weighted_cross_entropy(logits, label, weight=None):
assert len(logits.size()) == 2
batch_size, label_num = logits.size()
assert (batch_size == label.size(0))
if weight is None:
weight = torch.ones(label_num).float()
assert (label_num == weight.size(0))
x_terms = -torch.gather(logits, 1, label.unsqueeze(1)).squeeze()
log_terms = torch.log(torch.sum(torch.exp(logits), dim=1))
weights = torch.gather(weight, 0, label).float()
return torch.mean((x_terms+log_terms)*weights)
logits = torch.FloatTensor([
[0.1, 0.9],
[0.0, 0.1],
])
label = torch.LongTensor([0, 1])
neg_weight = 0.1
weight = torch.FloatTensor([neg_weight, 1])
criterion = nn.CrossEntropyLoss(weight=weight)
loss = criterion(logits, label)
print(loss.item()) # results: 0.69227
print(weighted_cross_entropy(logits, label, weight).item()) # results: 0.38075
What I did is to multiply each instance in the batch with its associated class weight. The result is still different from the original pytorch implementation, which makes me wonder how pytorch actually implement this.
machine-learning pytorch
asked Jan 19 at 9:22
AveryLiuAveryLiu
17210
According to Doc for cross entropy loss, the weighted loss is calculated by multiplying the weight for each class and the original loss.
However, in the pytorch implementation, the class weight seems to have no effect on the final loss value unless it is set to zero. Following is the code:
from torch import nn
import torch
logits = torch.FloatTensor([
[0.1, 0.9],
])
label = torch.LongTensor([0])
criterion = nn.CrossEntropyLoss(weight=torch.FloatTensor([1, 1]))
loss = criterion(logits, label)
print(loss.item()) # result: 1.1711
# Change class weight for the first class to 0.1
criterion = nn.CrossEntropyLoss(weight=torch.FloatTensor([0.1, 1]))
loss = criterion(logits, label)
print(loss.item()) # result: 1.1711, should be 0.11711
# Change weight for first class to 0
criterion = nn.CrossEntropyLoss(weight=torch.FloatTensor([0, 1]))
loss = criterion(logits, label)
print(loss.item()) # result: 0
As illustrated in the code, the class weight seems to have no effect unless it is set to 0, this behavior contradicts to the documentation.
Updates
I implemented a version of weighted cross entropy which is in my eyes the "correct" way to do it.
import torch
from torch import nn
def weighted_cross_entropy(logits, label, weight=None):
assert len(logits.size()) == 2
batch_size, label_num = logits.size()
assert (batch_size == label.size(0))
if weight is None:
weight = torch.ones(label_num).float()
assert (label_num == weight.size(0))
x_terms = -torch.gather(logits, 1, label.unsqueeze(1)).squeeze()
log_terms = torch.log(torch.sum(torch.exp(logits), dim=1))
weights = torch.gather(weight, 0, label).float()
return torch.mean((x_terms+log_terms)*weights)
logits = torch.FloatTensor([
[0.1, 0.9],
[0.0, 0.1],
])
label = torch.LongTensor([0, 1])
neg_weight = 0.1
weight = torch.FloatTensor([neg_weight, 1])
criterion = nn.CrossEntropyLoss(weight=weight)
loss = criterion(logits, label)
print(loss.item()) # results: 0.69227
print(weighted_cross_entropy(logits, label, weight).item()) # results: 0.38075
What I did is to multiply each instance in the batch with its associated class weight. The result is still different from the original pytorch implementation, which makes me wonder how pytorch actually implement this.
machine-learning pytorch
machine-learning pytorch
asked Jan 19 at 9:22
AveryLiuAveryLiu
17210
asked Jan 19 at 9:22
AveryLiuAveryLiu
17210
edited Jan 19 at 11:58
AveryLiu
asked Jan 19 at 9:22
AveryLiuAveryLiu
17210
asked Jan 19 at 9:22
AveryLiuAveryLiu
17210
asked Jan 19 at 9:22
AveryLiuAveryLiu
17210
17210
2
discuss.pytorch.org/t/… Here is an explanation of how it works, cheers, sorry for confusion.
– Szymon Maszke
Jan 19 at 12:57
2
It turns out that changing the final line of theweighted_cross_entropytoreturn torch.sum((x_terms+log_terms)*weights)/torch.sum(weights)gives the correct behavior. Thanks for your efforts!
– AveryLiu
Jan 19 at 14:00
Though in the zero weight case the denominator would become zero, giving nan.
– AveryLiu
Jan 19 at 14:08
Implementation probably checks for this age case and returns zero.
– Szymon Maszke
Jan 19 at 14:11
yep, the pytorch implementation is correct. I commented on my own implementation.
– AveryLiu
Jan 19 at 14:13
add a comment |
2
discuss.pytorch.org/t/… Here is an explanation of how it works, cheers, sorry for confusion.
– Szymon Maszke
Jan 19 at 12:57
2
It turns out that changing the final line of theweighted_cross_entropytoreturn torch.sum((x_terms+log_terms)*weights)/torch.sum(weights)gives the correct behavior. Thanks for your efforts!
– AveryLiu
Jan 19 at 14:00
Though in the zero weight case the denominator would become zero, giving nan.
– AveryLiu
Jan 19 at 14:08
Implementation probably checks for this age case and returns zero.
– Szymon Maszke
Jan 19 at 14:11
yep, the pytorch implementation is correct. I commented on my own implementation.
– AveryLiu
Jan 19 at 14:13
2
2
discuss.pytorch.org/t/… Here is an explanation of how it works, cheers, sorry for confusion.
– Szymon Maszke
Jan 19 at 12:57
discuss.pytorch.org/t/… Here is an explanation of how it works, cheers, sorry for confusion.
– Szymon Maszke
Jan 19 at 12:57
2
2
It turns out that changing the final line of the
weighted_cross_entropy to return torch.sum((x_terms+log_terms)*weights)/torch.sum(weights) gives the correct behavior. Thanks for your efforts!– AveryLiu
Jan 19 at 14:00
It turns out that changing the final line of the
weighted_cross_entropy to return torch.sum((x_terms+log_terms)*weights)/torch.sum(weights) gives the correct behavior. Thanks for your efforts!– AveryLiu
Jan 19 at 14:00
Though in the zero weight case the denominator would become zero, giving nan.
– AveryLiu
Jan 19 at 14:08
Though in the zero weight case the denominator would become zero, giving nan.
– AveryLiu
Jan 19 at 14:08
Implementation probably checks for this age case and returns zero.
– Szymon Maszke
Jan 19 at 14:11
Implementation probably checks for this age case and returns zero.
– Szymon Maszke
Jan 19 at 14:11
yep, the pytorch implementation is correct. I commented on my own implementation.
– AveryLiu
Jan 19 at 14:13
yep, the pytorch implementation is correct. I commented on my own implementation.
– AveryLiu
Jan 19 at 14:13
add a comment |
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2
discuss.pytorch.org/t/… Here is an explanation of how it works, cheers, sorry for confusion.
– Szymon Maszke
Jan 19 at 12:57
2
It turns out that changing the final line of the
weighted_cross_entropytoreturn torch.sum((x_terms+log_terms)*weights)/torch.sum(weights)gives the correct behavior. Thanks for your efforts!– AveryLiu
Jan 19 at 14:00
Though in the zero weight case the denominator would become zero, giving nan.
– AveryLiu
Jan 19 at 14:08
Implementation probably checks for this age case and returns zero.
– Szymon Maszke
Jan 19 at 14:11
yep, the pytorch implementation is correct. I commented on my own implementation.
– AveryLiu
Jan 19 at 14:13