For classification of an imbalanced image dataset I tried to apply weights and use the `CrossEntropy`

loss function:

```
_, class_counts = np.unique(data.y.items, return_counts=True)
weights = np.sum(class_counts)/class_counts
weights = tensor(weights).float().cuda()
learn.loss_func = CrossEntropyFlat(weight=weights)
```

After the above, the learn class can be used to `fit`

like normally. The problem is running the

`ClassificationInterpretation`

. This throws an error.

`RuntimeError: Expected object of backend CPU but got backend CUDA for argument #3 'weight'`

It seems the ClassificationInterpretation can’t handle the weights passed into the CrossEntropy loss function???

This is `a pity`

because I’d like to see the change in the confusion matrix after applying weights.