I figured it out… Answering my own question here in case someone else runs into the same issue.

`torch.nn.CrossEntropyLoss`

applies a softmax to the raw prediction output of the `learn.model`

method.

For example:

I first get the output of the model. In this case i’m just pulling the first batch. To compute the loss for the entire validation set I would need to loop through the entire validation set. For this example one batch should suffice.

```
x, target = next(iter(learn.data.valid_dl))
preds = learn.model(x)
preds
tensor([[ 0.6294, 0.5137, 2.7246, ..., -2.0160, 0.4596, -1.2756],
[-0.0355, -2.4116, 14.5470, ..., -0.5973, -2.3760, -1.9473],
[-3.0890, -2.2336, 3.6774, ..., -2.2486, -1.3904, 0.2988],
...,
[-2.3504, 0.5370, -0.1560, ..., 1.7313, -3.0057, -2.0379],
[-2.2719, -0.1256, -3.6734, ..., 12.2523, -0.1865, 0.5820],
[-2.9343, -1.9531, 0.4142, ..., 2.2272, -0.3494, -3.1263]],
device='cuda:0', grad_fn=<AddmmBackward>)
```

Now calculate the torch `CrossEntropyLoss`

```
loss = nn.CrossEntropyLoss(reduction='mean')
output = loss(preds, target)
output.item()
0.3030180037021637
```

Recomputing using scikit learn and scipy. The softmax function turns the numeric output of the neural network into probabilities.

```
from scipy.special import softmax
from sklearn.metrics import log_loss
log_loss(target.cpu().numpy(),
softmax(preds.cpu().detach().numpy()),
labels = list(range(0,37)))
0.3030180556320232
```