I tried training the classifier bit of ULMfit for a regression task by changing c=1, and changing the loss criterion, but I still get errors stemming from the accuracy metric function called from model.py.

Is there something I am overlooking, or does new code need to be written for regression tasks?

I am happy to submit a PR and write some code if we want to build a get_rnn_regression function.

Background I am trying to calculate political ideology (a scale that can be normalized from 0-1) from a transcript.

For reference here is the traceback:

TypeError Traceback (most recent call last)

in ()

----> 1 learn.fit(lrs, 1, wds=wd, cycle_len=1, use_clr=(8,3))

```
/home/ly501/anaconda3/lib/python3.6/site-packages/fastai/learner.py in fit(self, lrs, n_cycle, wds, **kwargs)
285 self.sched = None
286 layer_opt = self.get_layer_opt(lrs, wds)
--> 287 return self.fit_gen(self.model, self.data, layer_opt, n_cycle, **kwargs)
288
289 def warm_up(self, lr, wds=None):
/home/ly501/anaconda3/lib/python3.6/site-packages/fastai/learner.py in fit_gen(self, model, data, layer_opt, n_cycle, cycle_len, cycle_mult, cycle_save_name, best_save_name, use_clr, use_clr_beta, metrics, callbacks, use_wd_sched, norm_wds, wds_sched_mult, use_swa, swa_start, swa_eval_freq, **kwargs)
232 metrics=metrics, callbacks=callbacks, reg_fn=self.reg_fn, clip=self.clip, fp16=self.fp16,
233 swa_model=self.swa_model if use_swa else None, swa_start=swa_start,
--> 234 swa_eval_freq=swa_eval_freq, **kwargs)
235
236 def get_layer_groups(self): return self.models.get_layer_groups()
/home/ly501/anaconda3/lib/python3.6/site-packages/fastai/model.py in fit(model, data, n_epochs, opt, crit, metrics, callbacks, stepper, swa_model, swa_start, swa_eval_freq, **kwargs)
148
149 if not all_val:
--> 150 vals = validate(model_stepper, cur_data.val_dl, metrics)
151 stop=False
152 for cb in callbacks: stop = stop or cb.on_epoch_end(vals)
/home/ly501/anaconda3/lib/python3.6/site-packages/fastai/model.py in validate(stepper, dl, metrics)
209 else: batch_cnts.append(len(x))
210 loss.append(to_np(l))
--> 211 res.append([f(preds.data, y) for f in metrics])
212 return [np.average(loss, 0, weights=batch_cnts)] + list(np.average(np.stack(res), 0, weights=batch_cnts))
213
/home/ly501/anaconda3/lib/python3.6/site-packages/fastai/model.py in <listcomp>(.0)
209 else: batch_cnts.append(len(x))
210 loss.append(to_np(l))
--> 211 res.append([f(preds.data, y) for f in metrics])
212 return [np.average(loss, 0, weights=batch_cnts)] + list(np.average(np.stack(res), 0, weights=batch_cnts))
213
/home/ly501/anaconda3/lib/python3.6/site-packages/fastai/metrics.py in accuracy(preds, targs)
8 def accuracy(preds, targs):
9 preds = torch.max(preds, dim=1)[1]
---> 10 return (preds==targs).float().mean()
11
12 def accuracy_thresh(thresh):
/home/ly501/anaconda3/lib/python3.6/site-packages/torch/tensor.py in __eq__(self, other)
358
359 def __eq__(self, other):
--> 360 return self.eq(other)
361
362 def __ne__(self, other):
TypeError: eq received an invalid combination of arguments - got (torch.cuda.FloatTensor), but expected one of:
* (int value)
didn't match because some of the arguments have invalid types: (!torch.cuda.FloatTensor!)
* (torch.cuda.LongTensor other)
didn't match because some of the arguments have invalid types: (!torch.cuda.FloatTensor!)
```