---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
Cell In [19], line 1
----> 1 learn.fit(10, lr=0.1)

File c:\Users\teamt\miniconda3\envs\fastai\lib\site-packages\fastai\learner.py:256, in Learner.fit(self, n_epoch, lr, wd, cbs, reset_opt, start_epoch)
    254 self.opt.set_hypers(lr=self.lr if lr is None else lr)
    255 self.n_epoch = n_epoch
--> 256 self._with_events(self._do_fit, 'fit', CancelFitException, self._end_cleanup)

File c:\Users\teamt\miniconda3\envs\fastai\lib\site-packages\fastai\learner.py:193, in Learner._with_events(self, f, event_type, ex, final)
    192 def _with_events(self, f, event_type, ex, final=noop):
--> 193     try: self(f'before_{event_type}');  f()
    194     except ex: self(f'after_cancel_{event_type}')
    195     self(f'after_{event_type}');  final()

File c:\Users\teamt\miniconda3\envs\fastai\lib\site-packages\fastai\learner.py:245, in Learner._do_fit(self)
    243 for epoch in range(self.n_epoch):
    244     self.epoch=epoch
--> 245     self._with_events(self._do_epoch, 'epoch', CancelEpochException)

File c:\Users\teamt\miniconda3\envs\fastai\lib\site-packages\fastai\learner.py:193, in Learner._with_events(self, f, event_type, ex, final)
    192 def _with_events(self, f, event_type, ex, final=noop):
--> 193     try: self(f'before_{event_type}');  f()
    194     except ex: self(f'after_cancel_{event_type}')
    195     self(f'after_{event_type}');  final()

File c:\Users\teamt\miniconda3\envs\fastai\lib\site-packages\fastai\learner.py:239, in Learner._do_epoch(self)
    238 def _do_epoch(self):
--> 239     self._do_epoch_train()
    240     self._do_epoch_validate()

File c:\Users\teamt\miniconda3\envs\fastai\lib\site-packages\fastai\learner.py:231, in Learner._do_epoch_train(self)
    229 def _do_epoch_train(self):
    230     self.dl = self.dls.train
--> 231     self._with_events(self.all_batches, 'train', CancelTrainException)

File c:\Users\teamt\miniconda3\envs\fastai\lib\site-packages\fastai\learner.py:193, in Learner._with_events(self, f, event_type, ex, final)
    192 def _with_events(self, f, event_type, ex, final=noop):
--> 193     try: self(f'before_{event_type}');  f()
    194     except ex: self(f'after_cancel_{event_type}')
    195     self(f'after_{event_type}');  final()

File c:\Users\teamt\miniconda3\envs\fastai\lib\site-packages\fastai\learner.py:199, in Learner.all_batches(self)
    197 def all_batches(self):
    198     self.n_iter = len(self.dl)
--> 199     for o in enumerate(self.dl): self.one_batch(*o)

File c:\Users\teamt\miniconda3\envs\fastai\lib\site-packages\fastai\learner.py:227, in Learner.one_batch(self, i, b)
    225 b = self._set_device(b)
    226 self._split(b)
--> 227 self._with_events(self._do_one_batch, 'batch', CancelBatchException)

File c:\Users\teamt\miniconda3\envs\fastai\lib\site-packages\fastai\learner.py:193, in Learner._with_events(self, f, event_type, ex, final)
    192 def _with_events(self, f, event_type, ex, final=noop):
--> 193     try: self(f'before_{event_type}');  f()
    194     except ex: self(f'after_cancel_{event_type}')
    195     self(f'after_{event_type}');  final()

File c:\Users\teamt\miniconda3\envs\fastai\lib\site-packages\fastai\learner.py:208, in Learner._do_one_batch(self)
    206 self('after_pred')
    207 if len(self.yb):
--> 208     self.loss_grad = self.loss_func(self.pred, *self.yb)
    209     self.loss = self.loss_grad.clone()
    210 self('after_loss')

File c:\Users\teamt\miniconda3\envs\fastai\lib\site-packages\fastai\losses.py:71, in CrossEntropyLossFlat.__init__(self, axis, *args, **kwargs)
     65 @use_kwargs_dict(keep=True, weight=None, ignore_index=-100, reduction='mean')
     66 def __init__(self,
     67     *args,
     68     axis:int=-1, # Class axis
     69     **kwargs
     70 ):
---> 71     super().__init__(nn.CrossEntropyLoss, *args, axis=axis, **kwargs)

File c:\Users\teamt\miniconda3\envs\fastai\lib\site-packages\fastai\losses.py:28, in BaseLoss.__init__(self, loss_cls, axis, flatten, floatify, is_2d, *args, **kwargs)
     18 def __init__(self,
     19     loss_cls, # Uninitialized PyTorch-compatible loss
     20     *args,
   (...)
     25     **kwargs
     26 ):
     27     store_attr("axis,flatten,floatify,is_2d")
---> 28     self.func = loss_cls(*args,**kwargs)
     29     functools.update_wrapper(self, self.func)

File c:\Users\teamt\miniconda3\envs\fastai\lib\site-packages\torch\nn\modules\loss.py:1159, in CrossEntropyLoss.__init__(self, weight, size_average, ignore_index, reduce, reduction, label_smoothing)
   1157 def __init__(self, weight: Optional[Tensor] = None, size_average=None, ignore_index: int = -100,
   1158              reduce=None, reduction: str = 'mean', label_smoothing: float = 0.0) -> None:
-> 1159     super(CrossEntropyLoss, self).__init__(weight, size_average, reduce, reduction)
   1160     self.ignore_index = ignore_index
   1161     self.label_smoothing = label_smoothing

File c:\Users\teamt\miniconda3\envs\fastai\lib\site-packages\torch\nn\modules\loss.py:25, in _WeightedLoss.__init__(self, weight, size_average, reduce, reduction)
     24 def __init__(self, weight: Optional[Tensor] = None, size_average=None, reduce=None, reduction: str = 'mean') -> None:
---> 25     super(_WeightedLoss, self).__init__(size_average, reduce, reduction)
     26     self.register_buffer('weight', weight)
     27     self.weight: Optional[Tensor]

File c:\Users\teamt\miniconda3\envs\fastai\lib\site-packages\torch\nn\modules\loss.py:18, in _Loss.__init__(self, size_average, reduce, reduction)
     16 super(_Loss, self).__init__()
     17 if size_average is not None or reduce is not None:
---> 18     self.reduction: str = _Reduction.legacy_get_string(size_average, reduce)
     19 else:
     20     self.reduction = reduction

File c:\Users\teamt\miniconda3\envs\fastai\lib\site-packages\torch\nn\_reduction.py:35, in legacy_get_string(size_average, reduce, emit_warning)
     32 if reduce is None:
     33     reduce = True
---> 35 if size_average and reduce:
     36     ret = 'mean'
     37 elif reduce:

RuntimeError: Boolean value of Tensor with more than one value is ambiguous