---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In [17], 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:54, in BaseLoss.__call__(self, inp, targ, **kwargs)
     52 if targ.dtype in [torch.int8, torch.int16, torch.int32]: targ = targ.long()
     53 if self.flatten: inp = inp.view(-1,inp.shape[-1]) if self.is_2d else inp.view(-1)
---> 54 return self.func.__call__(inp, targ.view(-1) if self.flatten else targ, **kwargs)

File c:\Users\teamt\miniconda3\envs\fastai\lib\site-packages\torch\nn\modules\module.py:1130, in Module._call_impl(self, *input, **kwargs)
   1126 # If we don't have any hooks, we want to skip the rest of the logic in
   1127 # this function, and just call forward.
   1128 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1129         or _global_forward_hooks or _global_forward_pre_hooks):
-> 1130     return forward_call(*input, **kwargs)
   1131 # Do not call functions when jit is used
   1132 full_backward_hooks, non_full_backward_hooks = [], []

File c:\Users\teamt\miniconda3\envs\fastai\lib\site-packages\torch\nn\modules\loss.py:1164, in CrossEntropyLoss.forward(self, input, target)
   1163 def forward(self, input: Tensor, target: Tensor) -> Tensor:
-> 1164     return F.cross_entropy(input, target, weight=self.weight,
   1165                            ignore_index=self.ignore_index, reduction=self.reduction,
   1166                            label_smoothing=self.label_smoothing)

File c:\Users\teamt\miniconda3\envs\fastai\lib\site-packages\torch\nn\functional.py:3000, in cross_entropy(input, target, weight, size_average, ignore_index, reduce, reduction, label_smoothing)
   2934 r"""This criterion computes the cross entropy loss between input and target.
   2935
   2936 See :class:`~torch.nn.CrossEntropyLoss` for details.
   (...)
   2997     >>> loss.backward()
   2998 """
   2999 if has_torch_function_variadic(input, target, weight):
-> 3000     return handle_torch_function(
   3001         cross_entropy,
   3002         (input, target, weight),
   3003         input,
   3004         target,
   3005         weight=weight,
   3006         size_average=size_average,
   3007         ignore_index=ignore_index,
   3008         reduce=reduce,
   3009         reduction=reduction,
   3010         label_smoothing=label_smoothing,
   3011     )
   3012 if size_average is not None or reduce is not None:
   3013     reduction = _Reduction.legacy_get_string(size_average, reduce)

File c:\Users\teamt\miniconda3\envs\fastai\lib\site-packages\torch\overrides.py:1498, in handle_torch_function(public_api, relevant_args, *args, **kwargs)
   1492     warnings.warn("Defining your `__torch_function__ as a plain method is deprecated and "
   1493                   "will be an error in future, please define it as a classmethod.",
   1494                   DeprecationWarning)
   1496 # Use `public_api` instead of `implementation` so __torch_function__
   1497 # implementations can do equality/identity comparisons.
-> 1498 result = torch_func_method(public_api, types, args, kwargs)
   1500 if result is not NotImplemented:
   1501     return result

File c:\Users\teamt\miniconda3\envs\fastai\lib\site-packages\fastai\torch_core.py:376, in TensorBase.__torch_function__(cls, func, types, args, kwargs)
    374 if cls.debug and func.__name__ not in ('__str__','__repr__'): print(func, types, args, kwargs)
    375 if _torch_handled(args, cls._opt, func): types = (torch.Tensor,)
--> 376 res = super().__torch_function__(func, types, args, ifnone(kwargs, {}))
    377 dict_objs = _find_args(args) if args else _find_args(list(kwargs.values()))
    378 if issubclass(type(res),TensorBase) and dict_objs: res.set_meta(dict_objs[0],as_copy=True)

File c:\Users\teamt\miniconda3\envs\fastai\lib\site-packages\torch\_tensor.py:1121, in Tensor.__torch_function__(cls, func, types, args, kwargs)
   1118     return NotImplemented
   1120 with _C.DisableTorchFunction():
-> 1121     ret = func(*args, **kwargs)
   1122     if func in get_default_nowrap_functions():
   1123         return ret

File c:\Users\teamt\miniconda3\envs\fastai\lib\site-packages\torch\nn\functional.py:3014, in cross_entropy(input, target, weight, size_average, ignore_index, reduce, reduction, label_smoothing)
   3012 if size_average is not None or reduce is not None:
   3013     reduction = _Reduction.legacy_get_string(size_average, reduce)
-> 3014 return torch._C._nn.cross_entropy_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index, label_smoothing)

ValueError: Expected input batch_size (0) to match target batch_size (8).