Hi all, I’m trying to use a weighted cross-entropy loss for my text classifier with 5 categories to predict, but I get a TypeError: unsupported operand type(s) for +=: ‘TensorCategory’ and ‘TensorText’.
Any ideas?
text_learner = text_classifier_learner(dls_clas, AWD_LSTM,
drop_mult=0.5)
Where my dls_clas.c == 5
import torch.nn
sum_counts = 198000
counts = np.array([ 6299, 6895, 15973, 39130, 129703])
weights = counts/sum_counts
weights = torch.FloatTensor(weights).cuda()
learn.loss_func = nn.CrossEntropyLoss(weight=weights)
learn.fit_one_cycle(1, 0.01)
Full error:
TypeError Traceback (most recent call last)
in
1 ############################################## CHANGING IT UP ###################################
----> 2 learn.fit_one_cycle(1, 0.01)
/opt/conda/envs/fastai/lib/python3.8/site-packages/fastai/callback/schedule.py in fit_one_cycle(self, n_epoch, lr_max, div, div_final, pct_start, wd, moms, cbs, reset_opt)
110 scheds = {‘lr’: combined_cos(pct_start, lr_max/div, lr_max, lr_max/div_final),
111 ‘mom’: combined_cos(pct_start, *(self.moms if moms is None else moms))}
–> 112 self.fit(n_epoch, cbs=ParamScheduler(scheds)+L(cbs), reset_opt=reset_opt, wd=wd)
113
114 # Cell
/opt/conda/envs/fastai/lib/python3.8/site-packages/fastai/learner.py in fit(self, n_epoch, lr, wd, cbs, reset_opt)
203 self.opt.set_hypers(lr=self.lr if lr is None else lr)
204 self.n_epoch = n_epoch
–> 205 self._with_events(self._do_fit, ‘fit’, CancelFitException, self._end_cleanup)
206
207 def _end_cleanup(self): self.dl,self.xb,self.yb,self.pred,self.loss = None,(None,),(None,),None,None
/opt/conda/envs/fastai/lib/python3.8/site-packages/fastai/learner.py in with_events(self, f, event_type, ex, final)
152
153 def with_events(self, f, event_type, ex, final=noop):
–> 154 try: self(f’before{event_type}’) ;f()
155 except ex: self(f’after_cancel{event_type}’)
156 finally: self(f’after_{event_type}’) ;final()
/opt/conda/envs/fastai/lib/python3.8/site-packages/fastai/learner.py in _do_fit(self)
194 for epoch in range(self.n_epoch):
195 self.epoch=epoch
–> 196 self._with_events(self._do_epoch, ‘epoch’, CancelEpochException)
197
198 def fit(self, n_epoch, lr=None, wd=None, cbs=None, reset_opt=False):
/opt/conda/envs/fastai/lib/python3.8/site-packages/fastai/learner.py in with_events(self, f, event_type, ex, final)
152
153 def with_events(self, f, event_type, ex, final=noop):
–> 154 try: self(f’before{event_type}’) ;f()
155 except ex: self(f’after_cancel{event_type}’)
156 finally: self(f’after_{event_type}’) ;final()
/opt/conda/envs/fastai/lib/python3.8/site-packages/fastai/learner.py in _do_epoch(self)
188
189 def _do_epoch(self):
–> 190 self._do_epoch_train()
191 self._do_epoch_validate()
192
/opt/conda/envs/fastai/lib/python3.8/site-packages/fastai/learner.py in _do_epoch_train(self)
180 def _do_epoch_train(self):
181 self.dl = self.dls.train
–> 182 self._with_events(self.all_batches, ‘train’, CancelTrainException)
183
184 def _do_epoch_validate(self, ds_idx=1, dl=None):
/opt/conda/envs/fastai/lib/python3.8/site-packages/fastai/learner.py in with_events(self, f, event_type, ex, final)
152
153 def with_events(self, f, event_type, ex, final=noop):
–> 154 try: self(f’before{event_type}’) ;f()
155 except ex: self(f’after_cancel{event_type}’)
156 finally: self(f’after_{event_type}’) ;final()
/opt/conda/envs/fastai/lib/python3.8/site-packages/fastai/learner.py in all_batches(self)
158 def all_batches(self):
159 self.n_iter = len(self.dl)
–> 160 for o in enumerate(self.dl): self.one_batch(*o)
161
162 def _do_one_batch(self):
/opt/conda/envs/fastai/lib/python3.8/site-packages/fastai/learner.py in one_batch(self, i, b)
176 self.iter = i
177 self._split(b)
–> 178 self._with_events(self._do_one_batch, ‘batch’, CancelBatchException)
179
180 def _do_epoch_train(self):
/opt/conda/envs/fastai/lib/python3.8/site-packages/fastai/learner.py in with_events(self, f, event_type, ex, final)
152
153 def with_events(self, f, event_type, ex, final=noop):
–> 154 try: self(f’before{event_type}’) ;f()
155 except ex: self(f’after_cancel{event_type}’)
156 finally: self(f’after_{event_type}’) ;final()
/opt/conda/envs/fastai/lib/python3.8/site-packages/fastai/learner.py in _do_one_batch(self)
164 self(‘after_pred’)
165 if len(self.yb): self.loss = self.loss_func(self.pred, *self.yb)
–> 166 self(‘after_loss’)
167 if not self.training or not len(self.yb): return
168 self(‘before_backward’)
/opt/conda/envs/fastai/lib/python3.8/site-packages/fastai/learner.py in call(self, event_name)
130 def ordered_cbs(self, event): return [cb for cb in sort_by_run(self.cbs) if hasattr(cb, event)]
131
–> 132 def call(self, event_name): L(event_name).map(self._call_one)
133
134 def _call_one(self, event_name):
/opt/conda/envs/fastai/lib/python3.8/site-packages/fastcore/foundation.py in map(self, f, gen, *args, **kwargs)
177 def range(cls, a, b=None, step=None): return cls(range_of(a, b=b, step=step))
178
–> 179 def map(self, f, *args, gen=False, **kwargs): return self._new(map_ex(self, f, *args, gen=gen, **kwargs))
180 def argwhere(self, f, negate=False, **kwargs): return self._new(argwhere(self, f, negate, **kwargs))
181 def filter(self, f=noop, negate=False, gen=False, **kwargs):
/opt/conda/envs/fastai/lib/python3.8/site-packages/fastcore/basics.py in map_ex(iterable, f, gen, *args, **kwargs)
605 res = map(g, iterable)
606 if gen: return res
–> 607 return list(res)
608
609 # Cell
/opt/conda/envs/fastai/lib/python3.8/site-packages/fastcore/basics.py in call(self, *args, **kwargs)
595 if isinstance(v,_Arg): kwargs[k] = args.pop(v.i)
596 fargs = [args[x.i] if isinstance(x, _Arg) else x for x in self.pargs] + args[self.maxi+1:]
–> 597 return self.func(*fargs, **kwargs)
598
599 # Cell
/opt/conda/envs/fastai/lib/python3.8/site-packages/fastai/learner.py in _call_one(self, event_name)
134 def _call_one(self, event_name):
135 assert hasattr(event, event_name), event_name
–> 136 [cb(event_name) for cb in sort_by_run(self.cbs)]
137
138 def _bn_bias_state(self, with_bias): return norm_bias_params(self.model, with_bias).map(self.opt.state)
/opt/conda/envs/fastai/lib/python3.8/site-packages/fastai/learner.py in (.0)
134 def _call_one(self, event_name):
135 assert hasattr(event, event_name), event_name
–> 136 [cb(event_name) for cb in sort_by_run(self.cbs)]
137
138 def _bn_bias_state(self, with_bias): return norm_bias_params(self.model, with_bias).map(self.opt.state)
/opt/conda/envs/fastai/lib/python3.8/site-packages/fastai/callback/core.py in call(self, event_name)
42 (self.run_valid and not getattr(self, ‘training’, False)))
43 res = None
—> 44 if self.run and _run: res = getattr(self, event_name, noop)()
45 if event_name==‘after_fit’: self.run=True #Reset self.run to True at each end of fit
46 return res
/opt/conda/envs/fastai/lib/python3.8/site-packages/fastai/callback/rnn.py in after_loss(self)
31 def after_loss(self):
32 if not self.training: return
—> 33 if self.alpha != 0.: self.learn.loss += self.alpha * self.out.float().pow(2).mean()
34 if self.beta != 0.:
35 h = self.raw_out
TypeError: unsupported operand type(s) for +=: ‘TensorCategory’ and ‘TensorText’
