Error while using fbeta metric

(Mayank) #1

I am working on tabular data for multi class classification. Using metrics = accuracy is showing no error but using metrics = fbeta is showing this error.

---------------------------------------------------------------------------

RuntimeError                              Traceback (most recent call last)

<ipython-input-66-2a714d47a7ed> in <module>()
----> 1 learn.fit_one_cycle(2, 1e-2, wd=0.2)

/usr/local/lib/python3.6/dist-packages/fastai/train.py in fit_one_cycle(learn, cyc_len, max_lr, moms, div_factor, pct_start, wd, callbacks, **kwargs)
     20     callbacks.append(OneCycleScheduler(learn, max_lr, moms=moms, div_factor=div_factor,
     21                                         pct_start=pct_start, **kwargs))
---> 22     learn.fit(cyc_len, max_lr, wd=wd, callbacks=callbacks)
     23 
     24 def lr_find(learn:Learner, start_lr:Floats=1e-7, end_lr:Floats=10, num_it:int=100, stop_div:bool=True, **kwargs:Any):

/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py in fit(self, epochs, lr, wd, callbacks)
    170         callbacks = [cb(self) for cb in self.callback_fns] + listify(callbacks)
    171         fit(epochs, self.model, self.loss_func, opt=self.opt, data=self.data, metrics=self.metrics,
--> 172             callbacks=self.callbacks+callbacks)
    173 
    174     def create_opt(self, lr:Floats, wd:Floats=0.)->None:

/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py in fit(epochs, model, loss_func, opt, data, callbacks, metrics)
     92     except Exception as e:
     93         exception = e
---> 94         raise e
     95     finally: cb_handler.on_train_end(exception)
     96 

/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py in fit(epochs, model, loss_func, opt, data, callbacks, metrics)
     87             if not data.empty_val:
     88                 val_loss = validate(model, data.valid_dl, loss_func=loss_func,
---> 89                                        cb_handler=cb_handler, pbar=pbar)
     90             else: val_loss=None
     91             if cb_handler.on_epoch_end(val_loss): break

/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py in validate(model, dl, loss_func, cb_handler, pbar, average, n_batch)
     52             if not is_listy(yb): yb = [yb]
     53             nums.append(yb[0].shape[0])
---> 54             if cb_handler and cb_handler.on_batch_end(val_losses[-1]): break
     55             if n_batch and (len(nums)>=n_batch): break
     56         nums = np.array(nums, dtype=np.float32)

/usr/local/lib/python3.6/dist-packages/fastai/callback.py in on_batch_end(self, loss)
    237         "Handle end of processing one batch with `loss`."
    238         self.state_dict['last_loss'] = loss
--> 239         stop = np.any(self('batch_end', not self.state_dict['train']))
    240         if self.state_dict['train']:
    241             self.state_dict['iteration'] += 1

/usr/local/lib/python3.6/dist-packages/fastai/callback.py in __call__(self, cb_name, call_mets, **kwargs)
    185     def __call__(self, cb_name, call_mets=True, **kwargs)->None:
    186         "Call through to all of the `CallbakHandler` functions."
--> 187         if call_mets: [getattr(met, f'on_{cb_name}')(**self.state_dict, **kwargs) for met in self.metrics]
    188         return [getattr(cb, f'on_{cb_name}')(**self.state_dict, **kwargs) for cb in self.callbacks]
    189 

/usr/local/lib/python3.6/dist-packages/fastai/callback.py in <listcomp>(.0)
    185     def __call__(self, cb_name, call_mets=True, **kwargs)->None:
    186         "Call through to all of the `CallbakHandler` functions."
--> 187         if call_mets: [getattr(met, f'on_{cb_name}')(**self.state_dict, **kwargs) for met in self.metrics]
    188         return [getattr(cb, f'on_{cb_name}')(**self.state_dict, **kwargs) for cb in self.callbacks]
    189 

/usr/local/lib/python3.6/dist-packages/fastai/callback.py in on_batch_end(self, last_output, last_target, **kwargs)
    272         if not is_listy(last_target): last_target=[last_target]
    273         self.count += last_target[0].size(0)
--> 274         self.val += last_target[0].size(0) * self.func(last_output, *last_target).detach().cpu()
    275 
    276     def on_epoch_end(self, **kwargs):

/usr/local/lib/python3.6/dist-packages/fastai/metrics.py in fbeta(y_pred, y_true, thresh, beta, eps, sigmoid)
     16     y_pred = (y_pred>thresh).float()
     17     y_true = y_true.float()
---> 18     TP = (y_pred*y_true).sum(dim=1)
     19     prec = TP/(y_pred.sum(dim=1)+eps)
     20     rec = TP/(y_true.sum(dim=1)+eps)

RuntimeError: The size of tensor a (5) must match the size of tensor b (64) at non-singleton dimension 1
1 Like

(Swati) #2

i am also facing similar issues , were you able to solve this problem. any guidance will be really helpful.

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(Jeremy Easterbrook) #3

Also getting similar errors, wonder if maybe Sylvain could help on this ?

Linked issue

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(Mayank) #4

Couldn’t find any solution till now.

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