I’m following https://github.com/fastai/course-v3/blob/master/nbs/dl1/lesson3-imdb.ipynb. I’m using fastai 1.0.50.post1, Python 3.7.1. I got batch size error when I run lr_find.
ValueError: Expected input batch_size (102512) to match target batch_size (16).
Below is the related my scripts:
bs = 16
data_lm = TextLMDataBunch.from_csv(path, ‘all_texts.csv’, text_cols=1, label_cols=0, bs=bs)
data_lm.save(‘data_lm_export.pkl’)
data_lm = load_data(path, fname=‘data_clas_export.pkl’, bs = bs)
data_lm.vocab.itos[:20]
data_lm.train_ds[0][0]
learner = language_model_learner(data_lm, arch=AWD_LSTM, drop_mult=0.5)
learner.lr_find()
Then I got error
LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.
ValueError Traceback (most recent call last)
in
----> 1 learner.lr_find()
/home/application/anaconda/lib/python3.7/site-packages/fastai/train.py in lr_find(learn, start_lr, end_lr, num_it, stop_div, wd)
30 cb = LRFinder(learn, start_lr, end_lr, num_it, stop_div)
31 epochs = int(np.ceil(num_it/len(learn.data.train_dl)))
—> 32 learn.fit(epochs, start_lr, callbacks=[cb], wd=wd)
33
34 def to_fp16(learn:Learner, loss_scale:float=None, max_noskip:int=1000, dynamic:bool=False, clip:float=None,
/home/application/anaconda/lib/python3.7/site-packages/fastai/basic_train.py in fit(self, epochs, lr, wd, callbacks)
194 callbacks = [cb(self) for cb in self.callback_fns] + listify(callbacks)
195 if defaults.extra_callbacks is not None: callbacks += defaults.extra_callbacks
–> 196 fit(epochs, self, metrics=self.metrics, callbacks=self.callbacks+callbacks)
197
198 def create_opt(self, lr:Floats, wd:Floats=0.)->None:
/home/application/anaconda/lib/python3.7/site-packages/fastai/basic_train.py in fit(epochs, learn, callbacks, metrics)
98 for xb,yb in progress_bar(learn.data.train_dl, parent=pbar):
99 xb, yb = cb_handler.on_batch_begin(xb, yb)
–> 100 loss = loss_batch(learn.model, xb, yb, learn.loss_func, learn.opt, cb_handler)
101 if cb_handler.on_batch_end(loss): break
102
/home/application/anaconda/lib/python3.7/site-packages/fastai/basic_train.py in loss_batch(model, xb, yb, loss_func, opt, cb_handler)
27
28 if not loss_func: return to_detach(out), yb[0].detach()
—> 29 loss = loss_func(out, *yb)
30
31 if opt is not None:
/home/application/anaconda/lib/python3.7/site-packages/fastai/layers.py in call(self, input, target, **kwargs)
243 if self.floatify: target = target.float()
244 input = input.view(-1,input.shape[-1]) if self.is_2d else input.view(-1)
–> 245 return self.func.call(input, target.view(-1), **kwargs)
246
247 def CrossEntropyFlat(*args, axis:int=-1, **kwargs):
/home/application/anaconda/lib/python3.7/site-packages/torch/nn/modules/module.py in call(self, *input, **kwargs)
487 result = self._slow_forward(*input, **kwargs)
488 else:
–> 489 result = self.forward(*input, **kwargs)
490 for hook in self._forward_hooks.values():
491 hook_result = hook(self, input, result)
/home/application/anaconda/lib/python3.7/site-packages/torch/nn/modules/loss.py in forward(self, input, target)
902 def forward(self, input, target):
903 return F.cross_entropy(input, target, weight=self.weight,
–> 904 ignore_index=self.ignore_index, reduction=self.reduction)
905
906
/home/application/anaconda/lib/python3.7/site-packages/torch/nn/functional.py in cross_entropy(input, target, weight, size_average, ignore_index, reduce, reduction)
1968 if size_average is not None or reduce is not None:
1969 reduction = _Reduction.legacy_get_string(size_average, reduce)
-> 1970 return nll_loss(log_softmax(input, 1), target, weight, None, ignore_index, None, reduction)
1971
1972
/home/application/anaconda/lib/python3.7/site-packages/torch/nn/functional.py in nll_loss(input, target, weight, size_average, ignore_index, reduce, reduction)
1786 if input.size(0) != target.size(0):
1787 raise ValueError(‘Expected input batch_size ({}) to match target batch_size ({}).’
-> 1788 .format(input.size(0), target.size(0)))
1789 if dim == 2:
1790 ret = torch._C._nn.nll_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
ValueError: Expected input batch_size (102512) to match target batch_size (16).