Is there a guide for how to play with the defaults? I want to try different parameters to cnn_learner and see what happens, but I’m not having much luck.
Here is some base code that the course provides:
path = untar_data(URLs.IMAGENETTE_160)
dataloaders = ImageDataLoaders.from_folder(
path,
valid='val',
item_tfms=RandomResizedCrop(128, min_scale=0.35),
batch_tfms=aug_transforms()
)
learner = cnn_learner(
dls=dataloaders,
arch=resnet34,
metrics=error_rate
)
Then I try to modify loss_func like this:
learner = cnn_learner(
dls=dataloaders,
arch=resnet34,
# opt_func=Adam(),
loss_func=BCEWithLogitsLossFlat(),
metrics=error_rate
)
and I get an error like this:
ValueError Traceback (most recent call last)
<ipython-input-245-a1d02bbd6880> in <module>
----> 1 learner.fine_tune(1)
~/miniconda3/envs/fastai/lib/python3.8/site-packages/fastai/callback/schedule.py in fine_tune(self, epochs, base_lr, freeze_epochs, lr_mult, pct_start, div, **kwargs)
155 "Fine tune with `freeze` for `freeze_epochs` then with `unfreeze` from `epochs` using discriminative LR"
156 self.freeze()
--> 157 self.fit_one_cycle(freeze_epochs, slice(base_lr), pct_start=0.99, **kwargs)
158 base_lr /= 2
159 self.unfreeze()
~/miniconda3/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
~/miniconda3/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
~/miniconda3/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()
~/miniconda3/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):
~/miniconda3/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()
~/miniconda3/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
~/miniconda3/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):
~/miniconda3/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()
~/miniconda3/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):
~/miniconda3/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):
~/miniconda3/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()
~/miniconda3/envs/fastai/lib/python3.8/site-packages/fastai/learner.py in _do_one_batch(self)
163 self.pred = self.model(*self.xb)
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
~/miniconda3/envs/fastai/lib/python3.8/site-packages/fastai/losses.py in __call__(self, inp, targ, **kwargs)
31 if targ.dtype in [torch.int8, torch.int16, torch.int32]: targ = targ.long()
32 if self.flatten: inp = inp.view(-1,inp.shape[-1]) if self.is_2d else inp.view(-1)
---> 33 return self.func.__call__(inp, targ.view(-1) if self.flatten else targ, **kwargs)
34
35 # Cell
~/miniconda3/envs/fastai/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
725 result = self._slow_forward(*input, **kwargs)
726 else:
--> 727 result = self.forward(*input, **kwargs)
728 for hook in itertools.chain(
729 _global_forward_hooks.values(),
~/miniconda3/envs/fastai/lib/python3.8/site-packages/torch/nn/modules/loss.py in forward(self, input, target)
627
628 def forward(self, input: Tensor, target: Tensor) -> Tensor:
--> 629 return F.binary_cross_entropy_with_logits(input, target,
630 self.weight,
631 pos_weight=self.pos_weight,
~/miniconda3/envs/fastai/lib/python3.8/site-packages/torch/nn/functional.py in binary_cross_entropy_with_logits(input, target, weight, size_average, reduce, reduction, pos_weight)
2568 tens_ops = (input, target)
2569 if any([type(t) is not Tensor for t in tens_ops]) and has_torch_function(tens_ops):
-> 2570 return handle_torch_function(
2571 binary_cross_entropy_with_logits, tens_ops, input, target, weight=weight,
2572 size_average=size_average, reduce=reduce, reduction=reduction,
~/miniconda3/envs/fastai/lib/python3.8/site-packages/torch/overrides.py in handle_torch_function(public_api, relevant_args, *args, **kwargs)
1061 # Use `public_api` instead of `implementation` so __torch_function__
1062 # implementations can do equality/identity comparisons.
-> 1063 result = overloaded_arg.__torch_function__(public_api, types, args, kwargs)
1064
1065 if result is not NotImplemented:
~/miniconda3/envs/fastai/lib/python3.8/site-packages/fastai/torch_core.py in __torch_function__(self, func, types, args, kwargs)
317 # if func.__name__[0]!='_': print(func, types, args, kwargs)
318 # with torch._C.DisableTorchFunction(): ret = _convert(func(*args, **(kwargs or {})), self.__class__)
--> 319 ret = super().__torch_function__(func, types, args=args, kwargs=kwargs)
320 if isinstance(ret, TensorBase): ret.set_meta(self, as_copy=True)
321 return ret
~/miniconda3/envs/fastai/lib/python3.8/site-packages/torch/tensor.py in __torch_function__(cls, func, types, args, kwargs)
993
994 with _C.DisableTorchFunction():
--> 995 ret = func(*args, **kwargs)
996 return _convert(ret, cls)
997
~/miniconda3/envs/fastai/lib/python3.8/site-packages/torch/nn/functional.py in binary_cross_entropy_with_logits(input, target, weight, size_average, reduce, reduction, pos_weight)
2578
2579 if not (target.size() == input.size()):
-> 2580 raise ValueError("Target size ({}) must be the same as input size ({})".format(target.size(), input.size()))
2581
2582 return torch.binary_cross_entropy_with_logits(input, target, weight, pos_weight, reduction_enum)
ValueError: Target size (torch.Size([64])) must be the same as input size (torch.Size([640]))