I’d need a reproducer for this, and for Christ’s sake, as we have said a thousand times, please post the full stack trace and not just the last error!
Thank you 
2 Likes
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-10-d81c6bd29d71> in <module>
----> 1 learn.lr_find()
/opt/conda/envs/fastai/lib/python3.7/site-packages/fastai2/callback/schedule.py in lr_find(self, start_lr, end_lr, num_it, stop_div, show_plot, suggestions)
222 if suggestions:
223 lrs,losses = tensor(self.recorder.lrs[5:-5]),tensor(self.recorder.losses[5:-5])
--> 224 lr_min = lrs[losses.argmin()].item()
225 grads = (losses[1:]-losses[:-1]) / (lrs[1:].log()-lrs[:-1].log())
226 lr_steep = lrs[grads.argmin()].item()
RuntimeError: cannot perform reduction function argmin on a tensor with no elements because the operation does not have an identity
1 Like
I understand better now. You have less than 10 losses, I’m guessing because of the big jumps at the beginning, so there is nothing to get the lr_min from. Will add a test to remove that bug.
2 Likes
Thank you so much @sgugger, really appreciated your help.
not sure if this it is correlated and can help as well, but I was trying to play with Recorder.plot_lr_find() and I got this
You can’t call a method of a class unless it’s a special kind of method called classmethod. You need to call the method on an instance like learn.recorder.plot_lr_find().
1 Like
Awesome, yes this plot_lr_find is what I was looking for. Thank you so much.
I’ve created a learner on Kaggle as follows:
learn = cnn_learner(dls, resnet18, metrics=[error_rate, accuracy])
After this I’m trying to do learn.lr_find() and it gives me the following error
---------------------------------------------------------------------------
OSError Traceback (most recent call last)
/opt/conda/lib/python3.6/site-packages/fastai2/learner.py in fit(self, n_epoch, lr, wd, cbs, reset_opt)
187 try:
--> 188 self._do_begin_fit(n_epoch)
189 for epoch in range(n_epoch):
/opt/conda/lib/python3.6/site-packages/fastai2/learner.py in _do_begin_fit(self, n_epoch)
159 def _do_begin_fit(self, n_epoch):
--> 160 self.n_epoch,self.loss = n_epoch,tensor(0.); self('begin_fit')
161
/opt/conda/lib/python3.6/site-packages/fastai2/learner.py in __call__(self, event_name)
123
--> 124 def __call__(self, event_name): L(event_name).map(self._call_one)
125 def _call_one(self, event_name):
/opt/conda/lib/python3.6/site-packages/fastcore/foundation.py in map(self, f, *args, **kwargs)
371 else f.__getitem__)
--> 372 return self._new(map(g, self))
373
/opt/conda/lib/python3.6/site-packages/fastcore/foundation.py in _new(self, items, *args, **kwargs)
322 def _xtra(self): return None
--> 323 def _new(self, items, *args, **kwargs): return type(self)(items, *args, use_list=None, **kwargs)
324 def __getitem__(self, idx): return self._get(idx) if is_indexer(idx) else L(self._get(idx), use_list=None)
/opt/conda/lib/python3.6/site-packages/fastcore/foundation.py in __call__(cls, x, *args, **kwargs)
40
---> 41 res = super().__call__(*((x,) + args), **kwargs)
42 res._newchk = 0
/opt/conda/lib/python3.6/site-packages/fastcore/foundation.py in __init__(self, items, use_list, match, *rest)
313 if (use_list is not None) or not _is_array(items):
--> 314 items = list(items) if use_list else _listify(items)
315 if match is not None:
/opt/conda/lib/python3.6/site-packages/fastcore/foundation.py in _listify(o)
249 if isinstance(o, str) or _is_array(o): return [o]
--> 250 if is_iter(o): return list(o)
251 return [o]
/opt/conda/lib/python3.6/site-packages/fastcore/foundation.py in __call__(self, *args, **kwargs)
215 fargs = [args[x.i] if isinstance(x, _Arg) else x for x in self.pargs] + args[self.maxi+1:]
--> 216 return self.fn(*fargs, **kwargs)
217
/opt/conda/lib/python3.6/site-packages/fastai2/learner.py in _call_one(self, event_name)
126 assert hasattr(event, event_name)
--> 127 [cb(event_name) for cb in sort_by_run(self.cbs)]
128
/opt/conda/lib/python3.6/site-packages/fastai2/learner.py in <listcomp>(.0)
126 assert hasattr(event, event_name)
--> 127 [cb(event_name) for cb in sort_by_run(self.cbs)]
128
/opt/conda/lib/python3.6/site-packages/fastai2/callback/core.py in __call__(self, event_name)
23 (self.run_valid and not getattr(self, 'training', False)))
---> 24 if self.run and _run: getattr(self, event_name, noop)()
25 if event_name=='after_fit': self.run=True #Reset self.run to True at each end of fit
/opt/conda/lib/python3.6/site-packages/fastai2/callback/schedule.py in begin_fit(self)
175 super().begin_fit()
--> 176 self.learn.save('_tmp')
177 self.best_loss = float('inf')
/opt/conda/lib/python3.6/site-packages/fastai2/learner.py in save(self, file, **kwargs)
264 def save(self, file, **kwargs):
--> 265 file = join_path_file(file, self.path/self.model_dir, ext='.pth')
266 save_model(file, self.model, getattr(self,'opt',None), **kwargs)
/opt/conda/lib/python3.6/site-packages/fastcore/utils.py in join_path_file(file, path, ext)
481 if not isinstance(file, (str, Path)): return file
--> 482 path.mkdir(parents=True, exist_ok=True)
483 return path/f'{file}{ext}'
/opt/conda/lib/python3.6/pathlib.py in mkdir(self, mode, parents, exist_ok)
1245 try:
-> 1246 self._accessor.mkdir(self, mode)
1247 except FileNotFoundError:
/opt/conda/lib/python3.6/pathlib.py in wrapped(pathobj, *args)
386 def wrapped(pathobj, *args):
--> 387 return strfunc(str(pathobj), *args)
388 return staticmethod(wrapped)
OSError: [Errno 30] Read-only file system: '../input/rsna-hemorrhage-jpg/train_jpg/train_jpg/models'
During handling of the above exception, another exception occurred:
AttributeError Traceback (most recent call last)
<ipython-input-25-d81c6bd29d71> in <module>
----> 1 learn.lr_find()
/opt/conda/lib/python3.6/site-packages/fastai2/callback/schedule.py in lr_find(self, start_lr, end_lr, num_it, stop_div, show_plot, suggestions)
221 n_epoch = num_it//len(self.dls.train) + 1
222 cb=LRFinder(start_lr=start_lr, end_lr=end_lr, num_it=num_it, stop_div=stop_div)
--> 223 with self.no_logging(): self.fit(n_epoch, cbs=cb)
224 if show_plot: self.recorder.plot_lr_find()
225 if suggestions:
/opt/conda/lib/python3.6/site-packages/fastai2/learner.py in fit(self, n_epoch, lr, wd, cbs, reset_opt)
196
197 except CancelFitException: self('after_cancel_fit')
--> 198 finally: self('after_fit')
199
200 def validate(self, ds_idx=1, dl=None, cbs=None):
/opt/conda/lib/python3.6/site-packages/fastai2/learner.py in __call__(self, event_name)
122 def ordered_cbs(self, event): return [cb for cb in sort_by_run(self.cbs) if hasattr(cb, event)]
123
--> 124 def __call__(self, event_name): L(event_name).map(self._call_one)
125 def _call_one(self, event_name):
126 assert hasattr(event, event_name)
/opt/conda/lib/python3.6/site-packages/fastcore/foundation.py in map(self, f, *args, **kwargs)
370 else f.format if isinstance(f,str)
371 else f.__getitem__)
--> 372 return self._new(map(g, self))
373
374 def filter(self, f, negate=False, **kwargs):
/opt/conda/lib/python3.6/site-packages/fastcore/foundation.py in _new(self, items, *args, **kwargs)
321 @property
322 def _xtra(self): return None
--> 323 def _new(self, items, *args, **kwargs): return type(self)(items, *args, use_list=None, **kwargs)
324 def __getitem__(self, idx): return self._get(idx) if is_indexer(idx) else L(self._get(idx), use_list=None)
325 def copy(self): return self._new(self.items.copy())
/opt/conda/lib/python3.6/site-packages/fastcore/foundation.py in __call__(cls, x, *args, **kwargs)
39 return x
40
---> 41 res = super().__call__(*((x,) + args), **kwargs)
42 res._newchk = 0
43 return res
/opt/conda/lib/python3.6/site-packages/fastcore/foundation.py in __init__(self, items, use_list, match, *rest)
312 if items is None: items = []
313 if (use_list is not None) or not _is_array(items):
--> 314 items = list(items) if use_list else _listify(items)
315 if match is not None:
316 if is_coll(match): match = len(match)
/opt/conda/lib/python3.6/site-packages/fastcore/foundation.py in _listify(o)
248 if isinstance(o, list): return o
249 if isinstance(o, str) or _is_array(o): return [o]
--> 250 if is_iter(o): return list(o)
251 return [o]
252
/opt/conda/lib/python3.6/site-packages/fastcore/foundation.py in __call__(self, *args, **kwargs)
214 if isinstance(v,_Arg): kwargs[k] = args.pop(v.i)
215 fargs = [args[x.i] if isinstance(x, _Arg) else x for x in self.pargs] + args[self.maxi+1:]
--> 216 return self.fn(*fargs, **kwargs)
217
218 # Cell
/opt/conda/lib/python3.6/site-packages/fastai2/learner.py in _call_one(self, event_name)
125 def _call_one(self, event_name):
126 assert hasattr(event, event_name)
--> 127 [cb(event_name) for cb in sort_by_run(self.cbs)]
128
129 def _bn_bias_state(self, with_bias): return bn_bias_params(self.model, with_bias).map(self.opt.state)
/opt/conda/lib/python3.6/site-packages/fastai2/learner.py in <listcomp>(.0)
125 def _call_one(self, event_name):
126 assert hasattr(event, event_name)
--> 127 [cb(event_name) for cb in sort_by_run(self.cbs)]
128
129 def _bn_bias_state(self, with_bias): return bn_bias_params(self.model, with_bias).map(self.opt.state)
/opt/conda/lib/python3.6/site-packages/fastai2/callback/core.py in __call__(self, event_name)
22 _run = (event_name not in _inner_loop or (self.run_train and getattr(self, 'training', True)) or
23 (self.run_valid and not getattr(self, 'training', False)))
---> 24 if self.run and _run: getattr(self, event_name, noop)()
25 if event_name=='after_fit': self.run=True #Reset self.run to True at each end of fit
26
/opt/conda/lib/python3.6/site-packages/fastai2/callback/progress.py in after_fit(self)
37 def after_fit(self):
38 if getattr(self, 'mbar', False):
---> 39 self.mbar.on_iter_end()
40 delattr(self, 'mbar')
41 self.learn.logger = self.old_logger
/opt/conda/lib/python3.6/site-packages/fastprogress/fastprogress.py in on_iter_end(self)
155 total_time = format_time(time.time() - self.main_bar.start_t)
156 self.text = f'Total time: {total_time} <p>' + self.text
--> 157 self.out.update(HTML(self.text))
158
159 def add_child(self, child):
AttributeError: 'NBMasterBar' object has no attribute 'out'
Is this not the right function to use? I tried learn.recorder.plot_lr_find() and it gives me a blank plot
I’m also not able to plot validation loss. Is something wrong with my installation?
I’ve just done pip install fastai2 on Kaggle.
Please do not at-mention me when there are other people that can help.
For your first error, the real error message is
(the rest is an error that happened because of that).
This is because learn.lr_find tries to save your model (to reload it once it has done its mock training) but it can’t on the default path it has (data path/models by default).You need to pass model_dir=Path(...) in your call to cnn_learner with the … being a path that points to somewhere writeable.
For your second error, no one can help without seeing the code you are executing.
Thank you for the response. And sorry for the at-mention again, I only did it after waiting 20 hours. For my second error, I’ve run learn.fine_tune(5) and then I’m doing learn.recorder.plot_loss()