HenriquesLab / HenriquesLab/ZeroCostDL4Mic
YOLOv2 notebook local error - section 4.1
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Description
When I run the YOLOv2_ZeroCostDL4Mic notebook locally, I get an error in section 4.1, The error code is " train('config.json', full_model_path, percentage_validation) ". It seems that there is a problem with the format of the json file. And the error reported is as follows:
JSONDecodeError Traceback (most recent call last)
/tmp/ipykernel_3314/338773247.py in
1 os.chdir('/home/wan/Tensorflow_test/YOLOv2/content/keras-yolo2')
----> 2 train('config.json', full_model_path, percentage_validation)
3
4 shutil.copyfile('/home/wan/Tensorflow_test/YOLOv2/content/keras-yolo2/config.json',full_model_path+'/config.json')
5
/tmp/ipykernel_3314/2825244846.py in train(config_path, model_path, percentage_validation)
691
692 with open(config_path) as config_buffer:
--> 693 config = json.loads(config_buffer.read())
694
695 ###############################
~/miniconda3/envs/myenv/lib/python3.7/json/init.py in loads(s, encoding, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)
346 parse_int is None and parse_float is None and
347 parse_constant is None and object_pairs_hook is None and not kw):
--> 348 return _default_decoder.decode(s)
349 if cls is None:
350 cls = JSONDecoder
~/miniconda3/envs/myenv/lib/python3.7/json/decoder.py in decode(self, s, _w)
335
336 """
--> 337 obj, end = self.raw_decode(s, idx=_w(s, 0).end())
338 end = _w(s, end).end()
339 if end != len(s):
~/miniconda3/envs/myenv/lib/python3.7/json/decoder.py in raw_decode(self, s, idx)
353 obj, end = self.scan_once(s, idx)
354 except StopIteration as err:
--> 355 raise JSONDecodeError("Expecting value", s, err.value) from None
356 return obj, end
JSONDecodeError: Expecting value: line 5 column 33 (char 131)
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Research direction
Start with the YOLOv2_ZeroCostDL4Mic notebook section 4.1 and the train(config.json, full_model_path, percentage_validation) entry point. Inspect config.json at line 5, column 33 and run the notebook locally to confirm the configuration parses and training proceeds past that call.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- jupyter-notebook, keras, python, tensorflow
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100