Closed IamShubhamGupto closed 1 year ago
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See visual diffs & provide feedback on Jupyter Notebooks.
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There seems to be an error I missed, ill look into it tomorrow
It is telling me that all checks have passed. Should I approve?
It is telling me that all checks have passed. Should I approve?
The logs show there a logical error in the resnet notebook. Im taking a look now
UPDATE:
The error is generated form one of the older notebook Improving neural nets
We can merge the new notebook, they are fine
Run find _build/html -name "*.log" -print -exec cat {} \;
_build/html/reports/notebooks/Improving_Neural_networks.err.log
Traceback (most recent call last):
File "/usr/share/miniconda/envs/L96M2lines/lib/python3.9/site-packages/jupyter_cache/executors/utils.py", line 58, in single_nb_execution
executenb(
File "/usr/share/miniconda/envs/L96M2lines/lib/python3.9/site-packages/nbclient/client.py", line 1305, in execute
return NotebookClient(nb=nb, resources=resources, km=km, **kwargs).execute()
File "/usr/share/miniconda/envs/L96M2lines/lib/python3.9/site-packages/jupyter_core/utils/__init__.py", line 166, in wrapped
return loop.run_until_complete(inner)
File "/usr/share/miniconda/envs/L96M2lines/lib/python3.9/asyncio/base_events.py", line 647, in run_until_complete
return future.result()
File "/usr/share/miniconda/envs/L96M2lines/lib/python3.9/site-packages/nbclient/client.py", line 705, in async_execute
await self.async_execute_cell(
File "/usr/share/miniconda/envs/L96M2lines/lib/python3.9/site-packages/nbclient/client.py", line 1058, in async_execute_cell
await self._check_raise_for_error(cell, cell_index, exec_reply)
File "/usr/share/miniconda/envs/L96M2lines/lib/python3.9/site-packages/nbclient/client.py", line 914, in _check_raise_for_error
raise CellExecutionError.from_cell_and_msg(cell, exec_reply_content)
nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell:
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plt.figure(dpi=150)
plt.plot(train_loss, "b", label="Training loss")
plt.plot(val_loss, "b--", label="Validation loss")
plt.plot(train_loss_drop, "r", label="Training loss, with dropout")
plt.plot(val_loss_drop, "r--", label="Validation loss, with dropout")
plt.legend();
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Have not modified headers and text as there may be additional changes to make in content.