Comments (4)
Another potential approach is to copy the code in main.py
into a Jupyter notebook. This would allow to modify the code in the notebook as well.
from cgcnn.
%run main.py data/sample-regression
and
%run predict.py pre-trained/formation-energy-per-atom.pth.tar data/sample-regression
seem to do the trick in a Jupyter Notebook.
from cgcnn.
For Spyder, one way to go about it is using runfile() or debugfile(), i.e.
runfile(<path-to-main.py>, args='data/sample-regression', wdir=<path-to-working-directory>)
or
debugfile(<path-to-main.py>, args='data/sample-regression', wdir=<path-to-working-directory>)
e.g.
runfile('main.py', args='data/sample-regression', wdir='.')
from cgcnn.
Maybe you should copy and paste all the cgcnn code in your jupyter notebook in sequence, and should amend "args" part to a class with instance attributes. I am using cgcnn model like that. Until now, It's working well. hope you success!
from cgcnn.
Related Issues (20)
- How to understand output to generate crystal graphs without training model HOT 6
- Description of crys_fea in model.py HOT 1
- What does 'target' means in id_prop.csv?? HOT 2
- Description for Materials Project .csv files HOT 4
- Predict at atom level HOT 3
- why the cgcnn model cannot predict graphene and diamond? HOT 3
- The setting of hyperparameters to get the best performance HOT 2
- Data construction specifics HOT 2
- On understanding the AtomInitializer class
- The problem of using the Materials Project database and the Perovskite database HOT 3
- Add CIF module to be installed in the environment in README file HOT 2
- How to understand the shape of "nbr_fea_idx" in the code HOT 2
- Could you please tell me why do you use one hot encode?
- Question about fetching Materials Project Data HOT 2
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- Size mismatch when loading pre-trained models HOT 6
- Atom features specifics
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from cgcnn.