- Version submitted after revisions for GCA manuscript
- Various fixes, performance improvements, etc.
- Addition of a class for storing the weights of the different loss functions
- The activation function type is passed as an optional argument to the model class
- Training function as now a "save switch", allowing to turn off saving the model
- Calculation of validation loss during training is now done without asking for the gradient (smaller memory footprint)
- There is also a training2() function that splits the dataset in K folds to avoid memory issues for small GPU (slower training but much smaller memory footprint)
- A function R_Raman() now allows calculating the parameter R_Raman automatically.
- The notebook for the two experiments was moved in two individual python code, easier to run on a cluster.
- Notebooks for showing the results all are improved.
- A "best" architecture was selected and is now used for candidate training (4 layers, 300 neurons / layer, dropout 0.01) and selection (10 best networks are kept for predictions)
- A notebook allows simple predictions to be perform for one melt composition, see Prediction_simple.ipynb