Train your model

Train is a part of DeepH-pack, which is used to train a deep learning model using the processed dataset.

Prepare a configuration in the format of ini, setting up the file referring to the default DeepH-pack/deeph/default.ini. The meaning of the keywords can be found in the INPUT KEYWORDS section. For a quick start, you must set up graph_dir, save_dir, raw_dir and orbital, other keywords can stay default and be adjusted later.

With the configuration file prepared, run

deeph-train --config ${config_path}

with ${config_path} replaced by the path of your configuration file.

Tips:

  • Name your dataset. Use dataset_name to name your dataset, the same names may overwrite each other.

  • Hyperparameters of the neural network. The neural network here contains some hyperparameters. For a specific problem your should try adjusting the hyperparameters to obtain better results.

  • The keyword orbital. The keyword orbital states which orbitals or matrix elements are predicted. It is a little complicated to understand its data structure. To figure out it, you can refer to the INPUT KEYWORDS section or the method make_mask in class DeepHKernel defined in DeepH-pack/deeph/kernel.py.

    Alternatively, a Python script at DeepH-pack/tools/get_all_orbital_str.py can be used to generate a default configuration to predict all orbitals with one model.

  • Use TensorBoard for visualizations. You can track and visualize the training process through TensorBoard by running

    tensorboard --logdir=./tensorboard

    in the output directory (save_dir):