SI_Toolkit

System identification with neural networks - training and testing scripts

Use this repository to train neural networks on dynamical systems.

1. Collect Training Data

• Run a data generation script on your simulation environment.
• Example: run_data_generator in CartPoleSimulator
• Or create a folder ./Experiment_Recordings/Dataset-1/ and put data in there

2. Load Training Data and Normalize it

• Set paths to experiment folders in ./SI_Toolkit_ASF/config_training.yml
  • In our example: Set to ./Experiment_Recordings/Dataset-1/
• Then run python -m SI_Toolkit_ASF.run.Create_normalization_file
• This creates a new normalization file in [path_to_experiment_folder]/NormalizationInfo/

3. Train Network

• Type python -m SI_Toolkit_ASF.run.Train_Network -h to view all arguments that can be passed to training script
• You will want to specify a --net_name (see help message to see how to do that)
• Specify the --path_to_normalization_info to be the CSV file created in step 2.
• Training creates a new folder with the network name in [path_to_experiment_folder]/Models/ which contains
  • Save checkpoints
  • A .txt file to preserve all the params used for training
  • A .csv file and plot to document the training progress / loss curves

4. View model performance

• Adjust the parameters in ./SI_Toolkit_ASF/config_testing.yml to select the correct test file and model
• Run script python -m SI_Toolkit_ASF.run.Run_Brunton_Test to see how your model performs on test data
• All parameters for this script are in the 'testing' section of the config:
  • PATH_TO_NORMALIZATION_INFO: Path to the normalization info file created in 2.
  • tests: A list of model names, typically just the one model trained in 3.
  • TEST_FILE: An experiment run to compare observations and predictions on. Specify any list of CSVs generated in 1.

5. Run simulator with trained network model

• Specify NET_NAME in config -> this is the network used in predictor_autoregressive_neural.py
• When using the CartPoleSimulator repo as system: In the CartPoleSimulator config, specify "predictor_autoregressive_neural" as predictor type