Model

Model

• Build a Neural Network using ChemML
  • Fit the chemml.model.MLP model to the training data
  • Saving the ChemML model
  • Loading the saved ChemML model
  • Predict the densities for the test data
  • Evaluate model performance using chemml.utils.regression_metrics
  • Plot actual vs. predicted values
  • If the Underlying (TensorFlow/PyTorch) model is required …
  • Sometimes you may need the keras model without the output layer (for e.g., for transfer learning)
• Neural Fingerprints
  • Splitting and preprocessing the data
  • Building the Neural Fingerprints
  • Building and training the neural network