logging.info("build model") representation = spk.SchNet(n_interactions=6) output_modules = [ spk.Atomwise( property="energy", derivative="forces", mean=means["energy"], stddev=stddevs["energy"], negative_dr=True, ) ] model = schnetpack.atomistic.model.AtomisticModel(representation, output_modules) # build optimizer optimizer = Adam(params=model.parameters(), lr=1e-4) # hooks logging.info("build trainer") metrics = [MeanAbsoluteError(p, p) for p in properties] hooks = [ CSVHook(log_path=model_dir, metrics=metrics), ReduceLROnPlateauHook(optimizer) ] # trainer loss = mse_loss(properties) trainer = Trainer( model_dir, model=model, hooks=hooks,
########################### output_modules = [ spk.atomistic.Atomwise( n_in=representation.n_atom_basis, property="energy", derivative="forces", mean=means["energy"], stddev=stddevs["energy"], negative_dr=True, ) ] model = schnetpack.atomistic.model.AtomisticModel(representation, output_modules) # build optimizer optimizer = Adam(params=model.parameters(), lr=1e-4, ) # hooks logging.info("build trainer") metrics = [MeanAbsoluteError(p, p) for p in properties] ###hooks = [CSVHook(log_path=model_dir, metrics=metrics), ReduceLROnPlateauHook(optimizer)] hooks = [CSVHook(log_path=model_dir, metrics=metrics) ] # trainer clip_norm=None loss = build_mse_loss(properties, loss_tradeoff=[0.01, 0.99]) trainer = Trainer( model_dir, model=model, hooks=hooks,
output_modules = [ spk.atomistic.Atomwise( n_in=representation.n_atom_basis, property="energy", derivative="forces", mean=means["energy"], stddev=stddevs["energy"], negative_dr=True, ) ] model = schnetpack.atomistic.model.AtomisticModel(representation, output_modules) # build optimizer optimizer = Adam( params=model.parameters(), lr=1e-4, ) # hooks logging.info("build trainer") metrics = [MeanAbsoluteError(p, p) for p in properties] ###hooks = [CSVHook(log_path=model_dir, metrics=metrics), ReduceLROnPlateauHook(optimizer)] hooks = [CSVHook(log_path=model_dir, metrics=metrics)] # trainer clip_norm = None loss = build_mse_loss(properties, loss_tradeoff=[0.01, 0.99]) trainer = Trainer( model_dir,