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, loss_fn=loss, optimizer=optimizer, train_loader=train_loader, validation_loader=val_loader, ) # run training
spk.Atomwise( property=QM9.U0, mean=means[QM9.U0], stddev=stddevs[QM9.U0], atomref=atomrefs[QM9.U0], ) ] model = spk.AtomisticModel(representation, output_modules) # build optimizer optimizer = Adam(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, loss_fn=loss, optimizer=optimizer, train_loader=train_loader, validation_loader=val_loader, ) # run training logging.info("training")
n_conv=4, act="ssp", aggregation_mode="avg", norm=True) model.set_mean_std(dataset.mean, dataset.std) # build optimizer optimizer = Adam(model.parameters(), lr=4.0e-4) # hooks logging.info("build trainer") metrics = [MeanAbsoluteError("energy", model_output=None)] hooks = [ CSVHook(log_path=MODEL_DIR, metrics=metrics), ReduceLROnPlateauHook(optimizer, factor=0.75), TensorboardHook(log_path=MODEL_DIR, metrics=metrics), EarlyStoppingHook(80) ] # trainer loss = nn.MSELoss() trainer = Trainer( MODEL_DIR, model=model, hooks=hooks, loss_fn=loss, optimizer=optimizer, train_loader=train_loader, validation_loader=val_loader, )