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,
)