Python parameters Beispiele

Beispiel #1

Datei: ethanol_script.py Projekt: byael/schnetpack

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,

Beispiel #2

Datei: salicylic_script.py Projekt: YDS-Med/Transformer3D

###########################
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,

Beispiel #3

Datei: ethanol_script.py Projekt: YDS-Med/Transformer3D

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,