def objective(trial):
lr = trial.suggest_loguniform("lr", 1e-3, 1e-1)
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
criterion = nn.CrossEntropyLoss()
runner = dl.SupervisedRunner()
runner.train(
model=model,
loaders=loaders,
criterion=criterion,
optimizer=optimizer,
callbacks={
"optuna":
dl.OptunaPruningCallback(loader_key="valid",
metric_key="loss",
minimize=True,
trial=trial),
"accuracy":
dl.AccuracyCallback(num_classes=10,
input_key="logits",
target_key="targets"),
},
num_epochs=2,
valid_metric="accuracy01",
minimize_valid_metric=False,
)
return trial.best_score
def objective(trial):
lr = trial.suggest_loguniform("lr", 1e-3, 1e-1)
num_hidden = int(trial.suggest_loguniform("num_hidden", 32, 128))
loaders = {
"train":
DataLoader(
MNIST(os.getcwd(),
train=False,
download=True,
transform=ToTensor()),
batch_size=32,
),
"valid":
DataLoader(
MNIST(os.getcwd(),
train=False,
download=True,
transform=ToTensor()),
batch_size=32,
),
}
model = nn.Sequential(nn.Flatten(), nn.Linear(784, num_hidden),
nn.ReLU(), nn.Linear(num_hidden, 10))
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
criterion = nn.CrossEntropyLoss()
runner = dl.SupervisedRunner(input_key="features",
output_key="logits",
target_key="targets")
runner.train(
engine=engine or dl.DeviceEngine(device),
model=model,
criterion=criterion,
optimizer=optimizer,
loaders=loaders,
callbacks={
"optuna":
dl.OptunaPruningCallback(loader_key="valid",
metric_key="accuracy01",
minimize=False,
trial=trial),
"accuracy":
dl.AccuracyCallback(input_key="logits",
target_key="targets",
num_classes=10),
},
num_epochs=2,
)
score = runner.callbacks["optuna"].best_score
return score