def test_logistic_regression_model(tmpdir, datadir):
pl.seed_everything(0)
# create dataset
dm = MNISTDataModule(num_workers=0, data_dir=datadir)
model = LogisticRegression(input_dim=28 * 28,
num_classes=10,
learning_rate=0.001)
model.prepare_data = dm.prepare_data
model.setup = dm.setup
model.train_dataloader = dm.train_dataloader
model.val_dataloader = dm.val_dataloader
model.test_dataloader = dm.test_dataloader
trainer = pl.Trainer(
max_epochs=3,
default_root_dir=tmpdir,
progress_bar_refresh_rate=0,
logger=False,
checkpoint_callback=False,
)
trainer.fit(model)
trainer.test(model)
assert trainer.progress_bar_dict['test_acc'] >= 0.9
def objective(trial):
bias = trial.suggest_categorical("bias", [True, False])
learning_rate = trial.suggest_float("learning_rate", 1e-6, 1e-1, log=True)
l1_strength = trial.suggest_float("l1_strength", 1e-10, 1e2)
l2_strength = trial.suggest_float("l2_strength", 1e-10, 1e2)
metrics_callback = MetricsCallback()
datamodule = GrenadeDataModule()
model = LogisticRegression(input_dim=12,
num_classes=2,
bias=bias,
learning_rate=learning_rate,
l1_strength=l1_strength,
l2_strength=l2_strength)
trainer = Trainer(max_epochs=200,
gpus=1,
callbacks=[
metrics_callback,
PyTorchLightningPruningCallback(trial,
monitor="val_acc")
])
trainer.fit(model=model, datamodule=datamodule)
return metrics_callback.metrics[-1]["val_acc"].item()