def main(cfg):
shapes = cfg.model.shapes
opt_params = cfg.optimizer.params
experiment = Experiment(log_code=False)
experiment.set_code(filename=hydra.utils.to_absolute_path(__file__))
experiment.add_tag("with_hydra")
experiment.log_parameters({"hydra-cfg": [cfg]})
model = layers.MLP(shapes)
optimizer = optim.Adam(model.parameters(), **opt_params)
runner = tasks.ClassificationRunner(
model,
optimizer=optimizer,
criterion=nn.CrossEntropyLoss(),
experiment=experiment
)
runner.fit(x, y, epochs=10, checkpoint_path="./checkpoints")
runner.save()
import torch.optim as optim
from torch.utils.data import DataLoader
from torch.optim.lr_scheduler import ExponentialLR, CosineAnnealingLR
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from enchanter import addons
from enchanter import tasks
from enchanter.addons import layers
from enchanter.engine.modules import fix_seed, get_dataset
fix_seed(0)
experiment = Experiment()
model = layers.MLP([4, 512, 128, 3], addons.mish)
optimizer = optim.Adam(model.parameters())
runner = tasks.ClassificationRunner(model,
optimizer=optimizer,
criterion=nn.CrossEntropyLoss(),
experiment=experiment,
scheduler=[
CosineAnnealingLR(optimizer,
T_max=10,
eta_min=1e-10),
ExponentialLR(optimizer, gamma=0.9),
])
x, y = load_iris(return_X_y=True)
x = x.astype("float32")
y = y.astype("int64")
x, y = load_boston(return_X_y=True)
y = y.reshape(-1, 1)
x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=0)
x_train, x_val, y_train, y_val = train_test_split(x_train,
y_train,
random_state=0)
train_ds = get_dataset(x_train.astype(np.float32), y_train.astype(np.float32))
val_ds = get_dataset(x_val.astype(np.float32), y_val.astype(np.float32))
test_ds = get_dataset(x_test.astype(np.float32), y_test.astype(np.float32))
train_loader = DataLoader(train_ds, batch_size=32, shuffle=True)
val_loader = DataLoader(val_ds, batch_size=32, shuffle=False)
test_loader = DataLoader(test_ds, batch_size=32, shuffle=False)
model = layers.MLP([13, 512, 128, 1], Mish())
optimizer = optim.Adam(model.parameters())
def test_regression_1():
runner = tasks.RegressionRunner(
model, optimizer, nn.MSELoss(),
OfflineExperiment(offline_directory="./logs", display_summary_level=0))
runner.add_loader("train",
train_loader).add_loader("val", val_loader).add_loader(
"test", test_loader)
runner.train_config(epochs=1)
try:
runner.run(verbose=True)
is_pass = True
import torch.optim as optim
from sklearn.datasets import load_iris
import enchanter.tasks as tasks
import enchanter.addons as addons # pylint: disable=W0611
import enchanter.addons.layers as layers
from enchanter.utils import comet
config = comet.TunerConfigGenerator(
algorithm="bayes",
metric="train_avg_loss",
objective="minimize",
seed=0,
trials=5
)
config.suggest_categorical("activation", ["addons.mish", "torch.relu", "torch.sigmoid"])
opt = Optimizer(config.generate())
for experiment in opt.get_experiments():
model = layers.MLP([4, 512, 128, 3], eval(experiment.get_parameter("activation")))
optimizer = optim.Adam(model.parameters())
runner = tasks.ClassificationRunner(
model, optimizer=optimizer, criterion=nn.CrossEntropyLoss(), experiment=experiment
)
x, y = load_iris(return_X_y=True)
x = x.astype("float32")
y = y.astype("int64")
runner.fit(x, y, epochs=1, batch_size=32)