weight_decay = 5e-4
epochs = 50
# Utils
batch_size = 256
records = []
torch.manual_seed(0)
# Load data
train_loader, test_loader = load_data(batch_size)
print('Finish loading data...\n')
# FusionRegressor
model = FusionRegressor(estimator=MLP,
n_estimators=n_estimators,
output_dim=output_dim,
lr=lr,
weight_decay=weight_decay,
epochs=epochs)
tic = time.time()
model.fit(train_loader)
toc = time.time()
training_time = toc - tic
tic = time.time()
testing_mse = model.predict(test_loader)
toc = time.time()
evaluating_time = toc - tic
records.append(
('FusionRegressor', training_time, evaluating_time, testing_mse))
epochs = 50
# Utils
batch_size = 512
records = []
torch.manual_seed(0)
# Load data
train_loader, test_loader = load_data(batch_size)
print("Finish loading data...\n")
logger = set_logger("regression_YearPredictionMSD_mlp")
# FusionRegressor
model = FusionRegressor(estimator=MLP,
n_estimators=n_estimators,
cuda=True)
# Set the optimizer
model.set_optimizer("Adam", lr=lr, weight_decay=weight_decay)
tic = time.time()
model.fit(train_loader, epochs=epochs)
toc = time.time()
training_time = toc - tic
tic = time.time()
testing_mse = model.predict(test_loader)
toc = time.time()
evaluating_time = toc - tic