transform=train_transformer),
batch_size=batch_size,
shuffle=True,
)
test_loader = DataLoader(
datasets.CIFAR10(data_dir, train=False, transform=test_transformer),
batch_size=batch_size,
shuffle=True,
)
logger = set_logger("classification_cifar10_cnn")
# FusionClassifier
model = FusionClassifier(estimator=LeNet5,
n_estimators=n_estimators,
cuda=True)
# Set the optimizer
model.set_optimizer("Adam", lr=lr, weight_decay=weight_decay)
# Training
tic = time.time()
model.fit(train_loader, epochs=epochs)
toc = time.time()
training_time = toc - tic
# Evaluating
tic = time.time()
testing_acc = model.evaluate(test_loader)
toc = time.time()
(0.2023, 0.1994, 0.2010))])
train_loader = DataLoader(datasets.CIFAR10(
data_dir, train=True, download=True, transform=transformer),
batch_size=batch_size, shuffle=True)
test_loader = DataLoader(datasets.CIFAR10(
data_dir, train=False, transform=transforms.Compose([
transforms.ToTensor(), transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010))])),
batch_size=batch_size, shuffle=True)
# FusionClassifier
model = FusionClassifier(estimator=LeNet5,
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_acc = model.predict(test_loader)
toc = time.time()
evaluating_time = toc - tic
records.append(('FusionClassifier',
training_time,