def main(controller, cosine_annealing_scheduler):
running_reward = 0
child = Child(dropout_rate=float(args.dropout),
use_auxiliary=args.use_auxiliary).to(device)
child_optimizer = optim.SGD(child.parameters(),
lr=0.05,
momentum=0.9,
weight_decay=1e-4,
nesterov=True)
cosine_lr_scheduler = cosine_annealing_scheduler(child_optimizer, lr=0.05)
for epoch in range(150):
cosine_lr_scheduler.step()
child = train_child(epoch, controller, child, child_optimizer)
torch.save(child.state_dict(), './save_model/child.pth')
torch.save(controller.state_dict(), './save_model/controller.pth')
if epoch < 150 - 1:
controller_model, running_reward = train_controller(
controller, child, running_reward)
final_model = model
final_acc = test_final_model(final_model)
print('training is done. Final accuarcy is ', final_acc)
batch_size=100,
shuffle=False,
num_workers=args.num_worker)
# there are 10 classes so the dataset name is cifar-10
classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse',
'ship', 'truck')
print('==> Making model..')
criterion = nn.CrossEntropyLoss()
normal_arc = best_reward_arc[0]
reduction_arc = best_reward_arc[1]
child = Child().to(device)
child_optimizer = optim.SGD(child.parameters(),
lr=0.05,
momentum=0.9,
weight_decay=1e-4,
nesterov=True)
cosine_lr_scheduler = cosine_annealing_scheduler(child_optimizer, lr=0.05)
best_acc = 0
for epoch in range(300):
child.train()
cosine_lr_scheduler.step()
train_loss = 0
correct = 0
total = 0
for batch_idx, (inputs, targets) in enumerate(train_loader):
