def train_model(model, dataloader_dict, criterion, optimizer, num_epoch):

    since = time.time()
    best_acc = 0.0

    for epoch in range(num_epoch):
        print('Epoch {}/{}'.format(epoch+1, num_epoch))
        print('-'*20)

        for phase in ['train', 'val']:
            if phase == 'train':
                model.train()
            else:
                model.eval()

            epoch_loss = 0.0
            epoch_corrects = 0

            for inputs, labels in tqdm(dataloader_dict[phase]):
                inputs = inputs.to(device)
                labels = labels.to(device)
                optimizer.zero_grad()

                with torch.set_grad_enabled(phase == 'train'):
                    outputs = model(inputs)
                    _, preds = torch.max(outputs, 1)
                    loss = criterion(outputs, labels)

                    if phase == 'train':
                        loss.backward()
                        optimizer.step()

                    epoch_loss += loss.item() * inputs.size(0)
                    epoch_corrects += torch.sum(preds == labels.data)

            epoch_loss = epoch_loss / len(dataloader_dict[phase].dataset)
            epoch_acc = epoch_corrects.double() / len(dataloader_dict[phase].dataset)

            print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc))
    
    time_elapsed = time.time() - since
    print('Training complete in {:.0f}m {:.0f}s'.format(
          time_elapsed // 60, time_elapsed % 60))
    return model