text: batch_size   = 128
  
  config = {}
  # set the parameters related to the training and testing set
  data_train_opt = {} 
  data_train_opt['batch_size'] = batch_size
  data_train_opt['unsupervised'] = True
  data_train_opt['epoch_size'] = None
  data_train_opt['random_sized_crop'] = False
  data_train_opt['dataset_name'] = 'cifar10'
  data_train_opt['split'] = 'train'
  
  data_test_opt = {}
  data_test_opt['batch_size'] = batch_size
  data_test_opt['unsupervised'] = True
  data_test_opt['epoch_size'] = None
  data_test_opt['random_sized_crop'] = False
  data_test_opt['dataset_name'] = 'cifar10'
  data_test_opt['split'] = 'test'
  
  config['data_train_opt'] = data_train_opt
  config['data_test_opt']  = data_test_opt
  config['max_num_epochs'] = 200
  
  net_opt = {}
  net_opt['num_classes'] = 4
  net_opt['num_stages']  = 4
  net_opt['use_avg_on_conv3'] = False
  
  networks = {}
  net_optim_params = {'optim_type': 'sgd', 'lr': 0.1, 'momentum':0.9, 'weight_decay': 5e-4, 'nesterov': True, 'LUT_lr':[(100, 0.1),(200, 0.05),(400, 0.0025),(800, 0.0001)]}
  networks['model'] = {'def_file': 'architectures/NetworkInNetwork.py', 'pretrained': None, 'opt': net_opt,  'optim_params': net_optim_params} 
  config['networks'] = networks
  
  criterions = {}
  criterions['loss'] = {'ctype':'CrossEntropyLoss', 'opt':None}
  config['criterions'] = criterions
  config['algorithm_type'] = 'ClassificationModel'
  