old_contents: import os
  
  # Define paths
  CURRENT_PATH = os.path.dirname(__file__)
  IMG_PATH = os.path.join(CURRENT_PATH, r'../data/processed/train-tif-v2')
  LABEL_PATH = os.path.join(CURRENT_PATH, r'../data/processed/train_v2.csv')
  TRAIN_DATA_PATH = os.path.join(CURRENT_PATH, r'../data/processed/train_set.txt')
  VALIDATION_DATA_PATH = os.path.join(CURRENT_PATH, r'../data/processed/validation_set.txt')
  
  # Labels lookup table
  LABELS = {'primary': 0,
            'clear': 1,
            'agriculture': 2,
            'road': 3,
            'water': 4,
            'partly_cloudy': 5,
            'cultivation': 6,
            'habitation': 7,
            'haze': 8,
            'cloudy': 9,
            'bare_ground': 10,
            'selective_logging': 11,
            'artisinal_mine': 12,
            'blooming': 13,
            'slash_burn': 14,
            'blow_down': 15,
            'conventional_mine': 16}
  
  LABELS_SIZE = 17
  
  # Train-validation separation parameters
  TRAIN_VALIDATION_RATIO = 0.8
  SEED = 448
  
  # Raw image shape
  RAW_IMAGE_WIDTH = 256
  RAW_IMAGE_HEIGHT = 256
  RAW_IMAGE_DEPTH = 4
  
  # Training parameters
  DATA_AUGMENTATION = False  # flip only
  NORMALIZE_IMAGE = False
  ZERO_MEAN_IMAGE = True
  
  IMAGE_WIDTH = 32
  IMAGE_HEIGHT = 32
  IMAGE_DEPTH = 4
  
  NUM_RESIDUE_BLOCKS = 5
  WEIGHT_DECAY_FC = 0.0001
  WEIGHT_DECAY = 0.0001
  
  BATCH_SIZE = 128
  
  INITIAL_LEARNING_RATE = 0.01
  NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN = 33000
  NUM_EPOCHS_PER_DECAY = 10
  LEARNING_RATE_DECAY_FACTOR = 0.1
  
  MOVING_AVERAGE_DECAY = 0.9999
  
  # Log options
  LOG_PATH = os.path.join(CURRENT_PATH, r'../reports/planet_train')
  MAX_STEPS = 6000  # number of batches to run
  LOG_DEVICE_PLACEMENT = False
  LOG_FREQUENCY = 10
  
  # Validation parameters
  EVALUATION_PATH = os.path.join(CURRENT_PATH, r'../reports/planet_validation')
  
  NUM_EXAMPLES_PER_EPOCH_FOR_VALIDATION = 33000
  EVALUATION_NUM_EXAMPLES = 33000
  RUN_ONCE = False
  EVALUATION_INTERVAL_SECS = 60 * 5
  
new_contents: import os
  
  # Define paths
  CURRENT_PATH = os.path.dirname(__file__)
  IMG_PATH = os.path.join(CURRENT_PATH, r'../data/processed/train-tif-v2')
  LABEL_PATH = os.path.join(CURRENT_PATH, r'../data/processed/train_v2.csv')
  TRAIN_DATA_PATH = os.path.join(CURRENT_PATH, r'../data/processed/train_set.txt')
  VALIDATION_DATA_PATH = os.path.join(CURRENT_PATH, r'../data/processed/validation_set.txt')
  
  # Labels lookup table
  WEATHER_LABELS = {'clear': 0,
                    'partly_cloudy': 1,
                    'haze': 2,
                    'cloudy': 3}
  
  COMMON_LABELS = {'primary': 0,
                   'agriculture': 1,
                   'road': 2,
                   'water': 3,
                   'habitation': 4,
                   'cultivation': 5,
                   'bare_ground': 6}
  
  RARE_LABELS = {'selective_logging': 0,
                 'artisinal_mine': 1,
                 'blooming': 2,
                 'slash_burn': 3,
                 'blow_down': 4,
                 'conventional_mine': 5}
  
  AUGMENT_FLAG = {'partly_cloudy': 'flip_1',
                  'haze': 'rotate_45',
                  'cloudy': 'rotate_45'}  # to be added
  
  LABELS = [WEATHER_LABELS, COMMON_LABELS, RARE_LABELS]
  
  # Specify which network to train
  NETWORK_ID = 0
  
  # Train-validation separation parameters
  TRAIN_VALIDATION_RATIO = 0.8
  SEED = 448
  
  # Training parameters
  DATA_AUGMENTATION = False  # flip only
  NORMALIZE_IMAGE = False
  ZERO_MEAN_IMAGE = True
  
  IMAGE_WIDTH = 64
  IMAGE_HEIGHT = 64
  IMAGE_DEPTH = 4
  
  RAW_IMAGE_WIDTH = 256
  RAW_IMAGE_HEIGHT = 256
  RAW_IMAGE_DEPTH = 4
  
  WEIGHT_DECAY_FC = 0.0002
  WEIGHT_DECAY = 0.0002
  
  BATCH_SIZE = 128
  
  INITIAL_LEARNING_RATE = 0.01
  NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN = 70750
  NUM_EPOCHS_PER_DECAY = 10
  LEARNING_RATE_DECAY_FACTOR = 0.1
  
  MOVING_AVERAGE_DECAY = 0.9999
  
  # Log options
  LOG_PATH = os.path.join(CURRENT_PATH, r'../reports/planet_train')
  MAX_STEPS = 16000  # number of batches to run
  LOG_DEVICE_PLACEMENT = False
  LOG_FREQUENCY = 10
  
  # Validation parameters
  EVALUATION_PATH = os.path.join(CURRENT_PATH, r'../reports/planet_validation')
  
  NUM_EXAMPLES_PER_EPOCH_FOR_VALIDATION = 33000
  EVALUATION_NUM_EXAMPLES = 33000
  RUN_ONCE = False
  EVALUATION_INTERVAL_SECS = 60 * 5
  
subject: update settings to adapt the new code structure, use network_id to switch different models