Automatic fine-tune in production

After running the system for a while, we will have some user-labeled images. We will create a fine-tune process to automatically run every day and fine-tune the latest model with new data.

Let's create a file named finetune.py in the scripts folder.

Loading the user-labeled data

First, we will add the code to download all user-labeled images from the production server:

    import tensorflow as tf 
    import os 
    import json 
    import random 
    import requests 
    import shutil 
    from scipy.misc import imread, imsave 
    from datetime import datetime 
    from tqdm import tqdm 
 
    import nets, models, datasets 
 
 
    def ensure_folder_exists(folder_path): 
    if not os.path.exists(folder_path): 
        os.mkdir(folder_path) 
    return folder_path 
 
 
    def download_user_data(url, user_dir, train_ratio=0.8): 
    response = requests.get("%s/user-labels" % url) 
    data = json.loads(response.text) 
 
    if not os.path.exists(user_dir)...