Table of contents

• Project Description
• Project Setup
• How To Train Model In this Project
• How To Restore Images In this Project
• How To Check Model Architecture

Project Description

The GAN (Generative Adversarial Netwrok) algorithm is a class of machine learning frameworks designed by Ian Goodfellow and his colleagues in June 2014. It's based on "Game Theory", to make two neural networks contest with each other.

This project will restore image using GAN model, and here is how it works:

1. Model setup:

   • Build discriminator and generator by using high-level API of Tensorflow 2, the model architecture is shown below:

     • "k(number)" means a kernel size of "number by number"
     • "n(number)" means the corresponding block has "number" channels

     

2. Train model:
   1. Yields batches of images from training_data. The training_data's shape is (image_count, image_width, image_hight, image_channel)
   2. Put the random mask over data (each picture)
   3. Customize the loss function of discriminator and generator
      • discriminator's loss function
        • 
      • generator's loss function
        • 
   4. Gradient descent with respect to variables of discriminator and generator
      • using tensorflow.GradientTape to implement gradient descent
   5. Plot training progress bar in terminal
      • using rich packages of Python to plot epochs, completeness, generator loss and discriminator loss
   6. Save model structure and parameters when it finish model training
3. Image Restoration
   1. Load trained model
   2. Get any image with mask fits training_data's shape, e.g. (image_count, image_width, image_hight, image_channel)
   3. Restore image

Project Setup

To avoid TensorFlow version conflicts, the project use pipenv (Python vitural environment) to install Python packages.

Notice: Before executing the following command, please refer to TensorFlow Installation Source and modify the TensorFlow version in Pipfile and Pipfile.lock (or modify Pipfile and remove Pipfile.lock)

pip install pipenv
pipenv shell
pipenv install

How To Train Model In This Project

In model training stage, you can modify the model architecture or the hyperparameter in src/model/GAN.py like epochs, learning_rate, learning_rate_decay, etc.

python src/train.py

How To Restore Images In This Project

You can use model you trained or apply the following model to restore images:

• The example model generator_example.h5 at src/model/trained_model/
• Other trained model on Google Drive

python src/predict.py

How To Check Model Architecture

You can modify model_path in src/watch_model_architecture.py to watch any model you want

python src/watch_model_architecture.py