pokemon-gan

Neural Network project for designing a GAN for generating new Pokémon

Dataset

Both dataset and trained models can be found here.

Setup

Local machine

Use requirements.txt to run data_augmentation.ipynb and model_evaluation.ipynb notebooks on your local machine.

Cloud service

You will need a cloud service like Saturn Cloud platform in order to run gan.ipynb and classifier.ipynb. It is essential to create required directories:

• When running gan.ipynb, you should have the following directory structure:

root/
│
├── gan.ipynb                   # Jupyter notebook containing DCGAN implementation
├── trained_models/             # Folder to store trained models
├── generated_images/           # Folder to store generated images 
└── dataset/                    # Folder containing dataset used for training 
    │
    ├── gen_{generation_no}/    # Nested folder containing specific generation images
    │   ├── 151_0_9727.png      # Image 1
    │   ├── 151_0_6487.png      # Image 2
    │   └── ...                 # Other images

• When running classifier.ipynb, you should have the following directory structure (batches contain 500 images generated from each DCGAN specialized for a single generation):

root/
│
├── classifier.ipynb            # Jupyter notebook containing DCGAN implementation
└── dataset/                    # Folder containing dataset used for training 
    │
    ├── batch_1                 # Nested folder containing images from first batch
    │   ├── image-0_gen_1.png   # Image 1
    │   ├── image-0_gen_1.png   # Image 2
    │   └── ...             
    ├── batch_2                 # Nested folder containing images from second batch
    │   └── ...                 
    ├── batch_3                 # Nested folder containing images from third batch
    │   └── ...                 
    ├── batch_4                 # Nested folder containing images from fourth batch
    │   └── ...