Texture Generator using Generative Advisary Networks

This is a neural network for generating images using the GAN (Generative Advisary Network) arcitechture. It is initially setup for creating more texture-like images where some cropping, rotation and filtering can be used to increase the amount training data (input images).

Examples

Sonewalls

Usage

1. Put your real images in the input folder

2. Train a GAN using:
   python train.py network_name [iterations]

3. Generate textures using:
   python generate.py network_name [num_images]

Requirements

• Python 3
• Tensorflow
• Pillow
• Numpy

Inspirations

• Generative Adversarial Nets in TensorFlow
• DCGAN

TODO

• Fix the stalled learning
  • only small improvement beyond 20 000 iterations (mostly shuffling)
  • Better optimization?
    • Better cost function?
    • Better network configuration?
• Try bigger sizes than 64x64
  • maybe through an upscaling GAN
• Try making repeatable textures