DCGAN Tensorflow implementation of Deep Convolutional Generative Adversarial Networks was done by Kim Taehoon. Also the huge part of this README is copied from his repo.

• Brandon Amos wrote an excellent blog post and image completion code based on this repo.
• To avoid the fast convergence of D (discriminator) network, G (generator) network is updated twice for each D network update, which differs from original paper.

• Python 2.7 or Python 3.3+
• Tensorflow 0.12.1
• SciPy
• pillow
• (Optional) moviepy (for visualization)
• (I faced issue while using 1.16.0, so I rolled back to 1.15.4)[NumPy]

To training purposes I compiled 3 datasets (TSRD (I suppose with Japanies signs), Belgium-TSC, German-GTSRB) and extracted only speed limit signs with various numbers, stop sign, road closed, no entry for vehicular traffic.

To train a model with downloaded dataset:

$ python main.py --dataset DATASET_NAME --input_height=28 --output_height=28 --train
or
$ python main.py --dataset DATASET_NAME --input_height=108 --train --crop

To test with an existing model:

$ python main.py --dataset DATASET_NAME --input_height=28 --output_height=28
or
$ python main.py --dataset DATASET_NAME --input_height=108 --crop  

Progress of training

Basic dataset	Blue Cirle dataset	Blue Circle with preprocessing

Basic dataset	Blue Cirle dataset	Blue Circle with preprocessing

After 6th epoch:

After 10th epoch:

After 17th epoch:

After 24th epoch:

[DONE] Remove mnist related code

1. Preprocessing of dataset: apply auto crop and [DONE] brightness/contrast adjustment
2. Play around with networks architecture
3. Add traffic sign classifier and compare result on "real" signes and DCGAN-generated

Aleksander Lukashou / @va9abund with reference to @carpedm20