Deep Convolutional Generative Adversarial Networks (DCGAN) implemented in TensorFlow-Slim

This is a TensorFlow implementation of the following paper: https://arxiv.org/pdf/1511.06434v2.pdf. Some parameters and settings may not be exactly the same from the paper! Nonetheless, the code is able to generate images.

TensorFlow and tensorflow.contrib.slim are required, along with their dependencies (e.g. numpy). The only other additional dependency is PIL. This can be installed with pip:

pip install Pillow

Download the celebA dataset and put the images in data/celebA/ (create the directory structure if needed).

Put your images in data/your_dataset/. Create the directory structure and name your_dataset with whatever you want. Images should be *.jpg.

Optionally, create a file data/your_dataset.txt with each line containing an image file name in data/your_dataset/*. (One way to generate this file is to run the following command in the data/ directory: ls your_dataset/ > your_dataset.txt.) This file specifies the order of training. If this file is not found, then a random order will be used.

Train on celebA:

python main.py --experiment_name celebA_demo --dataset celebA --train True

Train on your dataset:

python main.py --experiment_name your_dataset_demo --dataset your_dataset --train True

The --dataset flag accepts whatever dataset folder you want to use in the data/ directory.

Use TensorBoard to visualize losses and generated images.