dcgan-tfslim

Deep Convolutional Generative Adversarial Networks (DCGAN) implemented with 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.

Dependencies

TensorFlow 1.1.0 or higher is required. 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

Run on celebA

Setup

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

Train

Train on celebA:

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

Check out the samples directory to see samples during training.

Test/Visualize

Sample and visualize images on trained model:

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

Samples and visualizations are saved to the samples directory.

Run on Custom Dataset

Setup

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

Train

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.
• Check out the samples directory to see samples during training.

Test/Visualize

Sample and visualize images on trained model:

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

Samples and visualizations are saved to the samples directory.

TensorBoard

Use TensorBoard to visualize losses and generated images.

Extending

Implement your own generator and discriminator by looking at generator.py and discriminator.py. This enables extending to different image resolutions and different tasks, for instance.