GAN with TensorFlow eager execution

A simple Generative Adversarial Network (GAN) example using eager execution. The discriminator and generator networks each contain a few convolution and fully connected layers.

Other eager execution examples can be found under the parent directory.

Content

• mnist.py: Model definitions and training routines.
• mnist_test.py: Benchmarks for training and using the models using eager execution.
• mnist_graph_test.py: Benchmarks for training and using the models using graph execution. The same model definitions and loss functions are used in all benchmarks.

To run

• Make sure you have installed TensorFlow 1.5+ or the latest tf-nightly or tf-nightly-gpu pip package in order to access the eager execution feature.

• Train model. E.g.,

  python mnist.py
  

  Use --output_dir=<DIR> to direct the script to save TensorBoard summaries during training. Disabled by default.

  Use --checkpoint_dir=<DIR> to direct the script to save checkpoints to <DIR> during training. DIR defaults to /tmp/tensorflow/mnist/checkpoints/. The script will load the latest saved checkpoint from this directory if one exists.

  Use -h for other options.