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# 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.,
```bash
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.
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