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README.md

      1 # GAN with TensorFlow eager execution
      2 
      3 A simple Generative Adversarial Network (GAN) example using eager execution.
      4 The discriminator and generator networks each contain a few convolution and
      5 fully connected layers.
      6 
      7 Other eager execution examples can be found under the parent directory.
      8 
      9 ##  Content
     10 
     11 - `mnist.py`: Model definitions and training routines.
     12 - `mnist_test.py`: Benchmarks for training and using the models using eager
     13 execution.
     14 - `mnist_graph_test.py`: Benchmarks for training and using the models using
     15 graph execution. The same model definitions and loss functions are used in
     16 all benchmarks.
     17 
     18 
     19 ## To run
     20 
     21 - Make sure you have installed TensorFlow 1.5+ or the latest `tf-nightly`
     22 or `tf-nightly-gpu` pip package in order to access the eager execution feature.
     23 
     24 - Train model. E.g.,
     25 
     26   ```bash
     27   python mnist.py
     28   ```
     29   
     30   Use `--output_dir=<DIR>` to direct the script to save TensorBoard summaries
     31   during training. Disabled by default.
     32   
     33   Use `--checkpoint_dir=<DIR>` to direct the script to save checkpoints to
     34   `<DIR>` during training. DIR defaults to /tmp/tensorflow/mnist/checkpoints/.
     35   The script will load the   latest saved checkpoint from this directory if
     36   one exists.
     37   
     38   Use `-h` for other options.
     39