You can turn a small GAN example into a command-line GitHub project by giving it a Python entry point, declaring its dependencies, exposing training settings as arguments, and documenting how to run it. The 2018 GAN-Project-2018 repository demonstrates that workflow, but its TensorFlow 1.x-era code should be treated as a historical example—not as a project guaranteed to run unchanged with current TensorFlow.
What the project does
A generative adversarial network (GAN) trains two networks in competition. The generator turns a latent input into a candidate image; the discriminator receives image-shaped input and tries to distinguish real examples from generated ones. As training proceeds, the generator aims to make candidates that are harder for the discriminator to reject. The example uses MNIST-style 28 × 28 image dimensions.
The repository’s value is as much in its project structure as in the model: it separates dependencies, puts execution in main.py, accepts settings from the command line, and records run information for TensorBoard. It does not establish a particular accuracy, training speed, or image-quality score, so do not use it as a benchmark.
What you need before running it
- Git and a shell, such as Terminal on macOS/Linux or a shell in Windows.
- Python and an environment in which you can install the project’s dependencies.
- The repository’s
requirements.txt, which lists TensorFlow, NumPy, Matplotlib, Keras, and pandas.
The 2018 implementation calls TensorFlow 1.x APIs including tf.Session, tf.layers, tf.contrib.layers.flatten, tf.reset_default_graph, and tf.variable_scope. Those calls identify the code as legacy TensorFlow, not a drop-in TensorFlow 2 example. The dependency list alone does not specify a compatible set of package versions, so installing current packages and expecting the old script to work is not a reliable reproduction plan.
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Clone the repository and inspect its command-line interface
- Clone the project and enter its directory:
git clone https://github.com/RubensZimbres/GAN-Project-2018cd GAN-Project-2018 - Review
requirements.txtand the repository’s run instructions before installing anything. The original workflow uses conda to install the listed requirements. Because the code depends on TensorFlow 1.x-era APIs and the source material does not establish compatible package versions, use an isolated environment and do not assume a fresh, current environment will satisfy it. - Inspect the parser in
main.pyor trypython main.py --helpto see the exact argument names accepted by the checked-out script. Its documented settings include epoch count, learning rate, sample size, generator hidden size, discriminator hidden size, and an operating-system login argument. - Run
python main.pywith the epoch, learning-rate, and login arguments required by that script. Use the spellings and values shown by its parser or repository instructions; they are implementation-specific.
Argument parsing makes a run easier to reproduce than editing constants in the source: the settings are visible in the command you launch. Record the command, environment, and code revision together if you need to compare runs. A login argument is a parameter exposed by this particular example; it is not a general requirement for training GANs.
What to expect during training and in TensorBoard
The project records generator and discriminator losses, generated and classified images, the TensorFlow graph, and weight histograms as summaries. Losses can help reveal whether training is changing, while image summaries let you inspect generated outputs over time. The graph and weight histograms expose model structure and parameter distributions for debugging; none of these summaries by itself proves that the model is producing high-quality images.
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- Use scikit-learn to track an example ML project end to end
- Explore several models, including support vector machines, decision trees, random forests, and ensemble methods
- Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection
- Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers
- Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning
In the documented workflow, an image window appears during the run. After you close it, TensorBoard starts and can be opened in a browser. Follow the address printed by the running process and use the project’s configured log directory. The repository-specific directory and browser address are not established here, so do not assume a fixed path or port.
Choose between reproducing the example and using current TensorFlow
There are two different goals: running the historical code as written, or building a maintainable new project with current APIs. Mixing them—installing a modern TensorFlow release and then expecting the old calls to work—creates avoidable compatibility problems.
| Approach | API compatibility | Installation effort | Reproducibility and observability | Compute |
|---|---|---|---|---|
| Run the 2018 repository | Uses TensorFlow 1.x-era APIs; do not assume compatibility with TensorFlow 2. | Requires finding an environment compatible with the legacy code and its dependencies; exact package versions are not stated. | Includes command-line settings and TensorBoard summaries for losses, images, graph structure, and weights. | Runtime is not reported; no speed claim is established. |
| Rewrite for TensorFlow 2/Keras | Use current TensorFlow/Keras APIs rather than the legacy session, contrib, and variable-scope calls. | TensorFlow installation guidance documents pip install tensorflow for CPU use and tensorflow[and-cuda] for supported Linux/WSL2 GPU use; confirm current platform support before installing. |
Preserve the same project practices: explicit arguments, documented dependencies, and summaries for the values and images you want to inspect. | CPU execution is an option; GPU execution depends on a supported environment. No project-specific timing is available. |
| Use a browser notebook such as Colab | Use a notebook tutorial designed for the TensorFlow version it specifies, rather than assuming it runs the legacy repository unchanged. | Browser-based Colab tutorials can avoid local TensorFlow installation. | Useful for experimentation, but capture code, settings, and dependencies if you need to reproduce a run elsewhere. | Available compute depends on the notebook environment; no guarantee of a particular accelerator or runtime is established. |
TensorFlow’s official DCGAN tutorial provides a current conceptual reference: it trains a generator and discriminator on handwritten-digit images, and the notebook setup shown in the cited material reports TensorFlow 2.17.0. That version is specific to the reported notebook setup, not a promise about a current installation. For GPU use, the cited installation guidance says native-Windows GPU support ends after TensorFlow 2.10; later GPU workflows use WSL2 or another supported path. Installation and platform guidance can change, so check the current TensorFlow installation instructions for your system before following those commands.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Make the project reproducible
A GitHub project is more useful when another person can understand not just how to start it, but what the run requires and what it produces. For this GAN example, check that the repository communicates:
- Which Python and TensorFlow environment is compatible with the code, including package versions where they are known.
- Which command-line arguments control the run and what each parameter means.
- Where generated images and TensorBoard event data are saved, and how to view them.
- That the original implementation is based on TensorFlow 1.x APIs, if retaining the legacy code.
If you rewrite the model, keep the generator/discriminator roles and experiment settings understandable, then verify that summaries actually appear in TensorBoard. For formal GAN evaluation, TF-GAN documents Inception Score, Frechet Distance, and Kernel Distance as possible metrics. The repository does not report those metrics, and adding a metric to a project is not the same as establishing a result.
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