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name: mnist-generators description: "Use, inspect, adapt, and troubleshoot Keras-GAN standalone MNIST-style generator scripts that do not require paired image-domain directories." disable-model-invocation: true metadata: disco-role: operating license: MIT

MNIST Generators

Use this sub-skill for Keras-GAN's standalone educational scripts that load Keras MNIST-like arrays directly instead of requiring paired image-domain folders. It covers GAN, DCGAN, CGAN, ACGAN, SGAN, InfoGAN, LSGAN, BGAN, BiGAN, Adversarial Autoencoder, CoGAN, DualGAN, WGAN, and WGAN-GP.

Do not start a full training run from a script's if __name__ == "__main__" defaults unless the user explicitly accepts long CPU/GPU training, dataset download/cache behavior, and generated file writes.

Route elsewhere

• CycleGAN, DiscoGAN, and Pix2Pix need image-domain or paired-image datasets: use the image-translation sub-skill.
• CCGAN, context encoder, PixelDA, and SRGAN need inpainting, domain-adaptation, restoration, super-resolution, or image-directory handling: use the domain-and-restoration sub-skill.
• Cross-cutting installation issues belong in the root Keras-GAN compatibility and troubleshooting references; this sub-skill records only the MNIST-generator-specific symptoms.

Fast workflow

1. Choose the script/class from references/model-api-reference.md. Check the train signature, default __main__ epoch count, output path, and special loss methods before adapting.

2. From this generated skill's root directory, run static inspection without importing Keras:

   python sub-skills/mnist-generators/scripts/model_inventory.py --repo-root /path/to/a/Keras-GAN-checkout
   python sub-skills/mnist-generators/scripts/model_inventory.py --repo-root /path/to/a/Keras-GAN-checkout --json
   

3. For dependency or constructor smoke checks, default to import-only and add --construct only after confirming a legacy Keras/TensorFlow runtime:

   python sub-skills/mnist-generators/scripts/dry_run_model.py --repo-root /path/to/a/Keras-GAN-checkout --model gan
   python sub-skills/mnist-generators/scripts/dry_run_model.py --repo-root /path/to/a/Keras-GAN-checkout --model dcgan --construct
   python sub-skills/mnist-generators/scripts/dry_run_model.py --repo-root /path/to/a/Keras-GAN-checkout --model wgan-gp
   

4. Follow references/workflows.md for safe adaptation, short smoke runs, output-directory handling, and model comparison.

5. Use references/troubleshooting.md when imports fail, WGAN-GP cannot find _Merge, images/ or saved_model/ is missing, Keras dataset downloads are blocked, or a run is taking too long.

Safety rules

• The bundled inventory script is static and safe: no Keras import, no network, no training, and no writes except optional shell redirection by the caller.
• The bundled dry-run script imports a selected source file and constructs a model only when --construct is supplied; it never calls train(...).
• The original scripts hard-code images/... and sometimes saved_model/... relative to the current working directory. Create those directories in a temporary run directory before any training smoke.
• WGAN-GP's RandomWeightedAverage uses a legacy private Keras _Merge API and a fixed batch dimension of 32; treat modern-Keras failures as compatibility diagnostics, not model-quality failures.