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name: vae_image_proxy_batch description: Create deterministic tiny image batches for bounded VAE/AEVB recovery experiments when full image datasets are unavailable or too expensive.

VAE Image Proxy Batch

Use this skill when a VAE recovery needs image-shaped unsupervised observations but must remain bounded. Do not use it to claim full MNIST or CIFAR reproduction; it produces a declared synthetic proxy batch.

Inputs

• batch_size: number of examples, default 8.
• height, width: image size, default 8x8.
• seed: deterministic ordering seed.
• Optional output path for JSON.

Outputs

• JSON with dataset, split, seed, shape, value_range, synthetic_proxy, and images; no labels are emitted because the VAE objective is unsupervised.
• Images are nested lists with shape [batch, 1, height, width] and values in [0, 1].

Workflow

1. Select structured binary patterns: bars, diagonals, checkerboards, and squares.
2. Stack examples as single-channel images.
3. Record metadata declaring synthetic reduced-proxy status.
4. Pass the JSON to an AEVB/VAE core step.

Validation

Run:

python scripts/create_proxy_batch.py --output /tmp/vae_proxy_batch.json
python tests/test_proxy_batch.py

Limitations

This skill supplies mechanism-faithful image-like data only. It does not download MNIST/CIFAR and does not provide labels.