---

name: training description: "Plans and validates Fast Style Transfer style.py training runs, VGG/COCO assets, checkpoints, loss weights, and long GPU-oriented training workflows." disable-model-invocation: true metadata: disco-role: operating license: CC BY-NC 4.0

Training

Use this sub-skill when the user wants to train a Fast Style Transfer transform network, prepare the bundled training runtime inputs, understand loss-weight/checkpoint options, debug VGG/COCO/data-layout problems, or decide whether a training run is safe to launch.

Read and run

• Read references/training-workflow.md for the end-to-end training workflow, asset layout, command templates, outputs, and checkpoint handoff.
• Read references/cli-reference.md for verified the bundled training runtime flags/defaults and optimize.optimize behavior.
• Read references/troubleshooting.md when paths, TensorFlow, VGG, data shapes, performance, or checkpoint outputs fail.
• Run scripts/validate_training_inputs.py before a long training run. It validates paths and options, samples image files, and prints JSON; it never downloads data or trains.

When to use this route

Use training guidance for requests like:

• "Train a style transfer network from this painting."
• "What files do I need before running the bundled training runtime?"
• "Build a short sanity check before launching a GPU training job."
• "Why does the bundled training runtime say the VGG network or train path is missing?"
• "How do I choose --content-weight, --style-weight, --tv-weight, or checkpoint frequency?"

Route away from this sub-skill when the user already has a trained checkpoint and wants to apply it to images or videos:

• Still images/directories: ../image-stylization/SKILL.md
• Videos: ../video-stylization/SKILL.md

Training workflow summary

A normal training run needs:

1. A TensorFlow environment compatible with the script.
2. A writable checkpoint directory that already exists.
3. A style image readable by imageio/Pillow.
4. A content training directory containing images.
5. A VGG19 .mat file for perceptual losses.
6. Optional test image and test output directory for checkpoint previews.

Minimal command shape:

python sub-skills/training/scripts/run_training.py \
  --checkpoint-dir checkpoints/udnie \
  --style assets/style/udnie.jpg \
  --train-path data/train2014 \
  --vgg-path data/imagenet-vgg-verydeep-19.mat \
  --content-weight 1.5e1 \
  --checkpoint-iterations 1000 \
  --batch-size 20

Before launching, run the bundled validator with the same paths:

python sub-skills/training/scripts/validate_training_inputs.py \
  --checkpoint-dir checkpoints/udnie \
  --style assets/style/udnie.jpg \
  --train-path data/train2014 \
  --vgg-path data/imagenet-vgg-verydeep-19.mat \
  --content-weight 1.5e1 \
  --checkpoint-iterations 1000 \
  --batch-size 20

Key decisions

• Training corpus: COCO train2014 is the documented corpus, but any image directory can be used if the images are readable and representative.
• VGG asset: the bundled training runtime defaults to data/imagenet-vgg-verydeep-19.mat; pass --vgg-path explicitly when the asset lives elsewhere.
• Checkpoint directory: the directory must exist before the script runs. The script saves a TensorFlow checkpoint path ending in fns.ckpt inside it.
• Preview image: if --test is provided, --test-dir must also exist.
• Backend: GPU is not required for parser validation, but full training is normally a GPU workflow.
• Slow mode: --slow switches to direct pixel optimization for debugging; it is not the normal feed-forward training path.

Validation limits

The validator and parser help checks catch missing files, bad numeric values, and obvious data-layout issues. They do not prove that:

• VGG .mat contents are semantically correct.
• A TensorFlow checkpoint will be compatible with another TensorFlow version.
• Training will converge or produce a visually pleasing result.
• A GPU backend is installed unless TensorFlow actually sees GPU devices.

After a checkpoint is created, use the image or video sub-skills to consume it.