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name: advanced-model-recipes description: "Choose and adapt higher-level TFLearn recipes for vision, NLP, sequence generation, generative models, recommenders, RL, estimators, and custom TensorFlow graph integration." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Advanced Model Recipes

Use this sub-skill when a future agent needs to choose a TFLearn example family, adapt it to a safe local fixture, combine TFLearn with a custom TensorFlow graph, or diagnose advanced recipe-specific failures without reopening the source repository.

Route by Need

• For recipe selection across vision, NLP, sequence generation, generative models, recommenders, RL, and estimators, read Model Recipes.
• For custom TensorFlow graphs, tflearn.TrainOp, tflearn.Trainer, multiple optimizers, validation monitors, and graph collections, read TensorFlow Integration.
• For verified signatures and public API reminders used by these recipes, read API Reference.
• For downloads, optional dependencies, TensorFlow 1.x/2.x issues, expensive examples, GPU assumptions, SequenceGenerator, estimator, notebook, and plotting failures, read Troubleshooting.
• For a safe no-network custom graph smoke, run scripts/custom_trainer_smoke.py with --help first.

Safe Default Workflow

1. Confirm the runtime is a TensorFlow 1.x-compatible TFLearn environment. The verified CPU stack for this skill was TFLearn 0.5.0, TensorFlow 1.15.5, NumPy 1.18.5, and protobuf 3.20.3; CUDA is optional and unverified for this skill.
2. Do not run the original examples as validation. Many examples download datasets, open plots, run for hundreds of epochs, use Gym/Atari, or depend on TensorFlow contrib.
3. Pick a recipe from Model Recipes, then replace dataset loaders with tiny in-memory arrays that preserve shape, dtype, and label assumptions.
4. Shrink units, filters, image size, text length, epochs, and batch size. Prefer tensorboard_verbose=0, snapshot_epoch=False, and temporary output directories for smoke tests.
5. Validate graph construction and one tiny fit/predict or generate path before recommending long training.

Smoke Command

From this sub-skill directory:

python scripts/custom_trainer_smoke.py --help
python scripts/custom_trainer_smoke.py --epochs 2

A successful run prints an OK custom_trainer_smoke ... line with finite loss, accuracy, and prediction shape. If imports fail, the script prints a targeted TensorFlow/TFLearn compatibility message instead of attempting downloads.

Boundaries

• Detailed layer and operation catalogs belong in layers-and-ops.
• Routine DNN.fit, checkpoint, save/load, callback, and TensorBoard mechanics belong in training-and-persistence.
• Data loading, CSV/HDF5/Dask conversion, image preloading, preprocessing, and augmentation details belong in data-input-pipelines.
• This sub-skill owns higher-level architecture family selection, safe recipe adaptation, optional dependency gates, SequenceGenerator, estimators, recommender/RL caveats, and custom TensorFlow graph integration.