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name: tensorflow-examples description: "Routes TensorFlow SIGNS and NER example requests to the correct preprocessing, training, evaluation, hyperparameter search, and results workflows." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

TensorFlow Examples

Use this sub-skill for every TensorFlow workflow in the repository:

• vision on the SIGNS image dataset;
• NLP on the named-entity recognition dataset;
• preprocessing, training, evaluation, hyperparameter search, and result synthesis for either workflow.

The helper scripts in this sub-skill are safe wrappers around the repository's TensorFlow example commands. Future agents should use them instead of trying to reconstruct the working directory and flags from memory.

Read when

• the user mentions TensorFlow, tf.data, tf.contrib, SIGNS, hand signs, or image classification;
• the user mentions TensorFlow NER, vocab building, Kaggle CSV splitting, or sentence tagging;
• the user wants a train/evaluate/search/synthesize command for a TensorFlow example;
• the user needs TensorFlow 1.15-specific troubleshooting or data-layout guidance.

What this sub-skill covers

Vision route

• resizing the SIGNS dataset to 64x64;
• training the convolutional classifier;
• evaluating a saved checkpoint;
• launching the built-in learning-rate search;
• aggregating experiment metrics.

NLP route

• converting the Kaggle CSV into train/, dev/, and test/ text splits;
• building word and tag vocabularies;
• training the LSTM-based NER model;
• evaluating a saved checkpoint;
• launching the built-in learning-rate search;
• aggregating experiment metrics.

What is excluded

• PyTorch workflows.
• Any workflow that needs the PyTorch torch / torchvision stack.
• Repository-wide provenance or framework selection questions; those belong in the root skill.

Key bundled files

• references/workflows.md for copyable command sequences and data layouts.
• references/api-reference.md for verified local module signatures and helper behavior.
• references/troubleshooting.md for common TensorFlow setup and data issues.
• scripts/run_workflow.py for a safe command wrapper with --repo-root and --execute support.

Installation guidance

• Install the packages listed in ../../tensorflow/vision/requirements.txt when you need the vision workflow.
• Install the packages listed in ../../tensorflow/nlp/requirements.txt when you need the NER workflow.
• A single environment that satisfies both files is usually enough for the full TensorFlow sub-skill.
• TensorFlow 1.15 is the intended stack for these examples, so use a compatible Python version and keep the environment isolated.

Example install commands:

python -m pip install -r ../../tensorflow/vision/requirements.txt
python -m pip install -r ../../tensorflow/nlp/requirements.txt

Minimal check

Run the shared environment helper first:

python ../../scripts/check_env.py --frameworks tensorflow

If you need to confirm the local checkout as well, add --repo-root <repo-root>.

Workflow selection

• Use the vision route when the task mentions images, signs, resizing, or 64x64 SIGNS preprocessing.
• Use the NLP route when the task mentions sentences, tokens, tags, words, Kaggle CSV splitting, or vocabulary building.

If the user only asks for a command, read references/workflows.md first. If an import or runtime error appears, jump to references/troubleshooting.md.