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name: training-and-inference description: "Configure tf_unet model graphs, training loops, checkpoints, predictions, and visual outputs." disable-model-invocation: true metadata: disco-role: operating license: GPL 3.0

training-and-inference

Use this sub-skill when the task is about Unet, Trainer, graph construction, losses, optimizers, checkpoints, prediction, accuracy, or image-visualization helpers.

This is the sub-skill for the TensorFlow 1.x model graph. If the task is really about input pipelines, file naming, or dataset loaders, switch to ../data-providers-and-launchers/SKILL.md.

Start here

• Read references/api-reference.md for signatures, tensor shapes, and return values.
• Read references/workflows.md for toy training and restore recipes.
• Run scripts/smoke_train_restore.py when you need a tiny train/save/restore/predict smoke.
• If the environment itself looks suspicious, run the root smoke helper ../../scripts/check_tf_unet_env.py first.

Core decisions

• Use layers and features_root to size the U-Net. VALID convolutions shrink the output and create an offset.
• Use cost="cross_entropy" for the default one-hot segmentation setup.
• Use cost="dice_coefficient" only when the task needs that segmentation-style objective.
• Pass class_weights or regularizer through cost_kwargs when the workflow needs imbalance handling or L2.
• Keep summaries=False for tiny inspection runs unless you explicitly want TensorBoard outputs.
• Crop labels with util.crop_to_shape(label, prediction.shape) before comparing against predictions.

What to mention in answers

• The Unet constructor resets the default graph and prepares placeholders for x, y, and keep_prob.
• Trainer.train(...) expects a callable data provider that returns training and verification batches.
• Trainer.train(...) writes checkpoint and prediction outputs under the paths you provide.
• predict(...) restores from a checkpoint base path such as model.ckpt.
• error_rate(...) compares argmax classes across the channel axis.

Bundled references

• references/api-reference.md — read for the public functions, classes, and shape contracts.
• references/workflows.md — read for the tiny smoke, checkpoint, and training recipes.
• references/troubleshooting.md — read when you hit TF1, shape, checkpoint, or loss-selection issues.

Bundled script

• scripts/smoke_train_restore.py — run a one-step synthetic training flow when you want to verify the training loop and checkpoint restore path.

Common safety notes

• Keep the smoke tiny and temporary. A long training run is not needed to answer routing or API questions.
• Do not assume TensorFlow 2 eager execution. This package is a graph/session workflow.
• If a checkpoint restore fails after changing layers or features_root, treat the checkpoint shape as stale and rebuild the graph.