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name: training-and-persistence description: "Train, evaluate, predict, save, restore, and debug TFLearn models with DNN, Trainer, TrainOp, Evaluator, callbacks, validation, TensorBoard, checkpoints, weights, and variable-scope restore mapping." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Training and Persistence

Use this sub-skill when the task is about running a TFLearn model after the network graph has been built: fitting arrays or feed dictionaries, evaluating metrics, predicting outputs, checkpointing, restoring into compatible or renamed scopes, inspecting layer weights, or diagnosing failed training/persistence.

Load This For

• tflearn.DNN.fit, predict, evaluate, save, load, get_weights, or set_weights workflows.
• Multi-input or multi-target fitting where list ordering or named feed dictionaries must be correct.
• Lower-level TensorFlow graph training with tflearn.TrainOp, tflearn.Trainer, and tflearn.Evaluator.
• Validation splits/sets, snapshot_epoch, snapshot_step, checkpoint_path, best_checkpoint_path, max_checkpoints, run_id, and TensorBoard log directories.
• Checkpoint stem/file confusion, missing input_data or regression, empty train-op collections, graph/session reuse bugs, and scope-renamed restores with scope_for_restore or variable_name_map.

Do Not Use This For

• Choosing or cataloging layer functions, activations, losses, metrics, or merge operators in depth. Use the layer/operator sub-skill for that.
• Loading datasets, CSV/text/image preprocessing, sequence padding, or data augmentation design. Use the data/input-pipeline sub-skill for that.
• Long image, NLP, reinforcement-learning, or recipe-selection guidance. Use the advanced-recipe sub-skill when a task is primarily about model family choice rather than training mechanics.

Quick Start

1. Use a TensorFlow 1.x-compatible runtime. The verified stack for this skill is TFLearn 0.5.0 with TensorFlow 1.15.5 and NumPy 1.18.5. TensorFlow 2.x modern imports are not compatible with this checkout without additional porting.
2. Build a fresh graph for each independent model in scripts/notebooks: with tf.Graph().as_default(): ... or tf.reset_default_graph() before a rebuild.
3. Include at least one tflearn.input_data(...) layer and one tflearn.regression(...) estimator before constructing tflearn.DNN(...).
4. Prefer named dictionaries for non-trivial feeds: model.fit({'input': X}, {'target': Y}, ...). Lists are matched by input and target creation order.
5. Put tensorboard_dir, checkpoint_path, manual save stems, and smoke-test --model-dir under an explicit experiment/temp directory, not an implicit current working directory.

Run the bundled no-network smoke from this sub-skill directory:

python scripts/tiny_dnn_regression_smoke.py --help
python scripts/tiny_dnn_regression_smoke.py --epochs 5
python scripts/tiny_dnn_regression_smoke.py --epochs 5 --model-dir /tmp/tflearn-smoke

Expected smoke output includes a prediction shape and either a checkpoint stem or <not saved> when --no-save is used.

References

• API reference: signatures, input forms, callbacks, DNN, Trainer, TrainOp, Evaluator, and weights APIs.
• Workflows: ready-to-adapt training, validation, prediction, multi-input/multi-target, custom Trainer, and callback patterns.
• Checkpointing and weights: checkpoint stems, snapshots, manual save/load, weights_only, layer variables, and scope mapping.
• Troubleshooting: concrete error signals and fixes for feeds, graph/session reuse, validation, TensorBoard, and restore failures.

Operating Checklist

Before telling a user a training or restore workflow is ready, check:

• Graph isolation: one fresh graph per independent model rebuild.
• Collections: tf.GraphKeys.INPUTS, TARGETS, and TRAIN_OPS are populated as expected.
• Feed contract: list ordering is intentional, or dictionaries use known input and target names/Tensor keys.
• Runtime paths: checkpoint/log directories are explicit and writable.
• Validation: validation_set/val_feed_dicts match the same feed form as training data; validation_batch_size is set when validation memory differs.
• Restore contract: checkpoint stem is used, graph architecture and variable names match, or scope_for_restore/variable_name_map is supplied.
• Persistence proof: at least one prediction/evaluation runs before and after save/load when the task depends on restored weights.