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name: deep-and-optional-integrations description: "Optional DeepActiveLearner, PyTorch/skorch MC-dropout, and Keras/TensorFlow integration guidance for modAL." disable-model-invocation: true metadata: disco-role: operating license: MIT

Deep and Optional Integrations

Use this sub-skill only when a modAL task explicitly involves optional deep-learning integrations: DeepActiveLearner, skorch-wrapped PyTorch estimators, Monte Carlo dropout query strategies from modAL.dropout, or legacy Keras/TensorFlow examples. These workflows are not part of the minimum classical scikit-learn active-learning path.

Read and run map

• Read references/deep-integrations.md when building or debugging DeepActiveLearner, skorch/PyTorch estimators, warm_start teaching, num_epochs/batch_size, or optional Keras/TensorFlow active-learning wrappers.
• Read references/mc-dropout-reference.md when using mc_dropout_bald, mc_dropout_mean_st, mc_dropout_max_entropy, mc_dropout_max_variationRatios, get_predictions, set_dropout_mode, dropout layer indexes, tensor/dict pools, or MC-dropout memory knobs.
• Read references/troubleshooting.md when imports fail, skorch initialization or partial_fit behavior is unclear, NumPy arrays are passed to MC dropout, a dropout layer index raises KeyError, or a user expects CUDA/Keras behavior that was not selected.
• Run scripts/dropout_inspection.py to perform a deterministic CPU-only import and dropout-layer-index inspection against the installed package. Use --help first; the script does not train, download data, require credentials, or write files.

Boundaries

• For ordinary ActiveLearner, Committee, CommitteeRegressor, bootstrapping, only_new=True with classical or Keras-like estimators, and learner setup not involving deep wrappers, use ../learners-and-committees/SKILL.md.
• For ordinary uncertainty, disagreement, ranked-batch, density, expected-error, multilabel, or custom query strategies that do not require MC dropout, use ../query-strategies/SKILL.md.
• Do not claim that Keras/TensorFlow, torchvision datasets, or CUDA are available unless the user's runtime has separately installed and smoke-tested them.

Fast routing cues

User signal	Next file
“DeepActiveLearner calls initialize”, “skorch NeuralNetClassifier”, “warm_start”, “partial_fit”	references/deep-integrations.md
“MC dropout BALD/entropy/variation ratios”, “dropout_layer_indexes”, “logits_adaptor”	references/mc-dropout-reference.md
“RuntimeError only dict or tensors supported”, “bad dropout layer index”, “missing torch/skorch/Keras”	references/troubleshooting.md
“Which dropout layer indexes are valid?”	scripts/dropout_inspection.py