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name: data-input-pipelines description: "Prepare, validate, and attach TFLearn data feeds, preprocessing, and augmentation objects." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Data Input Pipelines

Use this sub-skill when the task is about making data acceptable to TFLearn before training or prediction: CSV/tabular loading, categorical labels, sequence/text vectorization, bundled dataset loaders, NumPy/list/HDF5/Dask feeds, and DataPreprocessing/DataAugmentation objects attached through tflearn.input_data.

Start here

1. Classify the input source and expected tensor shape using data formats.
2. For tabular CSV, run the bundled validator before building a model:

   python scripts/validate_tflearn_tabular_data.py --csv data.csv --target-column 0 --categorical-labels --n-classes 2
   

3. Follow the appropriate recipe in workflows for CSV, sequences/text, dataset loaders, HDF5, Dask, image preprocessing, or multi-input feeds.
4. Check exact function signatures and gotchas in API reference.
5. If import, dtype, optional dependency, download, or shape errors occur, use troubleshooting.

Operating rules

• Always separate data preparation from architecture work. This sub-skill may decide input_data(shape=...), placeholder dtype, and label width; route hidden layers, losses beyond label compatibility, and model architecture choices to layers-and-ops.
• Always verify the first dimension of every feed is the sample axis and that all inputs/targets have the same number of samples.
• Convert feature arrays to numeric NumPy arrays, normally np.float32, before fitting unless a specific placeholder uses another dtype.
• For classification with categorical_crossentropy, provide one-hot labels using loader one_hot=True, load_csv(..., categorical_labels=True, n_classes=...), or tflearn.data_utils.to_categorical.
• Attach preprocessing and augmentation at input_data; preprocessing applies during training and evaluation/prediction, while augmentation applies during training only.
• Keep training loops, checkpoints, save/load, callbacks, and DNN.fit persistence behavior outside this sub-skill except for the feed-shape requirements needed by fit.

Quick routing map

User need	Use
CSV target column, ignored columns, one-hot labels, object dtype	data formats and bundled validator
Titanic-style workflow	workflows
IMDB/list-of-token sequences or character text generation inputs	workflows
MNIST/CIFAR/IMDB/Titanic/flower/SVHN loaders and downloads	data formats
HDF5 or Dask arrays passed to DNN.fit	workflows
Image preprocessing/augmentation	workflows
Missing optional packages, np.bool, TensorFlow 2 import failures	troubleshooting