DynamicPreprocessingPipeline

Chains Keras preprocessing layers over a dictionary of features.

Every layer is addressed by its name. When the pipeline runs, a layer reads its input from the entry that shares its name if that entry exists, and otherwise from the output of the layer that precedes it. The result is always written back under the layer's own name, so intermediate results stay available to later layers and to the caller.

That single rule covers both common- shapes: * supplying one entry per layer runs the layers independently over their own inputs; * supplying only the first layer's entry chains the layers, each one consuming what the previous layer produced.

Examples

```python
pipeline = DynamicPreprocessingPipeline(
    [ScalingLayer(name="scaling"), LogLayer(name="log")]
)
# "log" is absent from the data, so it consumes the scaling output.
out = pipeline.transform({"scaling": tf.constant([[1.0], [2.0]])})
out["scaling"], out["log"]
```

Constructor

__init__(self, layers: list) -> None

Initializes the pipeline with a list of preprocessing layers.

Parameters- layers (list): A list of TensorFlow preprocessing layers. Each layer

    must have a unique ``name``, which doubles as the key it reads
    from and writes to.

Raises

• ValueError: If two layers share the same name.

---

initialize_and_transform

initialize_and_transform(self, features: dict) -> dict

Applies every layer to the feature dictionary.

Kept as an alias- of: meth:transform for backwards compatibility.

Parameters

features (dict): Mapping of feature names to tensors.

Returns

• dict: The input entries plus one entry per layer.

---

process

process(self, dataset: tensorflow.python.data.ops.dataset_ops.DatasetV2) -> tensorflow.python.data.ops.dataset_ops.DatasetV2

Processes the dataset through the pipeline using the tf.data API.

Parameters- dataset (tf.data.Dataset): A dataset whose elements are dictionaries

    of features.

Returns

tf.data.Dataset: The processed dataset, with each layer's output
    stored under the layer's name.

---

transform

transform(self, features: dict) -> dict

Applies every layer to the feature dictionary.

Parameters- features (dict): Mapping of feature names to tensors. It is not

    modified; a new dictionary is returned.

Returns

• dict: The input entries plus one entry per layer, holding that layer's output.

Raises

• KeyError: If a layer has no entry to read from, i.e. neither its own key nor the previous layer's output is available.

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