Pipeline layer

Original Link : https://keras.io/api/layers/preprocessing_layers/image_augmentation/pipeline/
Last Checked at : 2024-11-25

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`Pipeline` class

keras.layers.Pipeline(layers, name=None)

Applies a series of layers to an input.

This class is useful to build a preprocessing pipeline, in particular an image data augmentation pipeline. Compared to a Sequential model, Pipeline features a few important differences:

It’s not a Model, just a plain layer.
When the layers in the pipeline are compatible with tf.data, the pipeline will also remain tf.data compatible. That is to say, the pipeline will not attempt to convert its inputs to backend-native tensors when in a tf.data context (unlike a Sequential model).

Example

from keras import layers
preprocessing_pipeline = layers.Pipeline([
    layers.AutoContrast(),
    layers.RandomZoom(0.2),
    layers.RandomRotation(0.2),
])

# `ds` is a tf.data.Dataset
preprocessed_ds = ds.map(
    preprocessing_pipeline,
    num_parallel_calls=4,
)

Pipeline layer

Pipeline class

`Pipeline` class