layer_pipeline: Applies a series of layers to an input.

In keras3: R Interface to 'Keras'

Applies a series of layers to an input.

Description

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).

Usage

layer_pipeline(layers, name = NULL)

Arguments

layers	A list of layers.
name	String, name for the object

Examples

preprocessing_pipeline <- layer_pipeline(c(
  layer_auto_contrast(, ),
  layer_random_zoom(, 0.2),
  layer_random_rotation(, 0.2)
))

# `ds` is a tf.data.Dataset of images
ds <- tfdatasets::tensor_slices_dataset(1:100) |>
  tfdatasets::dataset_map(\(.x) {
    random_normal(c(28, 28))
  }) |>
  tfdatasets::dataset_batch(32)
  #|>
  # tfdatasets::dataset_take(4) |>
  # iterate() |> str()

preprocessed_ds <- ds |>
  tfdatasets::dataset_map(preprocessing_pipeline, num_parallel_calls = 4)

keras3 documentation built on June 8, 2025, 1:36 p.m.