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tf.data.experimental.dense_to_sparse_batch

A transformation that batches ragged elements into tf.sparse.SparseTensors.

Like Dataset.padded_batch(), this transformation combines multiple consecutive elements of the dataset, which might have different shapes, into a single element. The resulting element has three components (indices, values, and dense_shape), which comprise a tf.sparse.SparseTensor that represents the same data. The row_shape represents the dense shape of each row in the resulting tf.sparse.SparseTensor, to which the effective batch size is prepended. For example:

# NOTE: The following examples use `{ ... }` to represent the
# contents of a dataset.
a = { ['a', 'b', 'c'], ['a', 'b'], ['a', 'b', 'c', 'd'] }

a.apply(tf.data.experimental.dense_to_sparse_batch(
    batch_size=2, row_shape=[6])) ==
{
    ([[0, 0], [0, 1], [0, 2], [1, 0], [1, 1]],  # indices
     ['a', 'b', 'c', 'a', 'b'],                 # values
     [2, 6]),                                   # dense_shape
    ([[0, 0], [0, 1], [0, 2], [0, 3]],
     ['a', 'b', 'c', 'd'],
     [1, 6])
}

batch_size A tf.int64 scalar tf.Tensor, representing the number of consecutive elements of this dataset to combine in a single batch.
row_shape A tf.TensorShape or tf.int64 vector tensor-like object representing the equivalent dense shape of a row in the resulting tf.sparse.SparseTensor. Each element of this dataset must have the same rank as row_shape, and must have size less than or equal to row_shape in each dimension.

A Dataset transformation function, which can be passed to tf.data.Dataset.apply.