A transformation that batches ragged elements into tf.sparse.SparseTensors.
tf.data.experimental.dense_to_sparse_batch(
batch_size, row_shape
)
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])
}
Args |
|---|
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.
|
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TensorFlow 1 version
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