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.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2023-10-06 UTC."],[],[]]