tf.ragged.stack_dynamic_partitions

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Stacks dynamic partitions of a Tensor or RaggedTensor.

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.ragged.stack_dynamic_partitions, `tf.compat.v2.ragged.stack_dynamic_partitions`

tf.ragged.stack_dynamic_partitions(
    data, partitions, num_partitions, name=None
)

Returns a RaggedTensor output with num_partitions rows, where the row output[i] is formed by stacking all slices data[j1...jN] such that partitions[j1...jN] = i. Slices of data are stacked in row-major order.

If num_partitions is an int (not a Tensor), then this is equivalent to tf.ragged.stack(tf.dynamic_partition(data, partitions, num_partitions)).

Example:

>>> data           = ['a', 'b', 'c', 'd', 'e']
>>> partitions     = [  3,   0,   2,   2,   3]
>>> num_partitions = 5
>>> tf.ragged.stack_dynamic_partitions(data, partitions, num_partitions)
<RaggedTensor [['b'], [], ['c', 'd'], ['a', 'e'], []]>

Args
data	A Tensor or RaggedTensor containing the values to stack.
partitions	An int32 or int64 Tensor or RaggedTensor specifying the partition that each slice of data should be added to. partitions.shape must be a prefix of data.shape. Values must be greater than or equal to zero, and less than num_partitions. partitions is not required to be sorted.
num_partitions	An int32 or int64 scalar specifying the number of partitions to output. This determines the number of rows in output.
name	A name prefix for the returned tensor (optional).

Returns
A RaggedTensor containing the stacked partitions. The returned tensor has the same dtype as data, and its shape is [num_partitions, (D)] + data.shape[partitions.rank:], where (D) is a ragged dimension whose length is the number of data slices stacked for each partition.