tf.ragged.stack_dynamic_partitions
Stacks dynamic partitions of a Tensor or RaggedTensor.
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)
<tf.RaggedTensor [[b'b'], [], [b'c', b'd'], [b'a', b'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. |
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/ragged/stack_dynamic_partitions