TensorFlow 1 version | View source on GitHub |
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 .
|