tf.ragged.stack_dynamic_partitions
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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.
|
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Last updated 2023-03-17 UTC.
[null,null,["Last updated 2023-03-17 UTC."],[],[],null,["# tf.ragged.stack_dynamic_partitions\n\n\u003cbr /\u003e\n\n|------------------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.8.4/tensorflow/python/ops/ragged/ragged_array_ops.py#L569-L669) |\n\nStacks dynamic partitions of a Tensor or RaggedTensor.\n\n#### View aliases\n\n\n**Compat aliases for migration**\n\nSee\n[Migration guide](https://www.tensorflow.org/guide/migrate) for\nmore details.\n\n[`tf.compat.v1.ragged.stack_dynamic_partitions`](https://www.tensorflow.org/api_docs/python/tf/ragged/stack_dynamic_partitions)\n\n\u003cbr /\u003e\n\n tf.ragged.stack_dynamic_partitions(\n data, partitions, num_partitions, name=None\n )\n\nReturns a RaggedTensor `output` with `num_partitions` rows, where the row\n`output[i]` is formed by stacking all slices `data[j1...jN]` such that\n`partitions[j1...jN] = i`. Slices of `data` are stacked in row-major\norder.\n\nIf `num_partitions` is an `int` (not a `Tensor`), then this is equivalent to\n`tf.ragged.stack(tf.dynamic_partition(data, partitions, num_partitions))`.\n\n#### Example:\n\n data = ['a', 'b', 'c', 'd', 'e']\n partitions = [ 3, 0, 2, 2, 3]\n num_partitions = 5\n tf.ragged.stack_dynamic_partitions(data, partitions, num_partitions)\n \u003ctf.RaggedTensor [[b'b'], [], [b'c', b'd'], [b'a', b'e'], []]\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `data` | A `Tensor` or `RaggedTensor` containing the values to stack. |\n| `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. |\n| `num_partitions` | An `int32` or `int64` scalar specifying the number of partitions to output. This determines the number of rows in `output`. |\n| `name` | A name prefix for the returned tensor (optional). |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| 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`. ||\n\n\u003cbr /\u003e"]]
View source on GitHub