TensorFlow 1 version
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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 = 5tf.ragged.stack_dynamic_partitions(data, partitions, num_partitions)<tf.RaggedTensor [[b'b'], [], [b'c', b'd'], [b'a', b'e'], []]>
Args | |
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data
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A Tensor or RaggedTensor containing the values to stack.
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partitions
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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.
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num_partitions
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An int32 or int64 scalar specifying the number of
partitions to output. This determines the number of rows in output.
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name
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A name prefix for the returned tensor (optional). |
Returns | |
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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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