tf.dynamic_partition( data, partitions, num_partitions, name=None )
See the guide: Tensor Transformations > Slicing and Joining
num_partitions tensors using indices from
For each index tuple
js of size
partitions.ndim, the slice
data[js, ...] becomes part of
outputs[partitions[js]]. The slices with
partitions[js] = i are placed in
outputs[i] in lexicographic order of
js, and the first dimension of
outputs[i] is the number of entries in
partitions equal to
i. In detail,
outputs[i].shape = [sum(partitions == i)] + data.shape[partitions.ndim:] outputs[i] = pack([data[js, ...] for js if partitions[js] == i])
data.shape must start with
# Scalar partitions. partitions = 1 num_partitions = 2 data = [10, 20] outputs =  # Empty with shape [0, 2] outputs = [[10, 20]] # Vector partitions. partitions = [0, 0, 1, 1, 0] num_partitions = 2 data = [10, 20, 30, 40, 50] outputs = [10, 20, 50] outputs = [30, 40]
dynamic_stitch for an example on how to merge partitions back.
int32. Any shape. Indices in the range
>= 1. The number of partitions to output.
name: A name for the operation (optional).
A list of
Tensor objects with the same type as
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.