tf.dynamic_partition

Partitions data into num_partitions tensors using indices from partitions.

tf.dynamic_partition(
    data: _atypes.TensorFuzzingAnnotation[TV_DynamicPartition_T],
    partitions: _atypes.TensorFuzzingAnnotation[_atypes.Int32],
    num_partitions: int,
    name=None
)

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 partitions.shape.

For example:

    # Scalar partitions.
    partitions = 1
    num_partitions = 2
    data = [10, 20]
    outputs[0] = []  # Empty with shape [0, 2]
    outputs[1] = [[10, 20]]

    # Vector partitions.
    partitions = [0, 0, 1, 1, 0]
    num_partitions = 2
    data = [10, 20, 30, 40, 50]
    outputs[0] = [10, 20, 50]
    outputs[1] = [30, 40]

See dynamic_stitch for an example on how to merge partitions back.

  • InvalidArgumentError in following cases:
  • If partitions is not in range [0, num_partiions)
  • If partitions.shape does not match prefix of data.shape argument.

data A Tensor.
partitions A Tensor of type int32. Any shape. Indices in the range [0, num_partitions).
num_partitions An int that is >= 1. The number of partitions to output.
name A name for the operation (optional).

A list of num_partitions Tensor objects with the same type as data.