T
- data type for output()
output@Operator(group="sparse") public final class SparseSegmentSum<T extends Number> extends PrimitiveOp implements Operand<T>
Read [the section on segmentation](https://tensorflow.org/api_docs/python/tf/math#Segmentation) for an explanation of segments.
Like `SegmentSum`, but `segment_ids` can have rank less than `data`'s first dimension, selecting a subset of dimension 0, specified by `indices`.
For example:
c = tf.constant([[1,2,3,4], [-1,-2,-3,-4], [5,6,7,8]])
# Select two rows, one segment.
tf.sparse_segment_sum(c, tf.constant([0, 1]), tf.constant([0, 0]))
# => [[0 0 0 0]]
# Select two rows, two segment.
tf.sparse_segment_sum(c, tf.constant([0, 1]), tf.constant([0, 1]))
# => [[ 1 2 3 4]
# [-1 -2 -3 -4]]
# Select all rows, two segments.
tf.sparse_segment_sum(c, tf.constant([0, 1, 2]), tf.constant([0, 0, 1]))
# => [[0 0 0 0]
# [5 6 7 8]]
# Which is equivalent to:
tf.segment_sum(c, tf.constant([0, 0, 1]))
operation
Modifier and Type | Method and Description |
---|---|
Output<T> |
asOutput()
Returns the symbolic handle of a tensor.
|
static <T extends Number,U extends Number> |
create(Scope scope,
Operand<T> data,
Operand<U> indices,
Operand<Integer> segmentIds)
Factory method to create a class wrapping a new SparseSegmentSum operation.
|
Output<T> |
output()
Has same shape as data, except for dimension 0 which
has size `k`, the number of segments.
|
equals, hashCode, op, toString
public static <T extends Number,U extends Number> SparseSegmentSum<T> create(Scope scope, Operand<T> data, Operand<U> indices, Operand<Integer> segmentIds)
scope
- current scopedata
- indices
- A 1-D tensor. Has same rank as `segment_ids`.segmentIds
- A 1-D tensor. Values should be sorted and can be repeated.public Output<T> output()
public Output<T> asOutput()
Operand
Inputs to TensorFlow operations are outputs of another TensorFlow operation. This method is used to obtain a symbolic handle that represents the computation of the input.
asOutput
in interface Operand<T extends Number>
OperationBuilder.addInput(Output)
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