Computes the sum of elements across dimensions of a SparseTensor.
This Op takes a SparseTensor and is the sparse counterpart to
tf.reduce_sum(). In contrast to SparseReduceSum, this Op returns a
SparseTensor.
Reduces `sp_input` along the dimensions given in `reduction_axes`. Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained with length 1.
If `reduction_axes` has no entries, all dimensions are reduced, and a tensor with a single element is returned. Additionally, the axes can be negative, which are interpreted according to the indexing rules in Python.
Nested Classes
| class | SparseReduceSumSparse.Options | Optional attributes for SparseReduceSumSparse
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|
Constants
| String | OP_NAME | The name of this op, as known by TensorFlow core engine |
Public Methods
| static <T extends TType> SparseReduceSumSparse<T> | |
| static SparseReduceSumSparse.Options |
keepDims(Boolean keepDims)
|
| Output<TInt64> | |
| Output<TInt64> | |
| Output<T> |
Inherited Methods
Constants
public static final String OP_NAME
The name of this op, as known by TensorFlow core engine
Public Methods
public static SparseReduceSumSparse<T> create (Scope scope, Operand<TInt64> inputIndices, Operand<T> inputValues, Operand<TInt64> inputShape, Operand<TInt32> reductionAxes, Options... options)
Factory method to create a class wrapping a new SparseReduceSumSparse operation.
Parameters
| scope | current scope |
|---|---|
| inputIndices | 2-D. `N x R` matrix with the indices of non-empty values in a SparseTensor, possibly not in canonical ordering. |
| inputValues | 1-D. `N` non-empty values corresponding to `input_indices`. |
| inputShape | 1-D. Shape of the input SparseTensor. |
| reductionAxes | 1-D. Length-`K` vector containing the reduction axes. |
| options | carries optional attributes values |
Returns
- a new instance of SparseReduceSumSparse
public static SparseReduceSumSparse.Options keepDims (Boolean keepDims)
Parameters
| keepDims | If true, retain reduced dimensions with length 1. |
|---|