Computes the sum of elements across dimensions of a SparseTensor.
tf.raw_ops.SparseReduceSumSparse(
input_indices,
input_values,
input_shape,
reduction_axes,
keep_dims=False,
name=None
)
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.
Args |
input_indices
|
A Tensor of type int64 .
2-D. N x R matrix with the indices of non-empty values in a
SparseTensor, possibly not in canonical ordering.
|
input_values
|
A Tensor . Must be one of the following types: float32 , float64 , int32 , uint8 , int16 , int8 , complex64 , int64 , qint8 , quint8 , qint32 , bfloat16 , qint16 , quint16 , uint16 , complex128 , half , uint32 , uint64 .
1-D. N non-empty values corresponding to input_indices .
|
input_shape
|
A Tensor of type int64 .
1-D. Shape of the input SparseTensor.
|
reduction_axes
|
A Tensor of type int32 .
1-D. Length-K vector containing the reduction axes.
|
keep_dims
|
An optional bool . Defaults to False .
If true, retain reduced dimensions with length 1.
|
name
|
A name for the operation (optional).
|
Returns |
A tuple of Tensor objects (output_indices, output_values, output_shape).
|
output_indices
|
A Tensor of type int64 .
|
output_values
|
A Tensor . Has the same type as input_values .
|
output_shape
|
A Tensor of type int64 .
|