Computes the sum of elements across dimensions of a SparseTensor.
tf.raw_ops.SparseReduceSum(
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 particular, this Op also returns a dense Tensor
instead of a sparse one.
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, 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 Tensor. Has the same type as input_values.
|