TensorFlow 1 version | View source on GitHub |
Computes the sum of elements across dimensions of a SparseTensor.
tf.sparse.reduce_sum(
sp_input, axis=None, keepdims=None, output_is_sparse=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
if output_is_sparse
is False
, or a SparseTensor
if output_is_sparse
is True
.
Reduces sp_input
along the dimensions given in axis
. Unless keepdims
is
true, the rank of the tensor is reduced by 1 for each entry in axis
. If
keepdims
is true, the reduced dimensions are retained with length 1.
If axis
has no entries, all dimensions are reduced, and a tensor
with a single element is returned. Additionally, the axes can be negative,
similar to the indexing rules in Python.
For example:
# 'x' represents [[1, ?, 1]
# [?, 1, ?]]
# where ? is implicitly-zero.
tf.sparse.reduce_sum(x) ==> 3
tf.sparse.reduce_sum(x, 0) ==> [1, 1, 1]
tf.sparse.reduce_sum(x, 1) ==> [2, 1] # Can also use -1 as the axis.
tf.sparse.reduce_sum(x, 1, keepdims=True) ==> [[2], [1]]
tf.sparse.reduce_sum(x, [0, 1]) ==> 3
Args | |
---|---|
sp_input
|
The SparseTensor to reduce. Should have numeric type. |
axis
|
The dimensions to reduce; list or scalar. If None (the
default), reduces all dimensions.
|
keepdims
|
If true, retain reduced dimensions with length 1. |
output_is_sparse
|
If true, returns a SparseTensor instead of a dense
Tensor (the default).
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
The reduced Tensor or the reduced SparseTensor if output_is_sparse is
True.
|