This is the reduction operation for the elementwise tf.sparse.add op.
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
keepdims is true, the rank of the tensor is reduced by 1 for each entry in
reduction_axes. If keepdims 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,
similar to the indexing rules in Python.
For example
'x' represents [[1, ?, 1]
[?, 1, ?]]
where ? is implicitly-zero.
x=tf.sparse.SparseTensor([[0,0],[0,2],[1,1]],[1,1,1],[2,3])tf.sparse.reduce_sum(x)<tf.Tensor:shape=(),dtype=int32,numpy=3>tf.sparse.reduce_sum(x,0)<tf.Tensor:shape=(3,),dtype=int32,numpy=array([1,1,1],dtype=int32)>tf.sparse.reduce_sum(x,1)# Can also use -1 as the axis<tf.Tensor:shape=(2,),dtype=int32,numpy=array([2,1],dtype=int32)>tf.sparse.reduce_sum(x,1,keepdims=True)<tf.Tensor:shape=(2,1),dtype=int32,numpy=array([[2],[1]],dtype=int32)>tf.sparse.reduce_sum(x,[0,1])<tf.Tensor:shape=(),dtype=int32,numpy=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.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2022-11-04 UTC."],[],[]]