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
False, or a
output_is_sparse is True, a gradient is not defined for this
function, so it can't be used in training models that need gradient descent.
sp_input along the dimensions given in
true, the rank of the tensor is reduced by 1 for each entry in
keepdims is true, the reduced dimensions are retained with length 1.
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.
# '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) ==> [, ] tf.sparse.reduce_sum(x, [0, 1]) ==> 3
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
SparseTensorinstead of a dense
name: A name for the operation (optional).
The reduced Tensor or the reduced SparseTensor if