tf.sparse_reduce_sum( sp_input, axis=None, keep_dims=False, reduction_axes=None )
See the guide: Sparse Tensors > Reduction
Computes the sum of elements across dimensions of a SparseTensor.
This Op takes a SparseTensor and is the sparse counterpart to
tf.reduce_sum(). In particular, this Op also returns a dense
instead of a sparse one.
sp_input along the dimensions given in
keep_dims is true, the rank of the tensor is reduced by 1 for each entry in
keep_dims is true, the reduced dimensions are retained
with length 1.
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.
# '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, keep_dims=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.
keep_dims: If true, retain reduced dimensions with length 1.
reduction_axes: Deprecated name of axis.
The reduced Tensor.