tf.sparse_reduce_sum(sp_input, reduction_axes=None, keep_dims=False)
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 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, similar to the indexing rules in Python.
For example:
1 2 3 4 5 6 7 8 | # 'x' represents [[1, ?, 1]# [?, 1, ?]]# where ? is implicitly-zero.tf.sparse_reduce_sum(x) ==> 3tf.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) ==> [[2], [1]]tf.sparse_reduce_sum(x, [0, 1]) ==> 3 |
Args:
-
sp_input: The SparseTensor to reduce. Should have numeric type. -
reduction_axes: The dimensions to reduce; list or scalar. IfNone(the default), reduces all dimensions. -
keep_dims: If true, retain reduced dimensions with length 1.
Returns:
The reduced Tensor.
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