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Computes the sum of elements across dimensions of a SparseTensor. (deprecated arguments) (deprecated arguments)
tf.compat.v1.sparse_reduce_sum(
sp_input, axis=None, keepdims=None, reduction_axes=None, keep_dims=None
)
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.
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. |
reduction_axes
|
Deprecated name of axis.
|
keep_dims
|
Deprecated alias for keepdims.
|
Returns | |
|---|---|
| The reduced Tensor. |