Watch keynotes, product sessions, workshops, and more from Google I/O

# tf.compat.v1.sparse_reduce_sum

Computes `tf.sparse.add` of elements across dimensions of a SparseTensor. (deprecated arguments) (deprecated arguments)

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.

# 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>`
```

`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`.

The reduced Tensor.