# tf.math.reduce_sum

Computes the sum of elements across dimensions of a tensor.

This is the reduction operation for the elementwise `tf.math.add` op.

Reduces `input_tensor` along the dimensions given in `axis`. Unless `keepdims` is true, the rank of the tensor is reduced by 1 for each of the entries in `axis`, which must be unique. If `keepdims` is true, the reduced dimensions are retained with length 1.

If `axis` is None, all dimensions are reduced, and a tensor with a single element is returned.

``````>>> # x has a shape of (2, 3) (two rows and three columns):
>>> x = tf.constant([[1, 1, 1], [1, 1, 1]])
>>> x.numpy()
array([[1, 1, 1],
[1, 1, 1]], dtype=int32)
>>> # sum all the elements
>>> # 1 + 1 + 1 + 1 + 1+ 1 = 6
>>> tf.reduce_sum(x).numpy()
6
>>> # reduce along the first dimension
>>> # the result is [1, 1, 1] + [1, 1, 1] = [2, 2, 2]
>>> tf.reduce_sum(x, 0).numpy()
array([2, 2, 2], dtype=int32)
>>> # reduce along the second dimension
>>> # the result is [1, 1] + [1, 1] + [1, 1] = [3, 3]
>>> tf.reduce_sum(x, 1).numpy()
array([3, 3], dtype=int32)
>>> # keep the original dimensions
>>> tf.reduce_sum(x, 1, keepdims=True).numpy()
array([[3],
[3]], dtype=int32)
>>> # reduce along both dimensions
>>> # the result is 1 + 1 + 1 + 1 + 1 + 1 = 6
>>> # or, equivalently, reduce along rows, then reduce the resultant array
>>> # [1, 1, 1] + [1, 1, 1] = [2, 2, 2]
>>> # 2 + 2 + 2 = 6
>>> tf.reduce_sum(x, [0, 1]).numpy()
6
``````

`input_tensor` The tensor to reduce. Should have numeric type.
`axis` The dimensions to reduce. If `None` (the default), reduces all dimensions. Must be in the range ```[-rank(input_tensor), rank(input_tensor)]```.
`keepdims` If true, retains reduced dimensions with length 1.
`name` A name for the operation (optional).

The reduced tensor, of the same dtype as the input_tensor.

## numpy compatibility

Equivalent to np.sum apart the fact that numpy upcast uint8 and int32 to int64 while tensorflow returns the same dtype as the input.

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