# tf.compat.v1.reduce_sum

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/math_ops.py#L2249-L2326) |

Computes the sum of elements across dimensions of a tensor. (deprecated arguments)

#### View aliases


**Compat aliases for migration**

See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.

[`tf.compat.v1.math.reduce_sum`](https://www.tensorflow.org/api_docs/python/tf/compat/v1/reduce_sum)

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    tf.compat.v1.reduce_sum(
        input_tensor,
        axis=None,
        keepdims=None,
        name=None,
        reduction_indices=None,
        keep_dims=None
    )

> [!WARNING]
> **Deprecated:** SOME ARGUMENTS ARE DEPRECATED: `(keep_dims)`. They will be removed in a future version. Instructions for updating: keep_dims is deprecated, use keepdims instead

This is the reduction operation for the elementwise [`tf.math.add`](https://www.tensorflow.org/versions/r2.13/api_docs/python/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.

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| ## For example ||
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| <br /> >>> # 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 <br /> ||

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| ## Args ||
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| `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). |
| `reduction_indices` | The old (deprecated) name for axis. |
| `keep_dims` | Deprecated alias for `keepdims`. |

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| ## Returns ||
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| The reduced tensor, of the same dtype as the input_tensor. ||

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## numpy compatibility

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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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