Computes the sum of elements across dimensions of a tensor.

tf.compat.v2.reduce_sum( input_tensor, axis=None, keepdims=False, name=None )

Reduces `input_tensor`

along the dimensions given in `axis`

. Unless `keepdims`

is true, the rank of the tensor is reduced by 1 for each entry in `axis`

. 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 = tf.constant([[1, 1, 1], [1, 1, 1]]) tf.reduce_sum(x) # 6 tf.reduce_sum(x, 0) # [2, 2, 2] tf.reduce_sum(x, 1) # [3, 3] tf.reduce_sum(x, 1, keepdims=True) # [[3], [3]] tf.reduce_sum(x, [0, 1]) # 6

Args | |
---|---|

`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). |

Returns | |
---|---|

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

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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Licensed under the Creative Commons Attribution License 3.0.

Code samples licensed under the Apache 2.0 License.

https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/compat/v2/reduce_sum