|---| | [ View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.21.0/tensorflow/python/ops/math_ops.py#L2227-L2291) | Computes the sum of elements across dimensions of a tensor. #### View aliases **Main aliases** [`tf.reduce_sum`](https://www.tensorflow.org/api_docs/python/tf/math/reduce_sum)
tf.math.reduce_sum( input_tensor, axis=None, keepdims=False, name=None ) ### Used in the notebooks | Used in the guide | Used in the tutorials | |---|---| | - [Advanced automatic differentiation](https://www.tensorflow.org/guide/advanced_autodiff) - [Better performance with tf.function](https://www.tensorflow.org/guide/function) - [TensorFlow basics](https://www.tensorflow.org/guide/basics) - [Matrix approximation with Core APIs](https://www.tensorflow.org/guide/core/matrix_core) - [Extension types](https://www.tensorflow.org/guide/extension_type) | - [Using DTensors with Keras](https://www.tensorflow.org/tutorials/distribute/dtensor_keras_tutorial) - [Distributed training with DTensors](https://www.tensorflow.org/tutorials/distribute/dtensor_ml_tutorial) - [Convolutional Variational Autoencoder](https://www.tensorflow.org/tutorials/generative/cvae) - [Neural style transfer](https://www.tensorflow.org/tutorials/generative/style_transfer) - [Playing CartPole with the Actor-Critic method](https://www.tensorflow.org/tutorials/reinforcement_learning/actor_critic) | This is the reduction operation for the elementwise [`tf.math.add`](https://www.tensorflow.org/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.


| ## For example || |---|---| |
>>> # 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().item() 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().item() 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. ||

## 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.