TensorFlow 1 version View source on GitHub

Adds all input tensors element-wise.

tf.math.add_n performs the same operation as tf.math.accumulate_n, but it waits for all of its inputs to be ready before beginning to sum. This buffering can result in higher memory consumption when inputs are ready at different times, since the minimum temporary storage required is proportional to the input size rather than the output size.

This op does not broadcast its inputs. If you need broadcasting, use tf.math.add (or the + operator) instead.

For example:

a = tf.constant([[3, 5], [4, 8]])
b = tf.constant([[1, 6], [2, 9]])
tf.math.add_n([a, b, a])
<tf.Tensor: shape=(2, 2), dtype=int32, numpy=
array([[ 7, 16],
       [10, 25]], dtype=int32)>

inputs A list of tf.Tensor or tf.IndexedSlices objects, each with the same shape and type. tf.IndexedSlices objects will be converted into dense tensors prior to adding.
name A name for the operation (optional).

A tf.Tensor of the same shape and type as the elements of inputs.

ValueError If inputs don't all have same shape and dtype or the shape cannot be inferred.