tf.math.add_n

Returns the element-wise sum of a list of tensors.

View aliases

Main aliases

tf.add_n

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.add_n, tf.compat.v1.math.add_n

tf.math.add_n(
    inputs, name=None
)

Used in the notebooks

Used in the guide	Used in the tutorials
Validating correctness & numerical equivalence Use TF1.x models in TF2 workflows Effective Tensorflow 2 Distributed training with TensorFlow Use a GPU	Neural style transfer Custom training with tf.distribute.Strategy Custom training loop with Keras and MultiWorkerMirroredStrategy Parameter server training with ParameterServerStrategy Overfit and underfit

All inputs in the list must have the same shape. 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]).numpy()
array([[ 7, 16],
       [10, 25]], dtype=int32)

See Also:

• tf.reduce_sum(inputs, axis=0) - This performs the same mathematical operation, but tf.add_n may be more efficient because it sums the tensors directly. reduce_sum on the other hand calls tf.convert_to_tensor on the list of tensors, unnecessarily stacking them into a single tensor before summing.

Args
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).

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

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