# Python – Tensorflow math.accumulate_n() method

• Last Updated : 02 Jun, 2022

Tensorflow math.accumulate_n() method performs  the element-wise sum of a list of passed tensors. The result is a tensor after performing the operation. The operation is done on the representation of a and b. This method belongs to math module.

Syntax: tf.math.accumulate_n( inputs, shape=None, tensor_dtype=None, name=None) Arguments

• inputs: This parameter takes a list of Tensor objects, and each of them with same shape and type.
• shape: This is optional parameter and it defines the expected shape of elements of inputs.
• dtype: This is optional parameter and it defines the expected data type of inputs.
• name: This is optional parameter and this is the name of the operation.

Return: It returns a Tensor having the same shape and type as the elements of inputs.

Let’s see this concept with the help of few examples: Example 1:

## Python3

 `# Importing the Tensorflow library``import` `tensorflow as tf` `# A constant a and b``a ``=` `tf.constant([[``1``, ``3``], [``6``, ``7``]])``b ``=` `tf.constant([[``5``, ``2``], [``3``, ``8``]]) ` `# Applying the accumulate_n() function``# storing the result in 'c'``c ``=` `tf.math.accumulate_n([a, b, b])` `# Initiating a Tensorflow session``with tf.Session() as sess:``    ``print``("``Input` `1``", a)``    ``print``(sess.run(a))``    ``print``("``Input` `2``", b)``    ``print``(sess.run(b))``    ``print``("Output: ", c)``    ``print``(sess.run(c))`

Output:

```Input 1 Tensor("Const_67:0", shape=(2, 2), dtype=int32)
[[1 3]
[6 7]]
Input 2 Tensor("Const_68:0", shape=(2, 2), dtype=int32)
[[5 2]
[3 8]]
Output:  Tensor("AccumulateNV2_2:0", shape=(2, 2), dtype=int32)
[[11  7]
[12 23]]```

Example 2:

## Python3

 `# Importing the Tensorflow library``import` `tensorflow as tf` `# A constant a and b``a ``=` `tf.constant([[``2``, ``4``], [``1``, ``3``]])``b ``=` `tf.constant([[``5``, ``3``], [``4``, ``6``]]) ` `# Applying the accumulate_n() function``# storing the result in 'c'``c ``=` `tf.math.accumulate_n([b, a, b], shape ``=``[``2``, ``2``], tensor_dtype ``=` `tf.int32)` `# Initiating a Tensorflow session``with tf.Session() as sess:``    ``print``("``Input` `1``", a)``    ``print``(sess.run(a))``    ``print``("``Input` `2``", b)``    ``print``(sess.run(b))``    ``print``("Output: ", c)``    ``print``(sess.run(c))`

Output:

```Input 1 Tensor("Const_73:0", shape=(2, 2), dtype=int32)
[[2 4]
[1 3]]
Input 2 Tensor("Const_74:0", shape=(2, 2), dtype=int32)
[[5 3]
[4 6]]
Output:  Tensor("AccumulateNV2_5:0", shape=(2, 2), dtype=int32)
[[12 10]
[ 9 15]]```

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