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 ba = 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 ba = 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]]
