Python - Tensorflow math.add_n() method - GeeksforGeeks
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Python – Tensorflow math.add_n() method
- Last Updated : 04 Jun, 2020
Tensorflow math.add_n()
method adds the all passed tensors element-wise. The operation is done on the representation of a and b.
This method belongs to math module.
Syntax:
tf.math.add_n(inputs, name=None)
Arguments
- inputs: It specifies a list of tf.Tensor or tf.IndexedSlices objects, and the shape and type of each must be same. tf.IndexedSlices objects converted automatically into dense tensors before applying method.
- 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 passed inputs.
Note: This method performs the same operation as tf.math.accumulate_n, but this method waits for the inputs to ready before starting to sum. So, this buffering results in more memory consumption when inputs might not ready at same time.
Example 1:
# Importing the Tensorflow library import tensorflow as tf # A constant a and b a = tf.constant([[ 1 , 3 ], [ 2 , 8 ]]) b = tf.constant([[ 2 , 1 ], [ 6 , 7 ]]) # Applying the math.add_n() function # storing the result in 'c' c = tf.math.add_n([a, 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) |
Output:
Input 1 Tensor("Const_99:0", shape=(2, 2), dtype=int32) [[1 3] [2 8]] Input 2 Tensor("Const_100:0", shape=(2, 2), dtype=int32) [[2 1] [6 7]] Output: Tensor("AddN:0", shape=(2, 2), dtype=int32) [[ 3 4] [ 8 15]]
Example 2:
# Importing the Tensorflow library import tensorflow as tf # A constant a and b a = tf.constant([[ 1 , 1 ], [ 2 , 6 ]]) b = tf.constant([[ 5 , 1 ], [ 8 , 7 ]]) # Applying the math.add_n() function # storing the result in 'c' c = tf.math.add_n([a, b], name = "Add_N" ) # 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_101:0", shape=(2, 2), dtype=int32) [[1 1] [2 6]] Input 2 Tensor("Const_102:0", shape=(2, 2), dtype=int32) [[5 1] [8 7]] Output: Tensor("Add_N:0", shape=(2, 2), dtype=int32) [[ 6 2] [10 13]]
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