Tensorflow is an open-source machine learning library developed by Google. One of its applications is to develop deep neural networks.
The module tensorflow.math provides support for many basic mathematical operations. Function tf.reciprocal() [alias tf.math.reciprocal] provides support to calculate the reciprocal of input in Tensorflow. It expects the input in form of complex numbers as 
, floating point numbers and integers. The input type is tensor and if the input contains more than one element, an element-wise reciprocal is computed, .
Syntax: tf.reciprocal(x, name=None) or tf.math.reciprocal(x, name=None)
Parameters:
x: A Tensor of type bfloat16, half, float32, float64, int32, int64, complex64 or complex128.
name (optional): The name for the operation.Return type: A Tensor with the same size and type as that of x.
Code #1:
Python3
# Importing the Tensorflow library import tensorflow as tf   # A constant vector of size 6 a = tf.constant([-0.5, -0.1, 0, 0.1, 0.5, 2], dtype = tf.float32)   # Applying the reciprocal function and # storing the result in 'b' b = tf.reciprocal(a, name ='reciprocal')   # Initiating a Tensorflow session with tf.Session() as sess:     print('Input type:', a)     print('Input:', sess.run(a))     print('Return type:', b)     print('Output:', sess.run(b)) |
Output:
Input type: Tensor("Const:0", shape=(6, ), dtype=float32)
Input: [-0.5 -0.1 0. 0.1 0.5 2. ]
Return type: Tensor("reciprocal:0", shape=(6, ), dtype=float32)
Output: [ -2. -10. inf 10. 2. 0.5]
Â
denotes that the reciprocal approaches to infinity as the input approaches to zero.
Code #2: Visualization
Python3
# Importing the Tensorflow library import tensorflow as tf   # Importing the NumPy library import numpy as np   # Importing the matplotlib.pyplot function import matplotlib.pyplot as plt   # Two vector each of size 20 with values from 0 to 10 a = np.linspace(0, 10, 20)   # Applying the reciprocal function and # storing the result in 'b' b = tf.reciprocal(a, name ='reciprocal')   # Initiating a Tensorflow session with tf.Session() as sess:     print('Input:', a)     print('Output:', sess.run(b))     plt.plot(a, sess.run(b), color = 'red', marker ='o')     plt.title("tensorflow.reciprocal")     plt.xlabel("X")     plt.ylabel("Y")     plt.grid()       plt.show() |
Output:
Input: [ 0. 0.52631579 1.05263158 1.57894737 2.10526316 2.63157895 3.15789474 3.68421053 4.21052632 4.73684211 5.26315789 5.78947368 6.31578947 6.84210526 7.36842105 7.89473684 8.42105263 8.94736842 9.47368421 10. ] Output: [ inf 1.9 0.95 0.63333333 0.475 0.38 0.31666667 0.27142857 0.2375 0.21111111 0.19 0.17272727 0.15833333 0.14615385 0.13571429 0.12666667 0.11875 0.11176471 0.10555556 0.1 ]
