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Python | Tensorflow reciprocal() method

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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

[Tex] $a+bi$ [/Tex]

, 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,

[Tex] $y=1/x$ [/Tex]

.

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]

[Tex] $ inf $ [/Tex]

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 ]



Last Updated : 29 Feb, 2024
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