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.atanh()
[alias tf.math.atanh
] provides support for the inverse hyperbolic tangent function in Tensorflow. Its domain is in the range [-1, 1] and it returns nan for any input outside this range. The input type is tensor and if the input contains more than one element, element-wise inverse hyperbolic tangent is computed.
Syntax: tf.atanh(x, name=None) or tf.math.atanh(x, name=None)
Parameters:
x: A tensor of any of the following types: float16, float32, float64, complex64, or complex128.
name (optional): The name for the operation.Return type: A tensor with the same type as that of x.
Code #1:
# Importing the Tensorflow library import tensorflow as tf # A constant vector of size 6 a = tf.constant([ 1.0 , - 0.5 , - 1 , 2.4 , 0.0 , - 6.5 ], dtype = tf.float32) # Applying the atanh function and # storing the result in 'b' b = tf.atanh(a, name = 'atanh' ) # 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_3:0", shape=(6, ), dtype=float32) Input: [ 1. -0.5 -1. 2.4 0. -6.5] Return type: Tensor("atanh_1:0", shape=(6, ), dtype=float32) Output: [ inf -0.54930615 -inf nan 0. nan]
Code #2: Visualization
# Importing the Tensorflow library import tensorflow as tf # Importing the NumPy library import numpy as np # Importing the matplotlib.pylot function import matplotlib.pyplot as plt # A vector of size 15 with values from -1 to 1 a = np.linspace( - 1 , 1 , 15 ) # Applying the inverse hyperbolic tangent # function and storing the result in 'b' b = tf.atanh(a, name = 'atanh' ) # 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.atanh" ) plt.xlabel( "X" ) plt.ylabel( "Y" ) plt.show() |
Output:
Input: [-1. -0.85714286 -0.71428571 -0.57142857 -0.42857143 -0.28571429 -0.14285714 0. 0.14285714 0.28571429 0.42857143 0.57142857 0.71428571 0.85714286 1. ] Output: [ -inf -1.28247468 -0.89587973 -0.64964149 -0.45814537 -0.29389333 -0.14384104 0. 0.14384104 0.29389333 0.45814537 0.64964149 0.89587973 1.28247468 inf]
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