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.asinh() [alias tf.math.asinh] provides support for the inverse hyperbolic sine function in Tensorflow. The input type is tensor and if the input contains more than one element, element-wise inverse hyperbolic sine is computed.
Syntax: tf.asinh(x, name=None) or tf.math.asinh(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:
Python3
# Importing the Tensorflow library import tensorflow as tf     # A constant vector of size 6 a = tf.constant([1.0, -0.5, 3.4, 22.1, 0.0, -6.5],                                dtype = tf.float32)     # Applying the asinh function and # storing the result in 'b' b = tf.asinh(a, name ='asinh')     # 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_1:0", shape=(6, ), dtype=float32)
Input: [ 1. -0.5 3.4 22.1 0. -6.5]
Return type: Tensor("asinh:0", shape=(6, ), dtype=float32)
Output: [ 0.8813736 -0.48121184 1.9378793 3.7892363 0. -2.5708146 ]
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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    # A vector of size 15 with values from -10 to 10 a = np.linspace(-10, 10, 15)    # Applying the inverse hyperbolic sine # function and storing the result in 'b' b = tf.asinh(a, name ='asinh')    # 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.asinh")     plt.xlabel("X")     plt.ylabel("Y")        plt.show() |
Output:
Input: [-10. -8.57142857 -7.14285714 -5.71428571 -4.28571429 -2.85714286 -1.42857143 0. 1.42857143 2.85714286 4.28571429 5.71428571 7.14285714 8.57142857 10. ] Output: [-2.99822295 -2.84496713 -2.66412441 -2.44368627 -2.16177575 -1.77227614 -1.15447739 0. 1.15447739 1.77227614 2.16177575 2.44368627 2.66412441 2.84496713 2.99822295]
