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.acosh() [alias tf.math.acosh] provides support for the inverse hyperbolic cosine function in Tensorflow. It expects the input in the range [1, ∞) and 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 cosine is computed.
Syntax: tf.acosh(x, name=None) or tf.math.acosh(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 libraryimport tensorflow as tf # A constant vector of size 6a = tf.constant([1.0, 0.5, 3.4, -2.1, 0.0, 6.5], dtype = tf.float32) # Applying the acosh function and# storing the result in 'b'b = tf.acosh(a, name ='acosh') # Initiating a Tensorflow sessionwith 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: [ 1. 0.5 3.4 -2.1 0. 6.5]
Return type: Tensor("acosh:0", shape=(6, ), dtype=float32)
Output: [0. nan 1.894559 nan nan 2.558979]
Code #2: Visualization
Python3
# Importing the Tensorflow libraryimport tensorflow as tf # Importing the NumPy libraryimport numpy as np # Importing the matplotlib.pyplot functionimport matplotlib.pyplot as plt # A vector of size 15 with values from 1 to 10a = np.linspace(1, 10, 15) # Applying the inverse hyperbolic cosine# function and storing the result in 'b'b = tf.acosh(a, name ='acosh') # Initiating a Tensorflow sessionwith 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.acosh") plt.xlabel("X") plt.ylabel("Y") plt.show() |
Output:
Input: [ 1. 1.64285714 2.28571429 2.92857143 3.57142857 4.21428571 4.85714286 5.5 6.14285714 6.78571429 7.42857143 8.07142857 8.71428571 9.35714286 10. ] Output: [0. 1.08055227 1.46812101 1.73714862 1.94591015 2.11724401 2.26282815 2.38952643 2.50174512 2.60249262 2.69391933 2.77761797 2.85480239 2.92641956 2.99322285]
