Python | Tensorflow acos () method

The tensorflow.math module provides support for many basic mathematical operations. The tf.acos () [alias tf.math.acos ] function provides support for the inverse cosine function in Tensorflow. The input is assumed to be in the range [-1, 1] and outputs the output in radians. Returns nan, if the input is not in the range [-1, 1]. Input type — tensor, and if the input contains more than one element, the inverse cosine is calculated for each element.

Syntax : tf.acos (x, name = None) or tf.math .acos (x, name = None)

Parameters :
x : A tensor of any of the following types: bfloat16, half , float32, float64, int32, int64, complex64, or complex128.
name (optional): The name for the operation.

Return type : A tensor with the same type as that of x.

Code # 1:

# Tensorflow library import

import tensorflow as tf

 
# Constant vector of size 6

a = tf.constant ([ 1.0 , - 0.5 , 3.4 , 0.2 , 0.0 , - 2 ],

dtype = tf.float32)

 
# Using the acos function and
# save the result to & # 39; b & # 39;

b = tf.acos (a, name = `acos` )

< code class = "undefined spaces">  
# 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_7: 0", shape = (6,), dtype = float32) Input: [1. -0.5 3.4 0.2 0. -2. ] Return type: Tensor ("acos: 0", shape = (6,), dtype = float32) Output: [0. 2.0943952 nan 1.3694384 1.5707964 nan] 

Code # 2: Visualization

# Library import Tensorflow

import tensorflow as tf

 
# Importing the NumPy library

import numpy as np

 
# Import function matplotlib.pylot

import matplotlib.pyplot as plt

 
# Vector size 15 with values ​​from -1 to 1

a = np.linspace ( - 1 , 1 , 15 )

 
# Applying the inverse cosine function and
# saving the result to & # 39; b & # 39;

b = tf.acos (a, name = `acos` )

 
# 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.acos "

plt.xlabel ( "X "

  plt.ylabel ( "Y"

 

plt.show ()

Exit:

 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: [3.14159265 2.60049313 2.36639928 2.17904191 2.01370737 1.86054803 1.7141439 1.57079633 1.42744876 1.28104463 1.12788528 0.96255075 0.77519337 0.54109953 0.]