Python | Tensorflow asin () method



The tensorflow.math module provides support for many basic mathematical operations. The tf.asin () [alias tf.math.asin ] function provides support for the inverse sine 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 elementwise inverse sine is calculated.

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

# Import Tensorflow Library

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 asin and
# save the result to & # 39; b & # 39;

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

  
# 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: Ten sor ("Const_6: 0", shape = (6,), dtype = float32) Input: [1. -0.5 3.4 0.2 0. -2. ] Return type: Tensor ("asin_2: 0", shape = (6,), dtype = float32) Output: [1.5707964 -0.5235988 nan 0.20135793 0. nan] 

Code # 2: Rendering

# Importing the Tensorflow library

import tensorflow as tf

 
# Import NumPy Library

import numpy as np

 
# Import matplotlib.pylot function

import matplotlib.pyplot as plt

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

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

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

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

  
# 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.asin"

plt.xlabel ( " X "

plt.ylabel ( "Y"

 

< code class = "undefined spaces">  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: [-1.57079633 -1.0296968 -0.79560295 -0.60824558 -0.44291104 -0.2897517 -0.14334757 0. 0.14334757 0.2897517 0.44291104 0.60824558 0.79560295 1.0296968 1.57079633]