 # 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] `