 # 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.] ```