  # Python | Tensorflow atan () method

NumPy | Python Methods and Functions

The `tensorflow.math ` module provides support for many basic mathematical operations. The ` tf.atan () ` [alias ` tf.math.atan `] function provides support for the inverse tangent function in Tensorflow. This gives the output in radians. Input type — tensor, and if the input contains more than one element, the inverse tangent is calculated for each element.

Syntax : tf.atan (x, name = None) or tf.math .atan (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 atan function and # save the result to & # 39; b & # 39; b = tf.atan (a, name = `atan` ) < 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_8: 0", shape = (6,), dtype = float32) Input: [1. -0.5 3.4 0.2 0. -2. ] Return type: Tensor ("atan: 0", shape = (6,), dtype = float32) Output: [0.7853982 -0.4636476 1.2847449 0.19739556 0. -1.1071488] `

Code # 2: Rendering

 ` # Import 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 -5 to 5 ` ` a = np.linspace ( - 5 , 5 , 15 ) ``   # Applying inverse tangent function and # saving the result to & # 39; b & # 39; b = tf.atan (a, name = ` atan` )    # Initiating a Tensorflow session with tf.Session () as sess: `` print ( `Input:` < code class = "plain">, a) ` ` print ` ` (` ` `Output:` ` `, sess.run (b)) ` ` plt.plot (a, sess.run (b), color ` ` = ` ` `red` ` `, marker ` ` = ` ` "o" ` `) ` ` plt.title (` `" tensorflow.atan "` `) ` ` plt.xlabel (` ` "X" ` ` ) ` ` plt.ylabel (` ` " Y "` `) ` `  `` plt.show () `

Exit:

` Input: [-5. -4.28571429 -3.57142857 -2.85714286 -2.14285714 -1.42857143 -0.71428571 0. 0.71428571 1.42857143 2.14285714 2.85714286 3.57142857 4.28571429 5.] Output: [-1.37340077 -1.34156439 -1.29778762 -1.23412151 -1.13416917 -0.96007036 -0.62024949 0. 0.62024949 0.96007036 1.13416917 1.23412151 1.29778762 1.34156439 1.37340077] `