  # Python | Tensorflow abs () method

NumPy | Python Methods and Functions

The `tensorflow.math ` module provides support for many basic mathematical operations. The ` tf.abs () ` [alias ` tf.math.abs `] function provides support for absolute functions in Tensorflow. Expected to be input as complex numbers in the form or floating point. Input type — tensor, and if the input contains more than one element, the element-wise absolute value is calculated.

For a complex number the absolute value is calculated as ,
For floating point numbers the absolute value is calculated as Syntax : tf.abs ( x, name = None) or tf.math.abs (x, name = None)

Parameters :
x : A Tensor or SparseTensor of type float16, float32, float64, int32, int64, complex64 or complex128.
name (optional): The name for the operation.

Return type : A Tensor or SparseTensor with the same size and type as that of x with absolute values. For complex64 or complex128 input, the returned Tensor will be of type float32 or float64, respectively.

Code # 1: For Floats

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` # Import Tensorflow library import tensorflow as tf   # Constant vector of size 5 a = tf.constant ([ - 0.5 , - 0.1 , 0 , 0.1 , 0.5 ] , dtype = tf.float32)    # Applying the abs function and # saving the result to & # 39; b & # 39; b = tf. abs (a, name = `abs` )    # Initiating a Tensorflow session with tf.Session () as sess:   print ( ` Input type: ` , a) print ( < code class = "string"> `Input:` , sess.run (a))   print ( `Return type:` , b) print ( `Output:` , sess.run (b)) `

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Output:

` Input type: Tensor ( "Const: 0", shape = (5,), dtype = float32) Input: [-0.5 -0.1 0. 0.1 0.5] Return Type: Tensor ("abs: 0", shape = (5,), dtype = float32 ) Output: [0.5 0.1 0. 0.1 0.5] `

Code # 2: Rendering

 ` # Import Tensorflow library ` ` import ` ` tensorflow as tf `   ` # Import NumPy library ` ` import ` ` numpy as np ` `   # Import of the matplotlib.pylot function `` import matplotlib.pyplot as plt   # Vector size 11 with values ​​from - 5 to 5 a = np. linspace ( - 5 , 5 , 11 )   # Applying an absolute function and # save the result to & # 39; b & # 39; b = tf. abs (a, name = `abs` )    # 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.abs" )  plt.xlabel ( "X" )  plt.ylabel ( "Y" )       plt.show () `

Output:

` Input: [-5. -four. -3. -2. -one. 0. 1. 2. 3. 4. 5.] Output: [5. 4. 3. 2. 1. 0. 1. 2. 3. 4. 5.] ` Code # 3: for complex numbers

 ` # Import Tensorflow library ` ` import ` ` tensorflow as tf ` `   # Constant vector of size 2 `` a = tf.constant ([[ - 2.25 + 4.75j ], [ - 3.25 + 5.75j ]], ``   dtype ` ` = ` ` tf.complex64) `   ` # Applying the abs function and ` ` # saving the result to & # 39; b & # 39; ` ` b ` ` = ` ` tf. ` ` abs ` ` (a, name ` ` = ` ` `abs` ` `) ` ` `  ` # 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_1: 0", shape = (2, 1), dtype = complex64) Input: [[-2.25 + 4.75j] [-3.25 + 5.75j]] Return Type: Tensor ( "abs_1: 0", shape = (2, 1), dtype = float32) Output: [[5.255949] [6.6049223]] `