Tensorflow is an open-source machine learning library developed by Google. One of its applications is to develop deep neural networks.
The module tensorflow.math
provides support for many basic mathematical operations. Function tf.cos()
[alias tf.math.cos
] provides support for the cosine function in Tensorflow. It expects the input in radian form and the output is in the range [-1, 1]. The input type is tensor and if the input contains more than one element, element-wise cosine is computed.
Syntax: tf.cos(x, name=None) or tf.math.cos(x, name=None)
Parameters:
x: A tensor of any of the following types: float16, float32, float64, complex64, or complex128.
name (optional): The name for the operation.Return type: A tensor with the same type as that of x.
Code #1:
# Importing the Tensorflow library import tensorflow as tf # A constant vector of size 6 a = tf.constant([ 1.0 , - 0.5 , 3.4 , - 2.1 , 0.0 , - 6.5 ], dtype = tf.float32) # Applying the sin function and # storing the result in 'b' b = tf.cos(a, name = 'cos' ) # 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_2:0", shape=(6, ), dtype=float32) Input: [ 1. -0.5 3.4000001 -2.0999999 0. -6.5 ] Return type: Tensor("cos:0", shape=(6, ), dtype=float32) Output: [ 0.54030228 0.87758255 -0.96679819 -0.50484604 1. 0.97658765]
Code #2: Visualization
# Importing the Tensorflow library import tensorflow as tf # Importing the NumPy library import numpy as np # Importing the matplotlib.pylot function import matplotlib.pyplot as plt # A vector of size 15 with values from -5 to 5 a = np.linspace( - 5 , 5 , 15 ) # Applying the sigmoid function and # storing the result in 'b' b = tf.cos(a, name = 'cos' ) # 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.cos" ) plt.xlabel( "X" ) plt.ylabel( "Y" ) plt.show() |
Output:
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: [ 0.28366219 -0.41384591 -0.90903414 -0.9598162 -0.5413659 0.1417459 0.75556135 1. 0.75556135 0.1417459 -0.5413659 -0.9598162 -0.90903414 -0.41384591 0.28366219]
Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.
To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course.