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:
Python3
# Importing the Tensorflow libraryimport tensorflow as tf# A constant vector of size 6a = tf.constant([1.0, -0.5, 3.4, -2.1, 0.0, -6.5], dtype = tf.float32)# Applying the cos function and# storing the result in 'b'b = tf.cos(a, name ='cos')# Initiating a Tensorflow sessionwith 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
Python3
# Importing the Tensorflow libraryimport tensorflow as tf# Importing the NumPy libraryimport numpy as np# Importing the matplotlib.pyplot functionimport matplotlib.pyplot as plt# A vector of size 15 with values from -5 to 5a = np.linspace(-5, 5, 15)# Applying the cos function and# storing the result in 'b'b = tf.cos(a, name ='cos')# Initiating a Tensorflow sessionwith 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]
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