Python | Tensorflow sin() method
Last Updated :
31 Mar, 2023
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.sin() [alias tf.math.sin] provides support for the sine 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 sine is computed.
Syntax: tf.sin(x, name=None) or tf.math.sin(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
import tensorflow as tf
a = tf.constant([ 1.0 , - 0.5 , 3.4 , - 2.1 , 0.0 , - 6.5 ], dtype = tf.float32)
b = tf.sin(a, name = 'sin' )
with tf.Session() as sess:
print ( 'Input type:' , a)
print ( 'Input:' , sess.run(a))
print ( 'Return type:' , b)
print ( 'Output:' , sess.run(b))
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Output:
Input type: Tensor("Const_1:0", shape=(6, ), dtype=float32)
Input: [ 1. -0.5 3.4000001 -2.0999999 0. -6.5 ]
Return type: Tensor("sin_0:0", shape=(6, ), dtype=float32)
Output: [ 0.84147096 -0.47942555 -0.25554121 -0.86320943 0. -0.21511999]
Code #2: Visualization
Python3
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
a = np.linspace( - 5 , 5 , 15 )
b = tf.sin(a, name = 'sin' )
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.sin" )
plt.xlabel( "X" )
plt.ylabel( "Y" )
plt.show()
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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: [ 0.95892427 0.91034694 0.41672165 -0.2806294 -0.84078711 -0.98990308
-0.6550779 0. 0.6550779 0.98990308 0.84078711 0.2806294
-0.41672165 -0.91034694 -0.95892427]
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