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.tan() [alias tf.math.tan] provides support for the tangent function in Tensorflow. It expects the input in radian form. The input type is tensor and if the input contains more than one element, element-wise tangent is computed.
Syntax: tf.tan(x, name=None) or tf.math.tan(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 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 tan function and # storing the result in 'b' b = tf.tan(a, name ='tan') # 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:0", shape=(6, ), dtype=float32)
Input: [ 1. -0.5 3.4 -2.1 0. -6.5]
Return type: Tensor("tan:0", shape=(6, ), dtype=float32)
Output: [ 1.5574077 -0.5463025 0.264317 1.7098469 0. -0.2202772]
Code #2: Visualization
Python3
# Importing the Tensorflow library import tensorflow as tf # Importing the NumPy library import numpy as np # Importing the matplotlib.pyplot function import matplotlib.pyplot as plt # A vector of size 15 with values from -1 to 1 a = np.linspace(-1, 1, 15) # Applying the tangent function and # storing the result in 'b' b = tf.tan(a, name ='tan') # 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.tan") plt.xlabel("X") plt.ylabel("Y") plt.show() |
Output:
Input: [-1. -0.85714286 -0.71428571 -0.57142857 -0.42857143 -0.28571429 -0.14285714 0. 0.14285714 0.28571429 0.42857143 0.57142857 0.71428571 0.85714286 1. ] Output: [-1.55740772 -1.15486601 -0.86700822 -0.64298589 -0.45689311 -0.29375136 -0.14383696 0. 0.14383696 0.29375136 0.45689311 0.64298589 0.86700822 1.15486601 1.55740772]
