Python | Tensorflow nn.tanh()

11 Jan, 2022
Tensorflow is an open-source machine learning library developed by Google. One of its applications is to develop deep neural networks. 
The module tensorflow.nn provides support for many basic neural network operations.
One of the many activation functions is the hyperbolic tangent function (also known as tanh) which is defined as .The hyperbolic tangent function outputs in the range (-1, 1), thus mapping strongly negative inputs to negative values. Unlike the sigmoid function, only near-zero values are mapped to near-zero outputs, and this solves the “vanishing gradients” problem to some extent. The hyperbolic tangent function is differentiable at every point and its derivative comes out to be . Since the expression involves the tanh function, its value can be reused to make the backward propagation faster.
Despite the lower chances of the network getting “stuck” when compared with the sigmoid function, the hyperbolic tangent function still suffers from “vanishing gradients”. Rectified Linear Unit (ReLU) can be used to overcome this problem.
The function tf.nn.tanh() [alias tf.tanh] provides support for the hyperbolic tangent function in Tensorflow. 
Syntax: tf.nn.tanh(x, name=None) or tf.tanh(x, name=None)Parameters: x: A tensor of any of the following types: float16, float32, double, complex64, or complex128. name (optional): The name for the operation.Return : 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 tanh function and# storing the result in 'b'b = tf.nn.tanh(a, name ='tanh') # 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("tanh_2:0", shape=(6, ), dtype=float32)
Output: [ 0.76159418 -0.46211717  0.9977749  -0.97045201  0.         -0.99999547]
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 tanh function and# storing the result in 'b'b = tf.nn.tanh(a, name ='tanh') # 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.nn.tanh")    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: [-0.9999092  -0.99962119 -0.99842027 -0.99342468 -0.97284617 -0.89137347
 -0.61335726  0.          0.61335726  0.89137347  0.97284617  0.99342468
  0.99842027  0.99962119  0.9999092 ]
 
sanskar27jain
sagar0719kumar
sweetyty
Tensorflow
Python
Writing code in comment?
Please use ide.geeksforgeeks.org,
generate link and share the link here.
Python Dictionary
Different ways to create Pandas Dataframe
Enumerate() in Python
Read a file line by line in Python
Python String | replace()
How to Install PIP on Windows ?
*args and **kwargs in Python
Python Classes and Objects
Iterate over a list in Python
Convert integer to string in Python