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Comprehensive Guide on tanh
Machine Learning
chevron_rightNeural Networks
chevron_rightActivation functions
schedule Jul 1, 2022
Last updated local_offer
Tags tocTable of Contents
expand_more The $\mathrm{tanh}$ curve looks similar to the sigmoid curve in that the shape is s-like. $\mathrm{tanh}$ takes in any real value as input, and outputs a value from $-1$ to $1$.
The following is a graph of $\mathrm{tanh}$ and sigmoid function:
Mathematically, we can represent $\mathrm{tanh}(x)$ like so:
$$\mathrm{tanh}(x)=\frac{e^x-e^{-x}}{e^x+e^{-x}}$$
Derivative
What makes $\mathrm{tanh}$ appealing as an activation function is that the derivative of $\mathrm{tanh}$ is clean:
$$\frac{d}{dx}\mathrm{tanh}(x)=1-\mathrm{tanh}(x)^2$$
The graph of the derivative of $\mathrm{tanh}(x)$ looks like the following:
Code
The code used to generate the plot is as follows:
import numpy as npimport matplotlib.pyplot as pltplt.style.use('seaborn-whitegrid')plt.style.use('seaborn')
def sigmoid(x): return 1 / (1 + np.exp(-x))
x_tanh = np.arange(-5, 5, 0.1)y_tanh = np.tanh(x)plt.figure(figsize=(5,4))plt.xlabel("x")plt.ylabel("y")plt.axhline(0, color="blue", alpha=0.2)plt.axvline(0, color="blue", alpha=0.2)x_sigmoid = np.arange(-5, 5, 0.1)y_sigmoid = sigmoid(x)plt.plot(x_sigmoid, y_sigmoid, color="red", label="Sigmoid")plt.plot(x_tanh, y_tanh, color="blue", label="tanh")plt.legend()
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Published by Isshin Inada
Edited by 0 others
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