This is an introduction to artificial neural networks (ANNs) including the idea of classification and how ANNs can classify data into number of distinct classes based on some features.
A basic neural network example is given that uses a single layer perceptron with three inputs and one output to classify data linearly using the Signum activation function.
The presented example is about classifying data about colors into two categories (Red and Blue).
Artificial neural networks (ANNs) or connectionist systems are a computational model used in machine learning, computer science and other research disciplines, which is based on a large collection of connected simple units called artificial neurons, loosely analogous to axons in a biological brain. Connections between neurons carry an activation signal of varying strength. If the combined incoming signals are strong enough, the neuron becomes activated and the signal travels to other neurons connected to it. Such systems can be trained from examples, rather than explicitly programmed, and excel in areas where the solution or feature detection is difficult to express in a traditional computer program. Like other machine learning methods, neural networks have been used to solve a wide variety of tasks, like computer vision and speech recognition, that are difficult to solve using ordinary rule-based programming.
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Introduction to Artificial Neural Networks (ANNs) - Step-by-Step Training & Testing Example 2
1. Artificial Neural Networks (ANNs)
Step-By-Step Training & Testing
Example 2
MENOUFIA UNIVERSITY
FACULTY OF COMPUTERS AND INFORMATION
ALL DEPARTMENTS
ARTIFICIAL INTELLIGENCE
المنوفية جامعة
والمعلومات الحاسبات كلية
األقسام جميع
الذكاءاإلصطناعي
المنوفية جامعة
Ahmed Fawzy Gad
ahmed.fawzy@ci.menofia.edu.eg
20. Activation Functions
Which activation function to use?
Outputs
Class
Labels
Activation
Function
TWO Class
Labels
TWO
Outputs
One that gives two outputs.
Which activation function to use?
𝑪𝒋𝒀𝒋
𝑭 𝟐𝑭 𝟏
16.8121
C1
15.2114
9.4210
C2
8.1195
54. Neural Networks Training Steps
Weights Initialization
Inputs Application
Sum of Inputs-Weights Products
Activation Function Response Calculation
Weights Adaptation
Back to Step 2
1
2
3
4
5
6
55. Regarding 5th Step: Weights Adaptation
• If the predicted output Y is not the same as the desired output d,
then weights are to be adapted according to the following equation:
𝑾 𝒏 + 𝟏 = 𝑾 𝒏 + η 𝒅 𝒏 − 𝒀 𝒏 𝑿(𝒏)
Where
𝑾 𝒏 = [𝒃 𝒏 , 𝑾 𝟏(𝒏), 𝑾 𝟐(𝒏), 𝑾 𝟑(𝒏), … , 𝑾 𝒎(𝒏)]
56. Neural Networks
Training Example
Step n=0
• In each step in the solution, the parameters of the neural network
must be known.
• Parameters of step n=0:
η = .01
𝑋 𝑛 = 𝑋 0 = +1, 121, 16.8
𝑊 𝑛 = 𝑊 0 = −1230, −30, 300
𝑑 𝑛 = 𝑑 0 = +1
𝑭 𝟐𝑭 𝟏
16.8121
C1 = +1
15.2114
9.4210
C2 = -1
8.1195
93. Correct Weights
• After testing the weights across all samples and results were correct
then we can conclude that current weights are correct ones for
training the neural network.
• After training phase we come to testing the neural network.
• What is the class of the unknown color of values of F1=140 and
F2=17.9?