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Home > Data > Neural Networks > Hands-On Neural Network Programming with C#
Hands-On Neural Network Programming with C#
Hands-On Neural Network Programming with C#

Hands-On Neural Network Programming with C#: Add powerful neural network capabilities to your C# enterprise applications

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Hands-On Neural Network Programming with C#

Building Our First Neural Network Together

Now that we've had a quick refresher on Neural Networks, I thought that perhaps a good starting point, code-wise, would be for us to write a very simple neural network. We're not going to go crazy; we'll just lay the basic framework for a few functions so that you can get a good idea of what is behind the scenes of many of the APIs that you'll use. From start to finish, we'll develop this network application so that you are familiar with all the basic components that are contained in a neural network. This implementation is not perfect or all-encompassing, nor is it meant to be. As I mentioned, this will merely provide a framework for us to use in the rest of the book. This is a very basic neural network with the added functionality of being able to save and load networks and data. In any event, you will have...

Technical requirements

You would need to have Microsoft Visual Studio installed on the system.

Check out the following video to see Code in Action: http://bit.ly/2NYJa5G.

Our neural network

Let's begin by showing you an of what a simple neural network would look like, visually. It consists of an input layer with 2 inputs, a Hidden Layer with 3 neurons (sometimes called nodes), and a final output layer consisting of a single neuron. Of course, neural networks can consist of many more layers (and neurons per layer), and once you get into deep learning you will see much more of this, but for now this will suffice. Remember, each node, which is labeled as follows with an N, is an individual neuron – its own little processing brain, if you will:

Let’s break down the neural network into its three basic parts; inputs, Hidden Layers and outputs:

Inputs: This is the initial data for our network. Each input is a whose output to the Hidden Layer is the initial input value.

Hidden Layers: These are the heart and soul of our network, and...

Neural network training

How do we train a neural network? Basically, we will provide the with a set of input data as well as the results we expect to see, which correspond to those inputs. That data is then run through the network until the network understands what we are looking for. We will train, test, train, test, train, test, on and on until our network understands our data (or doesn't, but that's a whole other conversation). We continue to do this until some designated stop condition is satisfied, such as an error rate threshold. Let's quickly cover some of the terminology we will use while training neural networks.

Back propagation: After our data is run through the network, we to validate that data what we expect to be the correct output. We do this by propagating backward (hence backprop or back propagation) through each of the Hidden Layers of our network...

Neural network functions

The following basic list contains the functions we are going to develop n order to lay down our neural network foundation:

  • Creating a new network
  • Importing a network
  • Manually entering user data
  • Importing a dataset
  • Training our network
  • Testing our network

With that behind us, let's start coding!

Creating a new network

This menu option will allow us to create a new network from scratch:

public NNManager SetupNetwork()
{
    _numInputParameters = 2;

    int[] hidden = new int[2];
    hidden[0] = 3;
    hidden[1] = 1;
    _numHiddenLayers = 1;
    _hiddenNeurons = hidden;
    _numOutputParameters = 1;
    _network = new Network(_numInputParameters, _hiddenNeurons,         
    _numOutputParameters);
   ...

The neural network

With many of the ancillary, but important, functions coded, we now turn our attention to the meat of the neural network, the network itself. Within a neural network, the network part is an all-encompassing universe. Everything resides within it. Within this structure we will need to store the input, output, and Hidden Layers of neurons, as well as the learning rate and Momentum, as follows:

public class Network
{
      public double LearningRate{ get; set; }
      public double Momentum{ get; set; }
      public List<Neuron>InputLayer{ get; set; }
      public List<List<Neuron>>HiddenLayers{ get; set; }
      public List<Neuron>OutputLayer{ get; set; }
      public List<Neuron>MirrorLayer {get; set; }
      public List<Neuron>CanonicalLayer{ get; set; }
...

Examples

Now that we have our code created, let's use a few examples to see how it can be used.

Training to a minimum

In this example, we will use the code we wrote to train a network to a minimum value or threshold. For each step, the network prompts you for the correct data, saving us the process of cluttering up our example code with this. In production, you would probably want to pass in the parameters without any user intervention, in case this is run as a service or microservice:

Training to a maximum

In this example, we are going to train the network to reach...

Summary

In this chapter, we saw how to write a complete neural network from scratch. Although the following is a lot we've left out, it does the basics, and we've gotten to see it as pure C# code! We should now have a much better understanding of what a neural network is and what it comprises than when we first started.

In the next chapter, we will begin our journey into more complicated network structures such as recurrent and convolutional neural networks. There's a lot to cover, so hold on to your coding hats!

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Key benefits

  • Get a strong foundation of neural networks with access to various machine learning and deep learning libraries
  • Real-world case studies illustrating various neural network techniques and architectures used by practitioners
  • Cutting-edge coverage of Deep Networks, optimization algorithms, convolutional networks, autoencoders and many more

Description

Neural networks have made a surprise comeback in the last few years and have brought tremendous innovation in the world of artificial intelligence. The goal of this book is to provide C# programmers with practical guidance in solving complex computational challenges using neural networks and C# libraries such as CNTK, and TensorFlowSharp. This book will take you on a step-by-step practical journey, covering everything from the mathematical and theoretical aspects of neural networks, to building your own deep neural networks into your applications with the C# and .NET frameworks. This book begins by giving you a quick refresher of neural networks. You will learn how to build a neural network from scratch using packages such as Encog, Aforge, and Accord. You will learn about various concepts and techniques, such as deep networks, perceptrons, optimization algorithms, convolutional networks, and autoencoders. You will learn ways to add intelligent features to your .NET apps, such as facial and motion detection, object detection and labeling, language understanding, knowledge, and intelligent search. Throughout this book, you will be working on interesting demonstrations that will make it easier to implement complex neural networks in your enterprise applications.

Who is this book for?

This book is for Machine Learning Engineers, Data Scientists, Deep Learning Aspirants and Data Analysts who are now looking to move into advanced machine learning and deep learning with C#. Prior knowledge of machine learning and working experience with C# programming is required to take most out of this book

What you will learn

  • •Understand perceptrons and how to implement them in C#
  • •Learn how to train and visualize a neural network using cognitive services
  • •Perform image recognition for detecting and labeling objects using C# and TensorFlowSharp
  • •Detect specific image characteristics such as a face using Accord.Net
  • •Demonstrate particle swarm optimization using a simple XOR problem and Encog
  • •Train convolutional neural networks using ConvNetSharp
  • •Find optimal parameters for your neural network functions using numeric and heuristic optimization techniques.
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Publication date : Sep 29, 2018
Length: 328 pages
Edition : 1st
Language : English
ISBN-13 : 9781789612011
Category :
Data
Languages :
C#
Concepts :
Neural Networks
Tools :
Accord.NET

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ISBN-13 : 9781789612011
Category :
Data
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Concepts :
Neural Networks
Tools :
Accord.NET

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Hands-On Neural Network Programming with C#
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Table of Contents

15 Chapters
A Quick Refresher Chevron down icon Chevron up icon
A Quick Refresher
Technical requirements
Neural network overview
The role of neural networks in today's enterprises
Types of learning
Understanding perceptrons
Understanding activation functions
Understanding back propagation
Summary
References
Building Our First Neural Network Together Chevron down icon Chevron up icon
Building Our First Neural Network Together
Technical requirements
Our neural network
Neural network training
Neural network functions
The neural network
Examples
Summary
Decision Trees and Random Forests Chevron down icon Chevron up icon
Decision Trees and Random Forests
Technical requirements
Decision trees
Random forests
SharpLearning
Example code and applications
Summary
References
Face and Motion Detection Chevron down icon Chevron up icon
Face and Motion Detection
Technical requirements
Facial detection
Motion detection
Summary
Training CNNs Using ConvNetSharp Chevron down icon Chevron up icon
Training CNNs Using ConvNetSharp
Technical requirements
Getting acquainted
Filters
Creating a network
GPU
Fluent training with the MNIST database
Training the network
Summary
References
Training Autoencoders Using RNNSharp Chevron down icon Chevron up icon
Training Autoencoders Using RNNSharp
Technical requirements
What is an autoencoder?
Different types of autoencoder
Creating your own autoencoder
Summary
References
Replacing Back Propagation with PSO Chevron down icon Chevron up icon
Replacing Back Propagation with PSO
Technical requirements
Basic theory
Replacing back propagation with Particle Swarm Optimization
Summary
Function Optimizations: How and Why Chevron down icon Chevron up icon
Function Optimizations: How and Why
Technical requirements
Getting started
Function minimization and maximization
Hyperparameters and tuning
Understanding visualizations
Plotting results
Adding new optimization functions
Summary
Finding Optimal Parameters Chevron down icon Chevron up icon
Finding Optimal Parameters
Technical requirements
Optimization
Optimization methods
Parallelism
Summary
References
Object Detection with TensorFlowSharp Chevron down icon Chevron up icon
Object Detection with TensorFlowSharp
Technical requirements
Working with Tensors
Developing your own TensorFlow application
Detecting images
Summary
References
Time Series Prediction and LSTM Using CNTK Chevron down icon Chevron up icon
Time Series Prediction and LSTM Using CNTK
Technical requirements
Long short-term memory
CNTK terminology
Our example
How well do LSTMs perform?
Summary
References
GRUs Compared to LSTMs, RNNs, and Feedforward networks Chevron down icon Chevron up icon
GRUs Compared to LSTMs, RNNs, and Feedforward networks
Technical requirements
QuickNN
Understanding GRUs
Differences between LSTM and GRU
Coding different networks
Comparing LSTM, GRU, Feedforward, and RNN operations
Network differences
Summary
Activation Function Timings Chevron down icon Chevron up icon
Activation Function Timings
Function Optimization Reference Chevron down icon Chevron up icon
Function Optimization Reference
The Currin Exponential function
The Webster function
The Oakley & O'Hagan function
The Grammacy function
Franke's function
The Lim function
The Ackley function
The Bukin function N6
The Cross-In-Tray function
The Drop-Wave function
The Eggholder function
The Holder Table function
The Levy function
The Levy function N13
The Rastrigin function
The Schaffer function N.2
The Schaffer function N.4
The Shubert function
The Rotated Hyper-Ellipsoid function
The Sum Squares function
The Booth function
The Mccormick function
The Power Sum function
The Three-Hump Camel function
The Easom function
The Michalewicz function
The Beale function
The Goldstein-Price function
The Perm function
The Griewank function
The Bohachevsky function
The Sphere function
The Rosenbrock function
The Styblinski-Tang function
Summary
Keep reading
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MrBigBeast Feb 10, 2020
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The book presents (sort of) code for a neural network.The good: it's all in C# without requiring outside packages. Interesting reference stuff towards the end.the bad: the code is cute, using lots of little C# specific tricks. Good luck porting this Java. One class appears on page 35, then its constructor is introduced a dozen or so pages later. Why? Code notes aren't commented.Why couldn't the author just cut and paste his actual code?The Packt Publishing outfit claims you can download the code from their web site. The site is dead. Once you register and agree to allow cookies, nothing works. The links that do respond take forever. Not a good sign for a publisher of programming books...
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About the author

Profile icon Matt Cole
Matt Cole
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Matt R. Cole is a developer and author with 30 years' experience. Matt is the owner of Evolved AI Solutions, a provider of advanced Machine Learning/Bio-AI, Microservice and Swarm technologies. Matt is recognized as a leader in Microservice and Artificial Intelligence development and design. As an early pioneer of VOIP, Matt developed the VOIP system for NASA for the International Space Station and Space Shuttle. Matt also developed the first Bio Artificial Intelligence framework which completely integrates mirror and canonical neurons. In his spare time Matt authors books, and continues his education taking every available course in advanced mathematics, AI/ML/DL, Quantum Mechanics/Physics, String Theory and Computational Neuroscience.
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Return Policy:

We want you to be happy with your purchase from Packtpub.com. We will not hassle you with returning print books to us. If the print book you receive from us is incorrect, damaged, doesn't work or is unacceptably late, please contact Customer Relations Team on customercare@packt.com with the order number and issue details as explained below:

  1. If you ordered (eBook, Video or Print Book) incorrectly or accidentally, please contact Customer Relations Team on customercare@packt.com within one hour of placing the order and we will replace/refund you the item cost.
  2. Sadly, if your eBook or Video file is faulty or a fault occurs during the eBook or Video being made available to you, i.e. during download then you should contact Customer Relations Team within 14 days of purchase on customercare@packt.com who will be able to resolve this issue for you.
  3. You will have a choice of replacement or refund of the problem items.(damaged, defective or incorrect)
  4. Once Customer Care Team confirms that you will be refunded, you should receive the refund within 10 to 12 working days.
  5. If you are only requesting a refund of one book from a multiple order, then we will refund you the appropriate single item.
  6. Where the items were shipped under a free shipping offer, there will be no shipping costs to refund.

On the off chance your printed book arrives damaged, with book material defect, contact our Customer Relation Team on customercare@packt.com within 14 days of receipt of the book with appropriate evidence of damage and we will work with you to secure a replacement copy, if necessary. Please note that each printed book you order from us is individually made by Packt's professional book-printing partner which is on a print-on-demand basis.

What tax is charged? Chevron down icon Chevron up icon

Currently, no tax is charged on the purchase of any print book (subject to change based on the laws and regulations). A localized VAT fee is charged only to our European and UK customers on eBooks, Video and subscriptions that they buy. GST is charged to Indian customers for eBooks and video purchases.

What payment methods can I use? Chevron down icon Chevron up icon

You can pay with the following card types:

  1. Visa Debit
  2. Visa Credit
  3. MasterCard
  4. PayPal
What is the delivery time and cost of print books? Chevron down icon Chevron up icon

Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela