4CCE1PHC Machine Learning with a Neural Network 2021
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4CCE1PHC
Assignment: Machine Learning with a Neural Network
2021
1 Introduction
As a C programmer (and an engineer), you will frequently be asked to use your specialist skills to contribute functional code as a smaller part of a larger project. In this assignment, you will write code to implement components and functions of a larger C program that implements a simple, artificial neural network (ANN), a kind of machine learning system.
Note that, you do not need any in-depth knowledge of ANNs or any other machine learning technique to complete this assignment.
2 Background
Artificial neural networks are kind of model often used in artificial intelligence for allowing machines to learn how to solve problems. They work by taking data about a problem, and training until they can solve it accurately. Originally, they were developed to imitate how the human brain solves problems, but since then ANNs of many shapes and sizes have been developed, including very large ones (so-called deep learning models).
A conceptual diagram of the ANN you will be working with is shown in Fig. 1.
The ANN works by propagating signals from left to right. Each big circle represents a “neuron” and takes a set of numbers as inputs (marked by the arrows going into the neurons in the diagram), performs some simple mathematical operations on them, and produces an output (marked by the arrows going out of the neurons). This output may be fed as the input to another neuron, or it may form the output of the whole network.
The network in Fig. 1 is designed to learn to solve a famous machine learning problem called the exclusive or (“XOR”) problem. The aim is to get the network to learn how an XOR logic gate works. To do this, the network takes two inputs and produces one output. The inputs can take the values one or zero (i.e., true or false), and the network should output one (i.e., true) if either of the two inputs are true, and zero (i.e., false) if (i) both of the inputs are false or (ii) both of the inputs are true. The truth table for all possible inputs are shown in Table 1.
The neural network program should learn to predict the correct output, given each of these possible inputs.
3 Preparation
To complete the assignment, you will need the following files:
● in.csv
● out.csv
● ann.c
● libann.h
● libann.c
● k0000000.c
Download these from KEATS and save them to your computer.
Important in.csv and out.csv are comma separated value files, a simple format in which values are stored as plain text. When downloading these, make sure you do not accidentally save them as another format (e.g., Excel, ODS). To test that you have downloaded them correctly, open the file in a plain text editor and check that you can read the values. For instance, when you open in.csv in a text editor you should see the following:
|
0 ,0 0 ,1 1 ,0 1 ,1 |
Use the following commands to compile the program:
|
$ gcc libann . c k0000000 . c ann . c -o ann $ ./ ann |
You should find that it compiles without errors.1
If you have completed this correctly, you should see the following output in your terminal:
|
0 0 0 0 |
Important In the following, you will be asked to implement functions in k0000000.c to create a working ANN. When completing the tasks, be sure to only implement code at the locations indicated in the tasks. Do not change the number or type of parameters used in the functions nor the type of their return values. Do not modify any file, other than k0000000.c. You are encouraged to comment your code to aid under- standing for the markers.
4 Tasks
Complete the following tasks.
1. The first task is to write a function to read in the data required for training the network, or report an error if the data cannot be found.
To complete this part of the assignment, find and modify the following lines in the template file:
|
/* - - - - - - - - - - - - - - - Begin Answer to Task return 0; /* - - - - - - - - - - - - - - - End Answer to Task 1 |
1 Here - - - - - - - - - - - - */ Here - - - - - - - - - - - - - - */ |
The function should take pointers to the arrays trIn and trOut, and the strings input_data_file and output_data_file as arguments. Implement code that reads the training data from the files in.csv and out.csv and stores them in the arrays trIn and trOut.
The first index of the array trIn should correspond to the index of the data point (i.e., row of in.csv) and the second should correspond to the index of the input (i.e., Input 1 or Input 2 ).
The function should return one and exit immediately if any errors are encountered when reading the files, or zero otherwise.
[6 marks] 2. The next task is to implement the basic building blocks of the neural network: the neurons.
To complete this part of the assignment, find and modify the following lines in the template file:
|
/* - - - - - - - - - - - - - - - Begin Answer to Task return 0; /* - - - - - - - - - - - - - - - End Answer to Task 2 |
2 Here - - - - - - - - - - - - */ Here - - - - - - - - - - - - - - */ |
Implement the function neuron() to compute the output of a neuron z from its inputs xi, using the following steps.
Step 1. Each of the inputs xi should be multiplied by a weighting factor wi and added together
according to the equation
where b is a constant (called the bias term) and d is the number of inputs.
Step 2. The resultant value a should be passed through another function (called the activation
function) to compute the output
As the activation function, use the equation
This is called the sigmoid function.
The template code k0000000.c contains an empty definition of a function neuron() to compute the output of a neuron from its inputs. It takes the following arguments: (i) the number d of inputs d, (ii) a pointer to an array x of size d that contains the actual input values xi , (iii) a pointer to an array w of size d that contains the weights for the neuron wi, and (iv) b that represents the bias term b. The return value should be the output of the neuron z. Complete the implementation of the function so that it computes and returns the output of a neuron from these input parameters, following Step 1. and Step 2.
[6 marks]
3. The final task is to write code to train the neural network on the data.
To complete this part of the assignment, find and modify the following lines in the template file: