How to design a single layer perceptron with MATLAB built-in functions/nets/Apps?

Suppose I have 1000 images of 512 pixels each. I want to design a single layer perceptron and to track the accuracy of the validation/test and the train datasets, but I don't know where to start? Is there a MATLAB built-in function where I could do it? Or how do I write it in code?

% % Data Splitting
[setTrain, setTest] = partition(Images, [0.8, 0.2], 'randomized');


%% Defining the perceptron

n=1; 
weights(:,n)=rand(1,1000); 
eta=0.1; 
epochs=50;
for i=1:epochs 
for j=1:length(Images) 
v=weights(:,n)'*x(:,j); 



function out=hardlimit(v)

for i=1:numel(v)
    if v(i)<0
        out(i)=0;
    else
        out(i)=1;
    end
end


error_train = 1 ;
error_test = 1; 
error_perc_test=1; 

Answer 1

[Ayush Aniket]

You can design a single-layer perceptron in MATLAB using built-in functions from the Deep Learning Toolbox. MATLAB provides functions like feedforwardnet, perceptron and train to simplify the process.Refer the code snippet below:

% 1. Load and Split Data
[setTrain, setTest] = partition(Images, [0.8, 0.2], 'randomized');

% 2. Define the Perceptron Model
net = perceptron;

% 3. Train the Model
net = train(net, setTrain, labelsTrain);

% 4. Evaluate Accuracy
predictions = net(setTest);
accuracy = sum(predictions == labelsTest) / numel(labelsTest);

Answer 2

[Philip Brown]

To do this with Deep Learning Toolbox, you can follow an example like this one: Get Started with Image Classification. This will cover splitting data into training and validation, and tracking training progress.

That showcases how to build a convolutional neural network using Deep Network Designer. If you want to build a 1-layer perceptron, instead of adding a convolutional layer in the example, add a fully-connected layer from the palette.

Note that a perceptron network (using fully-connected layers) will be less parameter-efficient than a convolutional neural network; the convolutional filters can be applied all across the image, learning spatially-local features, while a fully-connected layer is learning connections between pixels all across the image. Convolutional layers are typically used instead of fully-connected layers when working with image data for this reason.