How to Build A Convolutional Neural Network In MATLAB

 MATLAB is a numerical computing environment and programming language that is commonly used for technical computing and engineering applications. There are a few things that may make MATLAB seem easier to use than Python for certain tasks:

• Syntax: MATLAB has a simpler and more consistent syntax than Python, which can make it easier to learn and use for certain types of tasks, especially those that involve matrix manipulation or numerical computation.
• Toolboxes: MATLAB comes with a large number of pre-built toolboxes, which are collections of functions that are specialized for specific applications. These toolboxes can make it easier to perform certain types of tasks, such as image processing, signal processing, or optimization.
• Integrated development environment (IDE): MATLAB has a built-in IDE, which includes features such as code highlighting, debugging tools, and a graphical user interface (GUI) builder. These features can make it easier to develop and debug code, especially for users who are new to programming.
  Source: https://pyzo.org/python_vs_matlab.html

That being said, Python is a very powerful and flexible programming language that has a large and active community of users. It is also widely used in many fields, including scientific computing, data analysis, and machine learning. In some cases, Python may be a better choice than MATLAB, depending on the specific requirements and needs of a project.

How To Create a CNN in MATLAB!

To build a convolutional neural network (CNN) in MATLAB, you will need to perform the following steps:

• Load your dataset: You can use the 'ImageDatastore' function to load and store your images
• Preprocess the data: You may need to resize, crop, or augment your images to ensure that they are all the same size and have similar features.
• Define the CNN architecture: You can use the convolutional layer and the fully connected layer functions to specify the structure of your CNN.
• Set the training options: You will need to specify the training algorithm, the number of epochs, the mini-batch size, and other training options.
• Train the CNN: Use the 'trainNetwork' function to train your CNN on your dataset.
• Test the CNN: Use the classify function to test your CNN on a separate set of images and evaluate its performance.

Here is an example of how you might build a simple CNN in MATLAB:

% Load the training data
imds = imageDatastore('trainingset');

% Preprocess the data
imds = augment(imds);
imds = randomize(imds);
[imdsTrain,imdsValidation] = splitEachLabel(imds,0.7,'randomized');

% Define the CNN architecture
layers = [    imageInputLayer([28 28 1])
    
    convolution2dLayer(3,8,'Padding','same')
    batchNormalizationLayer
    reluLayer
    
    maxPooling2dLayer(2,'Stride',2)
    
    convolution2dLayer(3,16,'Padding','same')
    batchNormalizationLayer
    reluLayer
    
    maxPooling2dLayer(2,'Stride',2)
    
    convolution2dLayer(3,32,'Padding','same')
    batchNormalizationLayer
    reluLayer
    
    fullyConnectedLayer(10)
    softmaxLayer
    classificationLayer];

% Set the training options
options = trainingOptions('sgdm', ...
    'InitialLearnRate',0.01, ...
    'MaxEpochs',4, ...
    'Shuffle','every-epoch', ...
    'ValidationData',imdsValidation, ...
    'ValidationFrequency',30, ...
    'Verbose',false, ...
    'Plots','training-progress');

% Train the CNN
net = trainNetwork(imdsTrain,layers,options);

% Test the CNN
YPred = classify(net,imdsValidation);
YValidation = imdsValidation.Labels;

accuracy = sum(YPred == YValidation)/numel(YValidation)