How to Create a CNN for Classification in Matlab for Digit Classification - code is available

#cnn #matlab #recognition #classification
This video shows how to create and train a simple convolutional neural network for deep learning classification. Convolutional neural networks are essential tools for deep learning and are especially suited for image recognition.
The example demonstrates how to:
- Load and explore image data.
- Define the network architecture.
- Specify training options.
- Train the network.
- Predict the labels of new data and calculate the classification accuracy.

digitDatasetPath = fullfile(matlabroot,'toolbox','nnet','nndemos', ...
'nndatasets','DigitDataset');
imds = imageDatastore(digitDatasetPath, ...
'IncludeSubfolders',true,'LabelSource','foldernames');
figure;
perm = randperm(10000,20);
for i = 1:20
subplot(4,5,i);
imshow(imds.Files{perm(i)});
end

labelCount = countEachLabel(imds)
numTrainFiles = 750;
[imdsTrain,imdsValidation] = splitEachLabel(imds,numTrainFiles,'randomize');

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];

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

net = trainNetwork(imdsTrain,layers,options);

YPred = classify(net,imdsValidation);
YValidation = imdsValidation.Labels;

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