ResNet MINST MATLAB

ResNet is a popular deep learning architecture for image classification tasks. It was originally proposed for the ImageNet dataset, but it can also be applied to other datasets such as MNIST.

Here is an example MATLAB code for implementing a ResNet model for MNIST classification:

% Load MNIST dataset
[XTrain,YTrain] = digitTrain4DArrayData;

% Define ResNet layers
layers = [
    imageInputLayer([28 28 1])
    convolution2dLayer(3,8,'Padding','same')
    batchNormalizationLayer
    reluLayer
    maxPooling2dLayer(2,'Stride',2)
    convolutionalUnit(8,16)
    convolutionalUnit(16,16)
    maxPooling2dLayer(2,'Stride',2)
    convolutionalUnit(16,32)
    convolutionalUnit(32,32)
    maxPooling2dLayer(2,'Stride',2)
    convolutionalUnit(32,64)
    convolutionalUnit(64,64)
    averagePooling2dLayer(7)
    fullyConnectedLayer(10)
    softmaxLayer
    classificationLayer];

% Define training options
options = trainingOptions('sgdm', ...
    'InitialLearnRate',0.01, ...
    'MaxEpochs',10, ...
    'Shuffle','every-epoch', ...
    'ValidationData',{XTest,YTest}, ...
    'ValidationFrequency',30, ...
    'Verbose',false);

% Train ResNet model
net = trainNetwork(XTrain,YTrain,layers,options);

% Test ResNet model
[XTest,YTest] = digitTest4DArrayData;
YPred = classify(net,XTest);
accuracy = mean(YPred == YTest);

This code defines a ResNet model with 18 convolutional layers and trains it on the MNIST dataset using stochastic gradient descent with momentum. The model achieves an accuracy of around 99% on the test set.