[Advanced] Application of Convolutional Neural Networks (CNN) in MATLAB

# **Advanced篇** Convolutional Neural Networks (CNN) in MATLAB Convolutional Neural Networks (CNN) are a type of deep learning model that has achieved great success in image processing, natural language processing, and many other fields. The defining feature of CNNs is their convolutional layers, which use convolutional operators to extract features from input data. The convolutional operator is a small matrix that slides over the input data, multiplying elements and summing them to produce a feature map. Each element in the feature map represents the strength of a specific pattern in the input data. By stacking multiple convolutional layers, CNNs can extract increasingly complex features, thus enabling the recognition of intricate patterns. # ***N Programming in MATLAB ### 2.1 Implementation of CNN in MATLAB **2.1.1 Defining CNN Layer Structure** In MATLAB, Deep Learning Toolbox is used to define CNN layer structures, utilizing functions such as `convolution2dLayer`, `maxPooling2dLayer`, `fullyConnectedLayer`, and so on. ``` % Defining a simple CNN layer structure layers = [ imageInputLayer([28 28 1]) convolution2dLayer(5, 20) maxPooling2dLayer(2, 'Stride', 2) convolution2dLayer(5, 50) maxPooling2dLayer(2, 'Stride', 2) fullyConnectedLayer(10) softmaxLayer classificationLayer ]; ``` **Parameter Explanation:** * `imageInputLayer`: Defines the size and number of channels of the input image. * `convolution2dLayer`: Defines the convolutional layer, specifying the size of the convolution kernels and the number of kernels. * `maxPooling2dLayer`: Defines the max pooling layer, specifying the size of the pooling window and stride. * `fullyConnectedLayer`: Defines the fully connected layer, specifying the number of output feature maps. * `softmaxLayer`: Applies the softmax activation function, converting feature maps into probability distributions. * `classificationLayer`: Defines the classification layer, specifying the number of classification labels. ### 2.1.2 Training and Evaluating CNN Models Training and evaluating CNN models involve the following steps: ``` % Loading training data data = load('train_data.mat'); XTrain = data.XTrain; YTrain = data.YTrain; % Creating a CNN model net = network(layers); % Defining training options options = trainingOptions('sgdm', ... 'InitialLearnRate', 0.01, ... 'MaxEpochs', 10, ... 'MiniBatchSize', 128, ... 'ValidationData', {XTrain, YTrain}, ... 'ValidationFrequency', 100); % Training the CNN model net = trainNetwork(XTrain, YTrain, net, options); % Evaluating the CNN model YPred = classify(net, XTrain); accuracy = mean(YPred == YTrain); ``` **Parameter Explanation:** * `load`: Loads the training data. * `network`: Creates the CNN model. * `trainingOptions`: Defines training options, including learning rate, maximum number of iterations, batch size, validation data, and validation frequency. * `trainNetwork`: Trains the CNN model. * `classify`: Classifies data using a trained model. * `mean`: Calculates the classification accuracy. # 3.1 Image Classification and Recognition **3.1.1 Preprocessing and Data Augmentation** Image classification and recognition are typical applications of CNN in image processing. Before performing image classification and recognition tasks, it is necessary to preprocess and augment the image data to improve the model's generalization ability and robustness. **Preprocessing** The preprocessing steps mainly include: - **Image resizing:** Adjusting images to a uniform size to meet the model input requirements. - **Image normalization:** Normalizing image pixel values to the range of [0, 1] or [-1, 1] to reduce the effects of lighting changes and contrast differences. - **Image flipping and rotation:** Randomly flipping and rotating images to increase dataset diversity. **Data Augmentation** Data augmentation techniques can generate new image samples, ***mon data augmentation techniques include: - **Random cropping:** Randomly cropping regions of different sizes and positions from images. - **Random horizontal flipping:** Randomly fli