deep learning in matlab

Matlab provides a platform to build and train neural networks for deep learning applications. Here’s a simple step-by-step guide to getting started with deep learning in Matlab:

1. Load your data: You’ll need to load your data into Matlab to begin training your neural network. This can be done using the imread function to load images or the csvread function to load numerical data.

2. Preprocess your data: It’s important to preprocess your data before training your network. This could involve normalizing the data, resizing images or converting data types. Matlab provides a range of functions for data preprocessing.

3. Define your network architecture: Matlab provides an easy-to-use Neural Network Toolbox that makes defining neural networks simple. You can use pre-built network architectures or create your own customized network with the desired number of layers, activation functions and training algorithm.

4. Train your network: Once you’ve defined your network architecture, you can train it using the trainNetwork function. You’ll need to specify the training data, validation data and specify the training parameters such as learning rate, momentum and number of epochs.

5. Test your network: After training, you’ll need to test your network using a separate test dataset. This can be done using the classify function which returns the predicted class of your input data.

Here’s some sample code to get started with creating a simple neural network in Matlab:

main.m
% Load data
[XTrain,YTrain] = digitTrain4DArrayData;

% Preprocess data
[imdsTrain,imdsValidation] = splitEachLabel(imds,0.7,'randomized');
augimdsTrain = augmentedImageDatastore(inputSize,XTrain,YTrain);

% Define network architecture
layers = [
    imageInputLayer([28 28 1])
    convolution2dLayer(5,20)
    reluLayer
    maxPooling2dLayer(2,'Stride',2)
    fullyConnectedLayer(10)
    softmaxLayer
    classificationLayer];

% Train network
options = trainingOptions('sgdm','Plots','training-progress');
net = trainNetwork(augimdsTrain,layers,options);

% Test network
[XTest,YTest] = digitTest4DArrayData;
augimdsTest = augmentedImageDatastore(inputSize,XTest,YTest);
YPred = classify(net,augimdsTest);
708 chars
26 lines