train a deep dlnetwork in matlab

To train a deep neural network in Matlab, you should follow these general steps:

1. Define your neural network architecture using one of Matlab's neural network functions such as feedforwardnet or convnet.

2. Prepare your training data by organizing it into an appropriate data structure such as an image datastore or table.

3. Specify a set of training options such as the number of epochs, learning rate, and mini-batch size using the trainingOptions function.

4. Train the neural network using the trainNetwork function, passing in your network, training data, and training options as arguments.

Here is an example code snippet that trains a simple feedforward neural network on a sample dataset:

main.m
% Load the sample dataset
load simplefit_dataset
x = inputs;
t = targets;

% Define the neural network architecture
net = feedforwardnet(10);

% Specify training options
options = trainingOptions('sgdm', ...
    'MaxEpochs',500, ...
    'LearningRate',0.01, ...
    'MiniBatchSize',16);

% Train the neural network
net = trainNetwork(x,t,net,options);
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In this example, the feedforwardnet function is used to define a neural network with one hidden layer of 10 nodes. The trainingOptions function sets the optimizer to stochastic gradient descent with momentum (sgdm) and specifies a maximum of 500 training epochs, a learning rate of 0.01, and a mini-batch size of 16. The trainNetwork function is then used to train the network on the input x and target t data using the previously specified options.