Hi Maria,
The trainNetwork function's default training progress plot displays the mini-batch loss (such as MSE) and accuracy for each iteration.
- However, it does not directly provide options to display metrics computed over the entire training or validation set during training.
- To achieve this, the training loop should be customised using a custom training loop approach.
Here’s a possible workaround:
- Set hyperparameters like learning rate, number of epochs, and mini-batch size.
- Iterate over number of epochs.
- Within each epoch, iterate over mini-batches and compute predictions for mini-batch.
- Compute the gradients of the loss with respect to model parameters.
- Improve the model's performance by minimizing the loss using an optimization algorithm.
- Compute the MSE over the training and validation datasets after each mini-batch update. Here’s a sample code snippet:
%net -Network, (XTrain,YTrain)-Training data,
% (XValidation, YValidation)-Validation data
YPredTrain = predict(net, XTrain);
trainMSE = mean((YPredTrain - YTrain).^2);
YPredValidation = predict(net, XValidation);
validationMSE = mean((YPredValidation - YValidation).^2);
- Plot the MSE for both training and validation datasets throughout the training process using “addpoints” and “drawnow”.
trainingPlot = animatedline('Color','r');
validationPlot = animatedline('Color','b');
addpoints(trainingPlot, iteration, trainMSE);
addpoints(validationPlot, iteration, validationMSE);
drawnow;
Refer to the following MathWorks documentation for more information on:
