This example shows how to use plotperform to obtain a plot of training record error values against the number of training epochs.
[x,t] = bodyfat_dataset;
net = feedforwardnet(10);
[net,tr] = train(net,x,t);
plotperform(tr)
Generally, the error reduces after more epochs of training, but might start to increase on the validation data set as the network starts overfitting the training data. In the default setup, the training stops after six consecutive increases in validation error, and the best performance is taken from the epoch with the lowest validation error.
Training record (epoch and perf), returned as
a structure whose fields depend on the network training function
(net.NET.trainFcn). It can include fields such as:
Training, data division, and performance functions and parameters
Data division indices for training, validation and test sets
Data division masks for training validation and test sets
Number of epochs (num_epochs) and the best epoch
(best_epoch)
A list of training state names (states)
Fields for each state name recording its value throughout training
Performances of the best network (best_perf,
best_vperf, best_tperf)
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