I run a patternnet 4 times, each time resetting the random number generator:
clear all
close all
clc
[x,t] = iris_dataset;
rng('default');
net1 = patternnet(10);
[net1, tr1] = train(net1,x,t);
figure(1);
plotperform(tr1);
rng('default');
net2 = patternnet(10);
[net2, tr2] = train(net2,x,t);
figure(2);
plotperform(tr2);
rng('default');
net3 = patternnet(10);
[net3, tr3] = train(net3,x,t);
figure(3);
plotperform(tr3);
rng('default');
net4 = patternnet(10);
[net4, tr4] = train(net4,x,t);
figure(4);
plotperform(tr4);
But get two different training record outputs (excuse the sizing and resolution of the images - I've just shrunk their screenshots in Word to put them into one image):
The difference is very small in terms of the CE and epoch time for the iris dataset. But for the dataset I am using, it is quite large for the best performing epoch (only using a training and validation dataset):
The epoch is important for what I'm doing because I want to run it with the parameters for the best performing epoch - the train() function does not return the best performing NN, but instead the NN for the last run epoch. What I am doing to get around this is training the NN for a specified number of epochs, then taking note of which is the best performing epoch, and then train it again to reach that epoch and then run my test dataset on it. So, knowing exactly which is the best performing epoch and being able to reproduce it is important (unless there is another way to have train() return the network at the best performing epoch which I don't know of).
What is the cause of this discrepancy between the first training run and the subsequent runs? Should the first training run results of pattern net always be discarded because they don't seem to be the best and don't seem to be replicated in the subsequent runs? Is this a bug?
