Neural network achieved its best performance after only 21 iterations as shown in figure 5-1. The graph shows three curves, training, validation and test. At the beginning, model started to improve and after 15 iterations, it produced best validation performance. In MATLAB, once best validation performance is achieved, the training is continued for six more iterations and then stopped. It can be observed that, after fifteen iterations, the model started to over fit the data and validation curves started to rise.
Cross entropy plot is the measure of the quality of neural network predictions rather than the classification error. Classification error only shows the number of classifications whereas the cross entropy shows the quality of the prediction. The training error after 21 iterations reduced to 4.3% whereas the testing error was about
Confusion matrix is a very simple tool use to analyse the performance of a classifier. Confusion matrix for our classification problem is shown in figure 5-2.
Left confusion matrix shows overall classification accuracy of the neural network. It shows overall accuracy of about 94%. 2 out of 27 samples of good quality lemons were classified as average quality and 1 was classified as defective or unripe since the third category combines both defective and green lemons. There is no misclassification for average lemons therefor an accuracy of 100% was obtained shown in column four. Three of the defective or unripe lemons were misclassified, only was classified as good and 2 were classified as average quality.
Fourth row of the confusion matrix true positives and false positives rate. Figure shows that a total of 25 lemons were classified as good quality and only one lemon in this class was false positive compared to average class were out of 22 lemons, 4 were false positive. Fifty-two lemons were classified as defective of unripe out of which only one was false positive. No lemon from average class was misclassified to other classes and this class also have highest false positives.