Investigating the NIN Structure

Abstract: In this thesis the NIN artificial neural network structure created by Min Lin et al in 2014 is investigated. This is done by varying stage numbers and layer depth. By doing this ten different networks including the original NIN were created. Testing is carried out on a preprocessed version of the CIFAR10 dataset for these ten networks for a maximum of 150’000 iterations. The results show that the number of stages generally affect NIN performance more than layer depth does. The network with three stages and a layer depth of two performs best at a top accuracy of 87.44%. This is below Min Lin et al’s results. However, this is likely due to overfitting and lack of specifics on their training methods. The thesis concludes that studies of different types of micro-networks in the NIN are required. Studies are also required on deeper NINs with larger datasets to prevent the overfitting observed in the results. Larger datasets could be obtained by data augmentation. Furthermore, the results suggests that less complicated (by less complicated it is meant that the network stages have less depth) NIN implementations are more accurate than deeper ones.