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Convolutional neural networks have been achieving the best possibleaccuracies in many visual pattern classification problems. However, due to themodel capacity required to capture such representations, they are oftenoversensitive to overfitting and therefore require proper regularization togeneralize well. In this paper, we present a combination of regularizationtechniques which work together to get better performance, we built plain CNNs,and then we used data augmentation, dropout and customized early stoppingfunction, we tested and evaluated these techniques by applying models on fivefamous datasets, MNIST, CIFAR10, CIFAR100, SVHN, STL10, and we achieved threestate-of-the-art-of (MNIST, SVHN, STL10) and very high-Accuracy on the othertwo datasets.

Stochastic Optimization of Plain Convolutional Neural Networks with Simple methods