Image Classification using DNN with an Improved Optimizer

In deep learning, the optimization techniques are for the most part dependent on gradient descent methods, such as SGD, ADAM which adopt the leading place in the area of optimization methods. Fortuitous methodologies which depend on stochastic gradients are non-adaptive because the prescribed parameter worth's usage should be tuned for every application. But the generalization performance of the stochastic optimizers is far superior to the adaptive methods, whereas Adam and its variants cannot maintain this without a fast convergence rate in deep neural networks. To improve this generalization performance, we need to diminish the oscillation of the weights which is the general problem of the accuracy fall. Along these lines, we have attempted to propose Mean- ADAM, a variance of ADAM which has extended the updated weights by an external weight to diminish the oscillation and overcome a superior accuracy rate than all other adaptive gradient methods till the conclusion of the breeding. Therefore, we can substantially improve the generalization performance, permitting it to contend with SGD with momentum on image classification datasets such as MNIST, CIFAR10, CIFAR100, ImageNet, etc. We have attained 82% at 150 epochs with CIFAR10 and 99.49% with MNIST whereas the ADAM has indicated 76% and 99.43% individually.