A New Optimizer for Image Classification using Wide ResNet (WRN)

A new optimization algorithm called Adam Meged with AMSgrad (AMAMSgrad) is modified and used for training a convolutional neural network type Wide Residual Neural Network, Wide ResNet (WRN), for image classification purpose. The modification includes the use of the second moment as in AMSgrad and the use of Adam updating rule but with and (2) as the power of the denominator. The main aim is to improve the performance of the AMAMSgrad optimizer by a proper selection of and the power of the denominator. The implementation of AMAMSgrad and the two known methods (Adam and AMSgrad) on the Wide ResNet using CIFAR-10 dataset for image classification reveals that WRN performs better with AMAMSgrad optimizer compared to its performance with Adam and AMSgrad optimizers. The accuracies of training, validation and testing are improved with AMAMSgrad over Adam and AMSgrad. AMAMSgrad needs less number of epochs to reach maximum performance compared to Adam and AMSgrad. With AMAMSgrad, the training accuracies are (90.45%, 97.79%, 99.98%, 99.99%) respectively at epoch (60, 120, 160, 200), while validation accuracy for the same epoch numbers are (84.89%, 91.53%, 95.05%, 95.23). For testing, the WRN with AMAMSgrad provided an overall accuracy of 94.8%. All these accuracies outrages those provided by WRN with Adam and AMSgrad. The classification metric measures indicate that the given architecture of WRN with the three optimizers performs significantly well and with high confidentiality, especially with AMAMSgrad optimizer.