  Adam is applied widely to train neural networks. Different kinds of Adam
methods with different features pop out. Recently two new adam optimizers,
AdaBelief and Padam are introduced among the community. We analyze these two
adam optimizers and compare them with other conventional optimizers (Adam, SGD
+ Momentum) in the scenario of image classification. We evaluate the
performance of these optimization algorithms on AlexNet and simplified versions
of VGGNet, ResNet using the EMNIST dataset. (Benchmark algorithm is available
at
\hyperref[https://github.com/chuiyunjun/projectCSC413]{https://github.com/chuiyunjun/projectCSC413}).
