Questions raised
As the network stack has been deepened, the network will encounter gradient disappear or gradient explosion problem, and this problem may have been solved by normalizing input during initialization, but when the final network convergence, will a "degradation" problem , resulting in lower accuracy rate, so although you can continue to stack layers of the network, so that it could train and convergence, but still can not meet degradation.
