  Natural gradient descent (NGD) provided deep insights and powerful tools to
deep neural networks. However the computation of Fisher information matrix
becomes more and more difficult as the network structure turns large and
complex. This paper proposes a new optimization method whose main idea is to
accurately replace the natural gradient optimization by reconstructing the
network. More specifically, we reconstruct the structure of the deep neural
network, and optimize the new network using traditional gradient descent (GD).
The reconstructed network achieves the effect of the optimization way with
natural gradient descent. Experimental results show that our optimization
method can accelerate the convergence of deep network models and achieve better
performance than GD while sharing its computational simplicity.
