A Self-adaptive Learning Rate Principle for Stacked Denoising Autoencoders

Existing research on image classification mainly used the artificial definition as the pre-training of the original image,which cost a lot of time on adjusting parameters.However,the depth of learning algorithm intends to make the computers automatically choose the most suitable features in the training process.The substantial of deep learning is to train mass data and obtain an accurate classification or prediction without any artificial work by constructing a multi-hidden-layer model.However,current deep learning model has problems of local minimums when choosing a constant learning rate to solve non-convex objective cost function in model training.This paper proposes an algorithm based on the Stacked Denoising Autoencoders(SDA)to solve this problem,and gives a contrast of different layer designs to test the performance.A MNIST database of handwritten digits is used to verify the effectiveness of this model..