基于多层感知机的图像分类

Multilayer perceptron (MLP) is a feedforward neural network that overcomes the limitations of linear models and opens the door to deep learning by adding one or more hidden layers to the network. In this paper, multilayer perceptrons are used to classfy image, which is explored on the Fashion MNIST dataset, and is attempted to be migrated to the MNIST dataset. In Fashion MNIST, we selected different optimization methods and compared them after feature preprocessing, optimized and improved the multi-layer perceptron by adding regularization methods such as dropout and weight decay.<br /><br />Experiments show that appropriate feature processing can improve the numerical stability of the model. The momentum method significantly improves the effect of the model, weight decay and other regularization methods help to improve the generalization effect of the model.