Dynamic depth confidence network analysis method

Abstract

The invention relates to the field of deep learning and relates to a dynamic network structure training analysis method based on a depth confidence network. The invention aims to overcome the severaldefects of the depth confidence network, and an overall-special dynamic network structure training analysis strategy from and based on the depth confidence network is provided. According to the strategy, a fine analysis process is introduced according to an analysis strategy of the brain when the human identifies an object. According to the strategy, two networks are generated in the training stage for the classification problem and comprise a global network and a plurality of special networks for specific categories. In the prediction stage, the output results of the two networks are comprehensively considered, so that the prediction of the whole model is more targeted, and the accuracy of the classification model is improved.

Description

technical field [0001] The invention relates to a dynamic network structure training analysis method based on a deep belief network in the field of deep learning. technical background [0002] ●Deep Belief Network [0003] Deep belief network (Deep belief network, DBN) is a probabilistic generation model in the field of deep learning. This model was proposed by Geoffrey Hinton in his paper "A Fast Learning Algorithm For Deep Belief Nets" published in 2006. It is usually used for classification . As opposed to traditional neural networks for discriminative models, deep belief networks are used to build a joint distribution between observations and labels. [0004] Theoretically, for the neural network structure, the more layers of hidden layers, the stronger the expressive power of the model. However, when the number of network layers gradually deepens, using the traditional gradient descent algorithm to optimize parameters will cause some problems. For example, when calc...

AI Technical Summary

Problems solved by technology

[0007] The deep belief network has several obvious shortcomings: 1. When training the model, it strives to find the comprehensive optimal fit of all the data in the entire training set, without considering some special classes; 2. After the entire model is trained, it will not be changed. , all the test set data are input into a model for prediction, which is convenient but lacks certain pertinence

Images