Neural network training method and device

Abstract

The embodiment of the invention discloses a neural network training method and device. According to the embodiment of the invention, the method includes: obtaining a native neural network with a to-be-determined connection relationship wherein the native neural network comprises at least two network layers, and each network layer comprises at least one training node; acquiring node connection strategy information of a current training node in a current network layer; determining a target connection node corresponding to the current training node from the training nodes of the target network layer based on the node connection strategy information; establishing a connection relationship between the current training node and the target connection node to obtain a connected neural network; performing forward transmission on the sample data in the connected neural network to obtain a prediction output result; and based on the prediction output result, updating weight information of each training node in the connected neural network so as to determine the trained neural network. According to the scheme, the efficiency of neural network training can be improved, and the over-fitting phenomenon in neural network training can be improved.

Description

technical field [0001] The present application relates to the field of computer technology, in particular to a neural network training method and device. Background technique [0002] The artificial neural network can abstract the human brain neuron network from the perspective of information processing to build a model and form different networks according to different connection methods. For example, it can form a deep neural network including multiple hidden layers. With the continuous in-depth research on artificial neural networks, people have made great progress in various fields. [0003] In the process of research and practice of related technologies, the inventors of the present application found that in the training process of the deep neural network model, the problem of over-fitting often easily occurs, resulting in the model performing well on the training set, but in the test The performance on the set is poor, so that the trained model does not understand the...

AI Technical Summary

Problems solved by technology

[0003] In the process of research and practice of related technologies, the inventors of the present application found that in the training process of the deep neural network model, the problem of over-fitting often easily occurs, resulting in the model performing well on the training set, but in the test The performance on the set is poor, so that the trained model does not understand the laws behind the data and has poor generalization ability

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