Neural network model training method and device

Abstract

The invention is suitable for the technical field of machine learning, and provides a neural network model training method, which comprises the steps of inputting a training sample set into a preset student neural network model for processing to obtain first feature information, and calculating a first loss value according to the first feature information and a first loss function; inputting the training sample set into a preset teacher neural network model for processing to obtain fourth feature information, and calculating a second loss value according to the first feature information, the fourth feature information and a second loss function; determining a target loss value according to the first loss value and the second loss value; if the target loss value does not meet the preset stopping condition, updating the preset student neural network according to the target loss value; and if the target loss value meets the preset stopping condition, outputting the trained student neural network model. According to the invention, the training convergence condition of the neural network model can be effectively improved, so that the neural network can better learn features, and the recognition precision and generalization ability of the neural network are improved.

Description

technical field [0001] The present application belongs to the technical field of machine learning, and in particular relates to a training method and equipment for a neural network model. Background technique [0002] With the development of artificial intelligence, deep learning is increasingly showing its irreplaceability. During the training process, the loss function will be set in advance, and the characteristics of the data will be extracted by passing the training data to the preset neural network for forward propagation, and then the extracted features will be substituted into the loss function to calculate the loss value, and finally according to the loss value The preset parameters of the preset neural network model are updated by using the backpropagation algorithm. [0003] However, the loss function in the prior art generally adopts a single loss function, which will lead to insufficient features learned by the trained model or slow learning speed and low gener...

AI Technical Summary

Problems solved by technology

[0004] The embodiment of the present application provides a training method and equipment for a neural network model, which can solve the problem that the learned features of the current trained model are not sufficient or the learning speed is slow and the generalization ability is not high

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