Construction method and device of deep neural network model, medium and electronic equipment

Abstract

The embodiment of the invention discloses a construction method and a device of a deep neural network model, a medium and electronic equipment. The method comprises the steps of inputting labeled sample data into a first neural network model to obtain feature representation of the labeled sample data; wherein the first neural network model is obtained by training parameters of a network structureof the first neural network model according to the label-free sample data; and inputting the feature representation of the labeled sample data and the labeled data into a second neural network model so as to train parameters of the second neural network model. By operating the construction method provided by the invention, the purposes of ensuring a high-quality model training effect and labelingmore data without consuming a large amount of human resources under the condition of limited labeled training sample resources can be achieved.

Description

technical field [0001] The embodiments of the present application relate to the field of computer technology, and in particular to a method, device, medium, and electronic equipment for constructing a deep neural network model. Background technique [0002] With the rapid development of technology, understanding the real intention of users has become the new standard of intelligence. In the process of processing natural language, it is often necessary to build a neural network model to realize the processing of natural language text implication, intelligent question and answer, semantic similarity judgment, and text classification to obtain the user's true intention. [0003] However, the current neural network model construction process often has two problems. On the one hand, if unlabeled data is used as training samples, although there are many sample data, the characteristics of the trained neural network model are relatively generalized, and cannot provide high-quality...

AI Technical Summary

Problems solved by technology

On the one hand, if unlabeled data is used as training samples, although there are many sample data, the characteristics of the trained neural network model are relatively generalized, and cannot provide high-quality output results for specific problems.

On the other hand, if labeled data is used as training samples, due to the relatively scarce amount of data and the difficulty in obtaining it, it may result in insufficient training of the model, resulting in a poor training effect of the model.

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