Data processing method and device and storage medium

Abstract

The embodiment of the invention provides a data processing method. The data processing method comprises the steps: obtaining at least one sample of a target object; performing feature extraction on atleast one sample by using a first neural network to obtain at least one piece of first feature data, wherein the first feature data is used for indicating features of at least one sample; performingfeature extraction on at least one piece of first feature data by using a second neural network to obtain at least one piece of second feature data, wherein the second feature data is used for indicating change features of the corresponding first feature data; and making a decision according to at least one second feature data to obtain decision data. According to the data processing method provided by the embodiment of the invention, the first neural network and the second neural network are used for carrying out feature extraction on the sample twice, so the data capable of representing theevent represented by the sample and the decision data are obtained, the training data volume and the training time used by the deep learning model are reduced, and the training efficiency of the deeplearning model is improved.

Description

technical field [0001] The embodiments of the present application relate to the field of neural networks, and in particular, to a data processing method, device, and storage medium. Background technique [0002] With the continuous development of computer science and technology, deep learning technology has more and more profound influence on various fields. Deep learning is to learn the internal laws and representation levels of sample data. The information obtained during the learning process is of great help to the interpretation of data such as text, images and sounds. Its ultimate goal is to enable machines to have the ability to analyze and learn like humans, and to be able to recognize data such as text, images, and sounds. However, the existing deep learning model training methods require a large amount of training data or time. For example, alphastar needs 4,500 game hours equivalent to learning the strategy game StarCraft, and openai needs 10,000 years of training...

AI Technical Summary

Problems solved by technology

However, the existing deep learning model training methods require a large amount of training data or time. For example, alphastar needs 4,500 game hours equivalent to learning the strategy game StarCraft, and openai needs 10,000 years of training to master one-handed solve the rubik's cube

And the shortcomings of traditional deep learning models that cannot remember past events, and some models also need to learn concepts from human-labeled data, all of which make the training efficiency of deep learning models low

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