Model configuration method and device, electronic device and readable storage medium

Abstract

The invention discloses a model configuration method and device, an electronic device and a readable storage medium. The method comprises the steps of acquiring a first machine learning model, and acquiring one or more second machine learning models, wherein each second machine learning model is constructed according to corresponding equipment and / or platform; migrating the capability of the firstmachine learning model to each second machine learning model to obtain one or more trained second machine learning models; and configuring the equipment and / or the platform corresponding to each trained second machine learning model according to the trained second machine learning model, so that the equipment and / or the platform run the trained second machine learning model. According to the method, the mass data and the machine learning ability learned by a large machine learning model are embedded into various different devices by using a model migration technology, so that the performanceof a plurality of large machine learning models can be migrated to a small machine learning model, and the actual problem in life is solved.

Description

technical field [0001] The present application relates to the field of computer technology, and more specifically, to a model configuration method, device, electronic equipment, and readable storage medium. Background technique [0002] Machine learning techniques continue to evolve and already support many aspects of modern society, from web search, content filtering, automatic recommendation of commercial websites, automated games to object detection, image classification, speech recognition, machine translation, drug discovery, and genetics. The current state of the art in the field of machine learning are deep neural networks, which use computational models consisting of multiple processing layers that learn representations of data (often extremely large amounts of data) with multiple levels of abstraction, hence the term "Deep Learning", "Deep Networks", etc. [0003] A general consensus in current machine learning tasks is that under the conditions of sufficient sampl...

AI Technical Summary

Problems solved by technology

Although the larger and more complex the network model, the more accurate and reliable the recognition results are, but the existing machine learning methods cannot adapt to the application in terms of application promotion.

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