Neural network model determination method and device

Abstract

The embodiment of the invention provides a neural network model determination method and device, and the method comprises the steps of obtaining a task configuration parameter of a target task, a resource configuration parameter of a training resource, a model design configuration parameter, and a model compression configuration parameter; generating an initial neural network model for the targettask based on the task configuration parameter, the resource configuration parameter and the model design configuration parameter; and according to the model compression configuration parameters, selecting a corresponding preset compression mode, and performing compression processing on the initial neural network model to obtain a to-be-deployed neural network model. Through the scheme, the development efficiency in the neural network model determination process can be improved.

Description

technical field [0001] The present application relates to the technical field of machine learning, in particular to a method and device for determining a neural network model. Background technique [0002] As an emerging field in machine learning research, neural network parses data by imitating the mechanism of the human brain. It is an intelligent model that analyzes and learns by establishing and simulating the human brain. Neural networks have achieved great success in the fields of computer vision, speech recognition, robot control, etc. Through the layer-by-layer operation of multiple network layers, the feature information of the target can be extracted, so as to realize functions such as intelligent tracking and intelligent retrieval. Therefore, the neural network The network has gradually become the cornerstone of modern artificial intelligence. [0003] However, the process of determining the neural network model is very complicated. The process from the initial d...

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Problems solved by technology

[0003] However, the process of determining the neural network model is very complicated. The process from the initial design of the neural network model to the final determination of the neural network model that can be deployed on the device side takes a long time, and usually requires manual intervention. The model performs a lot of work such as model verification and model debugging, which leads to low development efficiency in the process of determining the neural network model

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