Method and device for optimizing neural network

Abstract

The embodiment of the invention discloses a method and a device for optimizing a neural network. The method comprises the steps of acquiring an initial neural network conforming to set accuracy conditions, and determining a weight parameter matrix between two adjacent layers of unit nodes in the initial neural network; processing the weight parameter matrix according to a preset deletion threshold, and determining unit nodes to be deleted in the initial neural network; and deleting the unit nodes so as to form an optimized target neural network. by using the method disclosed by the invention, compression can be performed on the neural network simply and efficiently, and optimization for the neural network is realized, so that the purposes of accelerating the recognition processing speed, shortening the recognition processing time and reducing space occupation for a memory, a running memory, a display memory and the like are achieved when face recognition is performed based on the optimized neural network.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of artificial neural networks, and in particular to a neural network optimization method and device. Background technique [0002] At present, face recognition is usually performed based on a trained neural network model (such as a deep convolutional neural network model). When using the neural network model to carry out face recognition, the existing problems are as follows: 1. The complexity of the image data processing calculation is high, which affects the operation time (such as processing the face image on an electronic device equipped with a Core i7 processor It often takes more than 1 second); 2. It needs to take up a lot of memory space or video memory space of the graphics card during the processing; 3. It also needs to take up a lot of storage space to store the entire neural network model. [0003] The existing neural network model optimization methods cannot completely sol...

AI Technical Summary

Problems solved by technology

When using the neural network model to carry out face recognition, the existing problems are as follows: 1. The complexity of image data processing and calculation is high, which affects the operation time (such as processing face images on electronic devices equipped with Core i7 processors It often takes more than 1 second); 2. It needs to take up a lot of memory space or graphics card memory space during the processing; 3. It also needs to take up a lot of storage space to store the entire neural network model

[0003] The existing neural network model optimization methods cannot completely solve the above-mentioned problems. For example, the optimization in the form of Huffman coding can ensure the processing and calculation accuracy of the optimized neural network model and effectively reduce the depth of the neural network. The storage space of the network model, but it cannot reduce the complexity of processing operations, shorten the running time, and at the same time, it cannot reduce the space occupied by memory or video memory during processing.

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