Deep neural network compression method based on reinforcement learning

Abstract

The invention discloses a deep neural network compression method based on reinforcement learning, and the method comprises the steps: S100, training a reinforcement learning agent based on a trained convolutional neural network, the reinforcement learning agent being used for recognizing the sparsity of a network, and employing a compression method to compress a model; and S200, training the compressed model again, optimizing parameters of the model, and obtaining a final compression model. According to the invention, a huge and complex deep neural network model is compressed. The compressed model can run on a platform with limited hardware resources, such as smart home equipment and the like. Compared with an original model, the compressed model has the advantages that the requirements for the storage space and the operand are greatly reduced. Meanwhile, the performance of the model is still kept at the original level, wide application of the image recognition technology in daily lifecan be achieved, and convenience and safety of life are improved.

Description

technical field [0001] The invention relates to the technical field of image recognition, in particular to a deep neural network compression method based on reinforcement learning. Background technique [0002] In the field of image recognition and face recognition, using deep learning technology and convolutional neural network can achieve very good recognition results, and its recognition accuracy is much higher than traditional image recognition methods. During the training process, the convolutional neural network receives a large number of training image samples, uses the convolutional layer to extract the features in the image layer by layer, and adjusts the parameters in the network through the direction propagation algorithm, so as to minimize the output error. Due to its extremely high accuracy, deep convolutional neural networks have gradually become the mainstream method in the field of image recognition. However, image recognition methods based on deep learning ...

AI Technical Summary

Problems solved by technology

[0003] The purpose of the present invention is to provide a deep neural network compression method based on reinforcement learning, which is used to solve the problem that the deep neural network model with huge volume but good performance cannot be directly run on smart home devices with limited hardware resources, and also solves the problem of Time-consuming and inflexible problems in the process of manually compressing the model

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