Image recognition method based on optimized deep neural network model

Abstract

The invention relates to an image recognition method based on an optimized deep neural network model, and belongs to the technical field of image recognition. The method comprises the following steps:S1, selecting a data set of an image as a training sample, dividing the data set into a plurality of sample sets according to batch setting of a batch number, and inputting a current sample set intoa specific deep neural network model to obtain a loss function value set corresponding to the current sample set, wherein the specific deep neural network model is used as a selected deep neural network; s2, updating network parameters of the selected deep neural network by discarding or forcibly zeroing training samples corresponding to a part of sets with smaller numerical values in the loss function value set; s3, iterating S1 and S2 for multiple times to obtain an optimized deep neural network model; and S4, inputting a to-be-identified image into the optimized deep neural network model toobtain an identified image. According to the method, the problem that the training performance of the deep neural network model under large-batch image data sets is greatly reduced is solved.

Description

technical field [0001] The invention relates to the technical field of image recognition, in particular to an image recognition method based on an optimized deep neural network model. Background technique [0002] At present, when the deep learning of neural network is used for image recognition, it is of great practical application value to increase the parallel computing scale of the model. Identify issues with degrading training performance. And through further research, it is found that this performance decline is accompanied by an increase in model noise and a decrease in the average gradient of network parameters. In the existing public literature, a training method that sets the learning rate based on the gradient within the model layer is proposed, that is, LARS, which greatly improves the performance of model training when the batch size is large. size) value continues to increase, the training performance will inevitably decline. Contents of the invention [0...

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Problems solved by technology

[0002] At present, when the deep learning of neural network is used for image recognition, it is of great practical application value to increase the parallel computing scale of the model. Identify issues with degrading training performance

And through further research, it is found that this decline in performance is accompanied by an increase in model noise and a decrease in the average gradient of network parameters

In the existing public literature, a training method that sets the learning rate based on the gradient within the model layer is proposed, that is, LARS, which greatly improves the performance of model training when the batch size is large. size) value continues to increase, the training performance will inevitably decline

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