Method for optimizing deep convolutional neural network for image classification

Abstract

The invention relates to a method for optimizing a deep convolutional neural network for image classification, which comprises the steps of first, building an image classification convolutional neural network; second, training the image classification convolutional neural network; third, testing the image classification convolutional neural network. The process of testing the image classification convolutional neural network comprises the steps that a preprocessed test data set is fed into a trained network model, and an Accuracy layer of the network outputs an accuracy value according to a probability value outputted by a Softmax layer and a label value of an input layer, wherein the accuracy value is the probability that a test image is correctly classified. According to operations performed in the steps, optimization for the deep convolutional neural network for image classification can be realized. The method provided by the invention for optimizing the deep convolutional neural network for image classification can effectively reduce the semantic gap and is high in classification accuracy.

Description

technical field [0001] The invention relates to the fields of computer vision and deep learning, in particular to an optimization method for deep convolutional neural networks for image classification, which belongs to the field of computer vision based on deep learning. Background technique [0002] The deep learning technology represented by the convolutional neural network has made major breakthroughs in many aspects in recent years, especially in the field of computer vision, such as image classification, target retrieval, etc., and has achieved good results. [0003] Image classification refers to the use of computer feature expression to simulate human understanding of images, and automatically divide images into different semantic spaces according to human understanding. In the image classification task, in order to obtain higher classification accuracy, it is necessary to extract feature information of different levels from the image. At present, there is still a hu...

AI Technical Summary

Problems solved by technology

[0004] In order to solve the problems of semantic gap and low classification accuracy existing in the prior art, the present invention provides an optimization method for image classification-oriented deep convolutional neural network that effectively narrows the semantic gap and has high classification accuracy

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