Image classification method capable of effectively preventing convolutional neural network from being overfit

Abstract

The invention relates to an image classification method capable of effectively preventing a convolutional neural network from being overfit. The image classification method comprises the following steps: obtaining an image training set and an image test set; training a convolutional neural network model; and carrying out image classification to the image test set by adopting the trained convolutional neural network model. The step of training the convolutional neural network model comprises the following steps: carrying out pretreatment and sample amplification to image data in the image training set to form a training sample; carrying out forward propagation to the training sample to extract image features; calculating the classification probability of each sample in a Softmax classifier; according to the probability yi, calculating to obtain a training error; successively carrying out forward counterpropagation from the last layer of the convolutional neural network by the training error; and meanwhile, revising a network weight matrix W by SGD (Stochastic Gradient Descent). Compared with the prior art, the invention has the advantages of being high in classification precision, high in rate of convergence and high in calculation efficiency.

Description

technical field [0001] The invention relates to the field of image processing, in particular to an image classification method for effectively preventing over-fitting of a convolutional neural network. Background technique [0002] With the wide application of multimedia technology and computer network, a large amount of image data appears on the network. In order to effectively manage these image files and provide users with better experience services, it is becoming more and more important to automatically identify the contents of these images. [0003] With the continuous improvement and development of random machine learning methods, more and more attention has been paid to deep learning algorithms. Among them, convolutional neural network is an important algorithm in deep learning, and it has become a research hotspot in the fields of speech analysis and image recognition. The convolutional neural network breaks the way of full connection of neurons between layers in t...

AI Technical Summary

Problems solved by technology

However, this method will greatly increase the computational complexity, and the traditional CPU operation speed can no longer meet such computational complexity.

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