Data equalization method based on deep learning multi-weight loss function

Abstract

The invention relates to a data equalization method based on a deep learning multi-weight loss function, and the method comprises the steps: firstly obtaining a target image data set in a training process employing a deep learning model, determining the class number C of data samples and the size Ni of each class of samples according to the target data set, determining hyper-parameters [alpha] and [gamma] and a weighting coefficient Ci of the importance of each class of samples, and determining a multi-weight loss function MWLfocal (z, y), carrying out continuous iterative training by using the neural network model, carrying out error calculation by using the multi-weight loss function in the training process, and continuously updating weight parameters of the model by using a back propagation algorithm until network convergence reaches an expected target, thereby finally completing training. By means of the loss function, the problems of sample number imbalance and classification difficulty imbalance of different data classes can be solved at the same time, the detection accuracy of key classes can be further improved, the method can be applied to a data set with the data imbalance problem, and therefore the influence of the class imbalance problem is effectively relieved.

Description

technical field [0001] The invention relates to the fields of artificial intelligence and deep learning, in particular to a data equalization method based on deep learning multi-weight loss functions. Background technique [0002] With the rapid development of artificial intelligence, deep learning has achieved very remarkable results in many aspects, such as data mining, natural language processing, multimedia learning, recommendation and personalization technology, medical image processing, etc. Deep learning enables machines to imitate human activities such as audio-visual and thinking through a large amount of data, mines the characteristics of data, and solves many complex problems. [0003] Obtaining a large amount of data is a very time-consuming and labor-intensive task, and the data in the actual environment is often unevenly distributed. In terms of sample size, the difference between different types of data samples may be too large. For example, in medical imaging...

AI Technical Summary

Problems solved by technology

[0005] In order to overcome the deficiencies of the prior art, the present invention proposes a data equalization method based on deep learning multi-weight loss function. Using this loss function can not only deal with the problem of unbalanced number of samples and unbal

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