Method for constructing deep convolution neural network model

A neural network model and convolutional neural network technology, applied in biological neural network models, neural learning methods, neural architectures, etc., can solve the problem of difficulty in determining the depth of the network and the number of feature maps, weak correlation between data samples and network models, and Excessive reliance on human experience and other issues, to overcome excessive reliance on human experience, to achieve adaptive incremental learning, network training time and good recognition effect

Inactive Publication Date: 2017-07-07
SHANDONG UNIV OF TECH
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Problems solved by technology

[0005] But at present, the traditional convolutional neural network structure in the existing technology has shortcomings such as over-reliance on human experience, diffi

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  • Method for constructing deep convolution neural network model
  • Method for constructing deep convolution neural network model
  • Method for constructing deep convolution neural network model

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[0031] Figure 1~5 It is the best embodiment of the present invention. Figure 1~5 The present invention will be further explained.

[0032] Reference attached Figure 1~5 : A deep convolutional neural network model construction method, including the following steps,

[0033] 1) Initialize the convolutional neural network model. The initial network model only sets up one branch, which contains the input layer, convolutional layer, pooling layer and fully connected layer, and each layer contains only one feature map; at this time, The network depth of is set to 5 layers, two convolutional layers and two pooling layers are set, one fully connected layer is set, and the convolutional layer and the pooling layer are set alternately.

[0034] 2) Using the convergence speed of the convolutional neural network as the evaluation index, perform end-to-end global expansion learning for the network until the average error of the convolutional neural network system reaches the preset expected va...

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Abstract

The invention provides a method for constructing a deep convolution neural network model, belonging to the field of mode recognition and machine learning. The method comprises the steps of (1) initializing a convolution neural network model, (2) carrying out end-to-end global extension learning on a network until the average error of a convolution neural network system reaches a preset expected value, (3) after global extension, using a cross validation sample to evaluate network performance, and carrying out local extension learning on the network if a recognition rate does not reach the expected value, (4) adding a new incremental end-to-end extension learning branch, realizing the incremental expansion learning of a network structure, and finally realizing the model construction of the deep convolution neural network. According to the method, a neural element can be added according to needs according to a condition of participating in training samples, the extension expansion and incremental expansion of the network structure are realized, the relevance between a data sample and the network model is enhanced, and the network structure adaptive incremental learning is realized.

Description

technical field [0001] A method for constructing a deep convolutional neural network model belongs to the field of pattern recognition and machine learning. Background technique [0002] Convolutional neural network is a special deep neural network model. Its particularity is reflected in two aspects. On the one hand, the connection between its neurons is not fully connected. On the other hand, the connection between some neurons in the same layer The weights of the connections between are shared (ie the same). Its non-fully connected and weight-sharing network structure makes it more similar to biological neural networks, reducing the complexity of the network model and reducing the number of weights. The convolutional neural network is a multi-layer perceptron specially designed for recognizing two-dimensional images. It has good fault tolerance, parallel processing ability and self-learning ability, and can handle complex situations such as complex environmental informat...

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Application Information

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IPC IPC(8): G06N3/04G06N3/08G06N99/00
CPCG06N3/08G06N20/00G06N3/048
Inventor 邹国锋傅桂霞赵新宇林钉屹高明亮尹丽菊李海涛
Owner SHANDONG UNIV OF TECH
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