Dense connection-based deep neural network structure improvement method and system

A technology of deep neural network and network structure, applied in the field of improvement of deep neural network structure based on dense connections, can solve problems such as slow learning and training process, gradient disappearance, and difficulty in convergence, so as to alleviate the problem of gradient disappearance and avoid excessive parameter quantity , The effect of improving training efficiency

Inactive Publication Date: 2019-09-20
朗坤智慧科技股份有限公司
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Problems solved by technology

[0003] However, with the increase in the number of deep neural network layers, some problems have also appeared. For example, the transmission of training signals is becoming more and more difficult, the gradient disappears and the gradient

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  • Dense connection-based deep neural network structure improvement method and system

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[0035] In order to better understand the technical content of the present invention, specific embodiments are given together with the attached drawings for description as follows.

[0036] Aspects of the invention are described in this disclosure with reference to the accompanying drawings, which show a number of illustrated embodiments. Embodiments of the present disclosure are not necessarily defined to include all aspects of the present invention. It should be appreciated that the various concepts and embodiments described above, as well as those described in more detail below, can be implemented in any of numerous ways, since the concepts and embodiments disclosed herein are not limited to any implementation. In addition, some aspects of the present disclosure may be used alone or in any suitable combination with other aspects of the present disclosure.

[0037] to combine figure 1 , the present invention refers to a method for improving a deep neural network structure ...

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Abstract

The invention discloses a deep neural network structure improvement method based on dense connection. The method comprises the steps of establishing a full-connection deep neural network, and determining the number of layers of the full-connection deep neural network, the number of neurons of each layer and parameters, dividing the full-connection deep neural network into a plurality of dense connection blocks with moderate sizes according to a sequence, realizing dense connection in each dense connection block, and combining the dense connection blocks into the deep dense neural network according to a full connection mode. According to the inventionmethod, the dense connection idea is introduced, the full-connection deep neural network structure is improved, the gradient problem can be effectively reduced, the feature utilization rate is improved, the training time is shortened, and the training accuracy is improved.

Description

technical field [0001] The invention relates to the technical field of deep learning algorithms, in particular to a densely connected deep neural network structure improvement method. Background technique [0002] With the substantial improvement of computer processing capabilities, the research and development of deep learning algorithm technology has developed rapidly. The layers of deep neural networks are getting deeper and deeper, and with the increasing amount of data, deep neural networks have shown great advantages compared with traditional machine learning algorithms. [0003] However, with the increase in the number of deep neural network layers, some problems have also appeared. For example, the transmission of training signals is becoming more and more difficult, the gradient disappears and the gradient explodes, and the learning and training process becomes slow and difficult to converge. At the same time, the traditional fully connected neural network structur...

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Application Information

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IPC IPC(8): G06N3/04
CPCG06N3/045
Inventor 武爱斌魏小庆
Owner 朗坤智慧科技股份有限公司
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