Depth neural network training method and device based on Hash coding layer

A deep neural network and hash coding technology, which is applied in the field of deep neural network training methods and devices, can solve the problems of difficult coding to achieve optimal performance and limited degree of nonlinearity

Active Publication Date: 2016-01-27
NETPOSA TECH
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  • Application Information

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Problems solved by technology

[0004] However, in the above encoding methods, the degree of nonlinearization is limited, and since the binary model to be solved directly is a non-deterministic p

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  • Depth neural network training method and device based on Hash coding layer
  • Depth neural network training method and device based on Hash coding layer
  • Depth neural network training method and device based on Hash coding layer

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Embodiment 1

[0058] see Figure 1A , the embodiment of the present invention provides a method for training a deep neural network based on a hash coding layer. The method specifically includes the following steps:

[0059] Step 101: Obtain a pre-trained neural network model, which includes a basic feature layer;

[0060] The above-mentioned pre-trained neural network model can be an image classifier model trained based on a large-scale data set, or an image classification model obtained by using a large-scale data set to train a custom network structure through a softmax optimization function.

[0061] In the above-mentioned pre-trained neural network model, multiple layers from the first layer to the layer capable of extracting better image features are used as the basic feature layers. Before the method provided by the embodiment of the present invention performs the training based on the hash coding layer on the above-mentioned pre-trained neural network model, the basic feature layer ...

Embodiment 2

[0097] see figure 2 An embodiment of the present invention provides a training device for a deep neural network based on a hash coding layer, and the device is used to implement the training method for a deep neural network based on a hash coding layer provided in Embodiment 1 above. Specifically, the device includes:

[0098] An acquisition module 201, configured to acquire a pre-trained neural network model, the neural network model including a basic feature layer;

[0099] The first insertion module 202 is used to insert the first fully connected layer used for the hash code linear map and the hash code layer used as the activation function after the basic feature layer in the neural network model; use the hash code layer as The activation function of the deep neural network model stimulates the deep neural network through the hash coding layer to improve the accuracy of the deep neural network model.

[0100] The second insertion module 203 is used to insert the second ...

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Abstract

The invention provides a depth neural network training method and device based on a Hash coding layer. The method comprises the steps: obtaining a pre-trained neural network model which comprises a basic feature layer; Inserting a first full connection layer for Hash coding linear mapping and the Hash coding layer serving as an activation function behind the basic feature layer in the neural network model; inserting a second full connection layer serving as an output layer behind the Hash coding layer; and carrying out the training of the weight of the first full connection layer and the second full connection layer. According to the invention, the method takes the Hash coding layer as the activation function of depth neural network training, carries out the training of the neural network through employing a method of random gradient descent, achieves overall optimization, achieves the direct training of a model outputting a binary code, and does not need to relax the optimization conditions.

Description

technical field [0001] The invention relates to the fields of computer technology and image processing, in particular to a training method and device for a deep neural network based on a hash coding layer. Background technique [0002] In the field of image retrieval and image storage, since a large amount of data storage and feature comparison are involved, it is necessary to perform binary coding on the image on the basis of maintaining the similarity of the original image feature space to reduce the time spent on image retrieval and image storage. The amount of data to be processed. [0003] At present, in related technologies, images are binarized by hash coding technology, and hash coding technology can be divided into three categories according to the binarization coding method: 1) linear mapping coding; 2) semi-nonlinear coding; 3 ) nonlinear coding. Among them, the linear mapping coding method can be divided into three categories according to the type of data requi...

Claims

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Application Information

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IPC IPC(8): G06N3/08
Inventor 任鹏远许健万定锐
Owner NETPOSA TECH
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