Deep learning model training method, device and equipment and storage medium
A deep learning and training method technology, applied in the field of deep learning, can solve the problems of large number of deep learning model samples, inability to provide deep learning models, poor training effect, etc., to improve anti-noise and anti-displacement capabilities, and improve training speed , Improve the effect of training effect
- Summary
- Abstract
- Description
- Claims
- Application Information
AI Technical Summary
Problems solved by technology
Method used
Image
Examples
Embodiment 1
[0025] figure 1 The implementation process of the deep learning model training method provided by Embodiment 1 of the present invention is shown. For the convenience of description, only the parts related to the embodiment of the present invention are shown, and the details are as follows:
[0026] In step S101, a deep learning model is constructed according to the received training image set, wherein the deep learning model includes a hidden layer and a fully connected layer, and the hidden layer consists of several feature extraction layers and a lower layer corresponding to the feature extraction layer The feature extraction layer is composed of several feature extraction surfaces, and the downsampling layer is composed of several downsampling surfaces.
[0027] Embodiments of the present invention are applicable to computing devices, such as personal computers, servers, and the like. In the embodiment of the present invention, according to the complexity of the training i...
Embodiment 2
[0047] image 3 The structure of the training device for the deep learning model provided by Embodiment 2 of the present invention is shown. For the convenience of description, only the parts related to the embodiment of the present invention are shown, including:
[0048] The model construction unit 31 is used to construct a deep learning model according to the training image set received, wherein the deep learning model includes a hidden layer and a fully connected layer, and the hidden layer is composed of several feature extraction layers and corresponding to the feature extraction layer. The downsampling layer is composed of the feature extraction layer consisting of several feature extraction surfaces, and the downsampling layer is composed of several downsampling surfaces.
[0049] Embodiments of the present invention are applicable to computing devices, such as personal computers, servers, and the like. In the embodiment of the present invention, according to the comp...
Embodiment 3
[0077] Figure 5 The structure of the computing device provided by the third embodiment of the present invention is shown, and for the convenience of description, only the parts related to the embodiment of the present invention are shown.
[0078] The computing device 5 of the embodiment of the present invention includes a processor 50 , a memory 51 and a computer program 52 stored in the memory 51 and operable on the processor 50 . When the processor 50 executes the computer program 52, the steps in the embodiment of the training method of the above-mentioned deep learning model are realized, for example figure 1 Steps S101 to S104 are shown. Alternatively, when the processor 50 executes the computer program 52, the functions of the units in the above-mentioned device embodiments are realized, for example image 3 Function of units 31 to 34 shown.
[0079] In the embodiment of the present invention, according to the received training image set, the preset feature extracti...
PUM
Abstract
Description
Claims
Application Information
- R&D Engineer
- R&D Manager
- IP Professional
- Industry Leading Data Capabilities
- Powerful AI technology
- Patent DNA Extraction
Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.
© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com