Analysis of Layer Efficiency and Layer Reduction on Pre-trained CNN Models
碩士 === 國立臺灣科技大學 === 電機工程系 === 106 === Deep learning still encounters several issues like overfitting and oversize due to the use of a large number of layers. The huge size greatly constrains performance and portability of deep learning models in limited environments like embedded and IoT devices. In...
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| Format: | Others |
| Language: | en_US |
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2018
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| Online Access: | http://ndltd.ncl.edu.tw/handle/2cvk37 |
| Summary: | 碩士 === 國立臺灣科技大學 === 電機工程系 === 106 === Deep learning still encounters several issues like overfitting and oversize due to the use of a large number of layers. The huge size greatly constrains performance and portability of deep learning models in limited environments like embedded and IoT devices. In this study, we reported our analysis of activation and gradient output and weight in each layer of the pre-trained models of VGG-16 and custom AlexNet to measure the efficiency of its layers. The efficiencies are estimated by using our measurements and compared with the manual layer reduction to validate the most relevant method. The method for multiple layer reductions is used for validation. With this found approach, the time of one-layer reduction and re-training processes on both models can save up to 9 folds and 5 folds respectively without significant accuracy reduction.
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