Supplementary data and algorithms associated with the article of deep learning for predicting TVC in peeled shrimp

Published: 27 May 2017| Version 1 | DOI: 10.17632/n83xjh2bxm.1
Contributors:
Xinjie Yu,
Jianping Wang,
Huanda Lu

Description

Python codes: 1.SAEs.py #this file is used to train the SAEs model 2.SAEs-PLSR.py #use this file to train PLSR model based on deep spectra features, and evaluate the model. 3.SPA-PLSR.py #use this file to train PLSR model based on characteristic wavelengths selected by SPA, and evaluate the model. 4.F-PLSR.py #use this file to train PLSR model based on full spectra, and evaluate the model. Data: 1.ramdonpixel_1.pkl #60060 spectra for training SAEs model 2.ramdonpixel_2.pkl #60060 spectra for validating SAEs model 3.Fullspectra.csv #200 samples with 230 bands spectra and reference TVC values 4.SPAspectra.csv #200 samples with 18 characteristic wavelengths and reference TVC values Logs: saved model files Results: experimental results files

Files

Institutions

Zhejiang University Ningbo Institute of Technology

Categories

Algorithms

License