Nondestructive freshness discriminating of shrimp using visible/near-infrared hyperspectral imaging technique and deep learning algorithm

Published: 17 June 2017| Version 1 | DOI: 10.17632/7t72vbszh6.1
Contributor:
Xinjie Yu

Description

####Python codes of algorithms 1.SAEs-LR_train.py. #Train SAEs and LR by ramdon pixels. 2.SAEs-LR_classify.py. #Use trained SAEs-LR model for freshness classifiction (by mean spectra) 3.Classification_maps.py. #Use SAEs-LR model to create classification maps for hyperspectral images. ####data folder sample_rois, #All pixels in ROI of shrimps (only 1 ROIs were uploaded because of the uploading time issue) folder train_pixels, #The 102400 ramdon pixel spectra used to train the SAEs-LR model file Meanspectra.csv, #The mean spectra of samples and the original experimantal data in this paper ####logs LR_model #LR model struction file LR_model.weights.hdf5 #The weights of trained LR model SAEs_model #SAEs model struction file SAEs_model.weights.hdfs #The weights of trained SAEs model SAEs_model.train.log #The train error of SAEs model ####results data, figures, and classification images created by the algorithms

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