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26 lines
1.7 KiB
Markdown
26 lines
1.7 KiB
Markdown
# SCNet: A deep learning network framework for analyzing near-infrared spectroscopy using short-cut
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## Pre-processing
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Since the method we proposed is a regression model, the classification dataset weat kernel is not used in this work.
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The other three dataset (corn, marzipan, soil) were preprocessed manually with Matlab and saved in the sub dictionary of `./dataset` dir. The original dataset of these three dataset were stored in the `./dataset/`. And the data are shared with google drive with this [link](https://drive.google.com/drive/folders/1RFREskNcI2sDv6p7lvLhxFRLUgVTwho6?usp=sharing)
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The mango dataset is not in Matlab .m file format, so we save them with the `process.py`.
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Meanwhile, we drop the useless part and only save the data between 684 and 900 nm.
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> The data set used in this study comprises a total of 11,691 NIR spectra (684–990 nm in 3 nm sampling with a total 103 variables) and DM measurements performed on 4675 mango fruit across 4 harvest seasons 2015, 2016, 2017 and 2018 [24].
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The detailed preprocessing progress can be found in [./preprocess.ipynb](./01_preprocess.ipynb)
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## Network Training
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In order to show our network can prevent degration problem, we hold the experiment which contains the training loss curve of four models. The detailed information can be found in [model_training.ipynb](./02_model_training.ipynb).
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The training results were saved on the google drive, here is the [link](https://drive.google.com/drive/folders/1-p1SPg-6lt7i6NkgzUOf5GDhh0cDePsr?usp=sharing])
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## Network evaluation
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After training our model on training set, we evaluate the models on testing dataset that spared before. The evaluation is done with [model_evaluation.ipynb](03_model_evaluating.ipynb).
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