## 3 Results
### Raman Spectroscopy Enhancement Module Training Results  
#### 3.2.1 Selection of Generative Adversarial Networks  
The study described in this article selected the improved WGAN as the data enhancement network. In order to verify whether our choice would be appropriate, we selected several generative adversarial networks for comparative experiments. In this experiment, the original GAN, Conditional Generative Adversarial Network (CGAN), Deep Convolutional Generative Adversarial Network (DCGAN), Wasserstein Generative Adversarial Network (WGAN), and Progressive Growth Generative Adversarial Network (PGGAN) were selected as training models and unified, using the same parameters for training. In order to compare the training effects of several networks more scientifically, we introduced several indicators to evaluate the generated data.  
* Frechet Inception Distance (FID) [44]. FID is a classic evaluation index in generative adversarial networks which is used to evaluate the distance between real data and generated data feature vectors. The smaller the value of the FID, the more similar the generated data is to the real data.
* Normalized Relative Discriminative Score (NRDS) [45]. The larger the value of NRDS, the better the data generated by this GAN.
* Pearson Correlation Coefficient (PCC). The Pearson correlation coefficient is a method commonly used in statistics to measure the correlation between two sets of data. Although it is not used as an evaluation indicator in traditional generative adversarial networks, in this experimental model, due to the particularity of the data, it can be used as an indicator.  
In addition, we also use the duration of network training as an evaluation metric. We trained several selected GAN networks using the same parameters, and after 500 rounds of model training, calculated the relevant indicator values, as shown in Table 2. It can be seen that the PGGAN and WGAN are generally superior to other generative adversarial networks, but the training time of PGGAN network is longer than that of WGAN and the quality of generated data is not significantly improved, compared to WGAN. Therefore, in this study of the amplification of Raman spectroscopy data associated with shrimp pathogenic bacteria, WGAN is the best choice.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Raman Shift (cm\({}^{-1}\))** & **Component** & **Assignment** \\ \hline
1300 & Lipids & CH\({}_{2}\) twisting [39] \\
1440–1460 & Lipids & CH\({}_{2}\) deformation vibration [40] \\
1542 & Tyrotophan & Indole ring stretching [41] \\
1660 & Proteins & Amide I [42] \\
1750 & Lipids & C=O stretching [43] \\
2095 & Thiocyanate & C-N stretch [43] \\
2343 & CO\({}_{2}\)\({}^{-}\) hydrates & Asymmetric stretching [43] \\
2715 & Lipids & C-H stretches [43] \\
2800–3000 & Lipids, Fatty acids & CH, CH\({}_{2}\), and CH\({}_{3}\) symmetric \\  & & and antisymmetric stretching [43] \\
3300–3400 & Proteins & N-H vibration [43] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Major Raman bands found in spectra, and their tentative vibrational assignments.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **FID** & **NRDS** & **PCC** & **Time (min)** \\ \hline GAN & 5.139 & 0.072 & 0.785 & \(30\pm 3\) \\ CGAN & 2.368 & 0.086 & 0.874 & \(32\pm 2\) \\ DCGAN & 1.214 & 0.295 & 0.879 & \(45\pm 5\) \\ WGAN & 0.449 & 0.501 & 0.907 & \(45\pm 5\) \\ PGGAN & 0.318 & 0.548 & 0.918 & \(78\pm 3\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Training results of different GANs.