# Raman-Spectra-Deep-Learning

**Repository Path**: libertyme/Raman-Spectra-Deep-Learning

## Basic Information

- **Project Name**: Raman-Spectra-Deep-Learning
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-03-03
- **Last Updated**: 2025-03-03

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# PyTorch implementation of deep learning for Raman spectrum recognition

Deep learning has been widely applied for Raman spectroscopy. This repository contributes PyTorch implementation of vanilla CNN and LSTM for Raman spectrum recognition (Liu et al., 2017; Yu et al., 2021). On this basis, this repository adopts three novel deep learning models: (1) Transformer (2) graph neural networks (GNN) (3) contrastive learning, for Raman spectrum recognition.

## Requirements

The code has been tested running under Python 3.9.12, with the following packages and their dependencies installed:

```
numpy==1.16.5
pandas==1.4.2
pytorch==1.7.1
sklearn==0.21.3
```

## Usage

```bash
python main.py --c 2 --model LSTM
```

Parameter `model` can be one of these models: CNN, LSTM, Transformer, GCN, CLR.

GNN is implemented as graph convolutional networks (GCN) (Kipf et al., 2017) with spectral angle for graph construction. Contrastive learning model is implemented as SimCLR with maximum mean discrepancy (Chen et al., 2020; Zhang et al., 2022).

## Datasets

This repo validates the models on two datasets proposed by Yu et al. (2021). The region of the Raman spectra data is from 600 to 1800 cm-1. The dimension of the Raman spectra data is 1200. Following Yu et al. (2021), the Raman spectra of each sample is preprocessed by 0-1 normalization.

The binary classification dataset `bin.csv` consists of the Raman spectra data from two kinds of microbes, *Acinetobacter baumannii* (Label 0) and *Pseudomonas nitritireducens* (Label 1). 

The multi-class classification dataset `multi.csv` consists of the Raman spectra data from eight strains of *Urechis unicinctus*, named SX-1 to SX-8 (Label 0 to 7). 

## Results

Deep learning models can achieve 99% accuracy on binary classification dataset, and 95% accuracy on multi-class classification dataset. For comparison, this repo also implements SVM and random forest for this task, see `baseline.py`.

## Options

We adopt an argument parser by package  `argparse` in Python, and the options for running code are defined as follow:

```python
parser = argparse.ArgumentParser()
parser.add_argument('--use-cuda', default=False,
                    help='CUDA training.')
parser.add_argument('--seed', type=int, default=1, help='Random seed.')
parser.add_argument('--epochs', type=int, default=200,
                    help='Number of epochs to train.')
parser.add_argument('--lr', type=float, default=0.01,
                    help='Learning rate.')
parser.add_argument('--wd', type=float, default=1e-5,
                    help='Weight decay (L2 loss on parameters).')
parser.add_argument('--hidden', type=int, default=64,
                    help='Dimension of representations')
parser.add_argument('--c', type=int, default=2,
                    help='Num of classes')
parser.add_argument('--d', type=int, default=1200,
                    help='Num of spectra dimension')
parser.add_argument('--model', type=str, default='CLR',
                    help='Model')                    

args = parser.parse_args()
args.cuda = args.use_cuda and torch.cuda.is_available()
```

## References

Chen et al. A simple framework for contrastive learning of visual representations. ICML. 2020

Deng et al. Scale-adaptive deep model for bacterial raman spectra identification. IEEE JBHI. 2022

Gu et al. Conformal prediction based on raman spectra for the classification of chinese liquors. Appl. Spectrosc. 2019

Ho et al. Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning. Nat. Commun. 2019

Kipf et al. Semi-supervised classification with graph convolutional networks. ICLR. 2017

Liu et al. Deep convolutional neural networks for raman spectrum recognition: a unified solution. Analyst. 2017

Liu et al. Dynamic spectrum matching with one-shot learning. Chemometr. Intel. Lab. Sys. 2019

Yu et al. Analysis of raman spectra by using deep learning methods in the identification of marine pathogens. Anal. Chem. 2021

Zhang et al. Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning. NeurIPS. 2022

Zhong et al. Accurate prediction of salmon storage time using improved raman spectroscopy. J. Food Eng. 2021