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# Deeplearning
#README
## Quickly Start
1. 将项目文件夹编辑成**日期+项目名**
2. 编辑好label名称，label名称命名变成英文或者数字
>例如：”PDMS“， ”1“ 等, , 如果你的每一个类，下面又多个子特征则可以建立一个文件夹，在创建神经网络类的时候将**isDir**参数改成True即可.
>详细如下图：
>2.1. 如果只有一类特征
>![image.png](https://qq-pic.oss-cn-nanjing.aliyuncs.com/img/image.png)
> ![image.png](https://qq-pic.oss-cn-nanjing.aliyuncs.com/img/2024-12-28-f7f922-image.png)
>2.2. 如果有多类特征
>![image.png](https://qq-pic.oss-cn-nanjing.aliyuncs.com/img/image.png)
>![image.png](https://qq-pic.oss-cn-nanjing.aliyuncs.com/img/2024-12-28-e6a2bf-image.png)
3. 将准备好的文件夹移动到**Static**文件夹中（没有就建立），如果没有 **Result** 建立一个**Result**文件夹用来存放结果
4. 读取数据：
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data=data, labels=label_names, test_size= 0.3
)
```
5. 创建神经网络类
```python
model = Qmlp(
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6. 训练并获取数据
```python
pca_2d, pca_3d = model.get_PCA()
model.fit(300)
cm = model.get_cm()
epoch_data = model.get_epoch_data()
save_to_xlsx(project_name=projet_name, file_name="pca_2d", data=pca_2d)
save_to_xlsx(project_name=projet_name, file_name="pca_3d", data=pca_3d)
save_to_xlsx(project_name=projet_name, file_name="cm", data=cm )
save_to_xlsx(project_name=projet_name, file_name="acc_and_loss", data=epoch_data)
```