包含网络

推荐使用:

Deep Belief Network (DBN)

Stacked Autoencoder (sAE)

Stacked Sparse Autoencoder (sSAE)

Stacked Denoising Autoencoders (sDAE)
尝试更好的模型：

Convolutional Neural Network (CNN)

Recurrent Neural Network (RNN)

Long Short Term Memory (LSTM)
所依赖包
```
pip install tensorflow (version: 1.X)
pip install keras
pip install librosa (用于语音分类，选装)
pip install --upgrade --user numpy pandas h5py (升级包)
```
用于任务
use_for = 'classification' 用于分类任务

use_for = 'prediction' 用于预测任务

版本信息

Pytorch版本：

User：

用户可以通过model.py文件控制一些功能的开关：

self.show_pic => show curve in ‘Console’?

self.tbd => open/close tensorboard

self.save_model => save/ not save model

self.plot_para => plot W image or not

self.save_weight => save W matrix or not

self.do_tSNE => do t-SNE or not

Version 2018.11.7:

New 新增了两个数据集，一个用于分类，一个用于预测

New 新增t-SNE低维可视化

Chg 修正部分 use_for = 'prediction' 时的Bug

Version 2018.6.1:

New 新增了绘制训练曲线图，预测标签分布图，权值图的功能

Chg 重写了SAE，现在可以放心使用了

Chg 代码的整体运行函数run_sess放到了base_func.py

Chg 回归是可以实现的，需要设置 use_for = 'prediction'

测试结果

用于minst数据集分类，运行得到正确率可达98.78%

用于Urban Sound Classification语音分类，正确率达73.37%

(这个跑完console不会显示结果，因为是网上的比赛数据集，需上传才能得到正确率)

用于Big Mart Sales III预测，RMSE为1152.04

(这个也是网上的数据集，也没有test_Y)

跑的结果并不是太高，有更好的方法请赐教

语音分类未尝试语谱法，欢迎做过的和我交流

数据地址

USC,
BMS III

参考资料

Tensorflow基本函数,
RBM原理,
Hinton源码,
sDAE原论文,
sSAE分析TE过程,
RNN原理,
LSTM,
Tensorboard

My blog

ResearchGate,
知乎,
CSDN

QQ群：640571839

Paper

希望大家多支持支持我们的工作，欢迎交流探讨~

[1] Z. Pan, H. Chen, Y. Wang, B. Huang, and W. Gui, “A new perspective on ae-and vae-based process monitoring,” TechRxiv, Apr. 2022, doi.10.36227/techrxiv.19617534.

[2] Z. Pan, Y. Wang, k. Wang, G. Ran, H. Chen, and W. Gui, “Layer-Wise Contribution-Filtered Propagation for Deep Learning-Based Fault Isolation,” Int. J. Robust Nonlinear Control, Jul. 2022, doi.10.1002/rnc.6328

[3] Z. Pan, Y. Wang, K. Wang, H. Chen, C. Yang, and W. Gui, “Imputation of Missing Values in Time Series Using an Adaptive-Learned Median-Filled Deep Autoencoder,” IEEE Trans. Cybern., 2022, doi.10.1109/TCYB.2022.3167995

[4] Y. Wang, Z. Pan, X. Yuan, C. Yang, and W. Gui, “A novel deep learning based fault diagnosis approach for chemical process with extended deep belief network,” ISA Trans., vol. 96, pp. 457–467, 2020.

[5] Z. Pan, Y. Wang, X. Yuan, C. Yang, and W. Gui, “A classification-driven neuron-grouped sae for feature representation and its application to fault
classification in chemical processes ,” Knowl.-Based Syst., vol. 230, p. 107350, 2021.

[6] H. Chen, B. Jiang, S. X. Ding, and B. Huang, “Data-driven fault diagnosis for traction systems in high-speed trains: A survey, challenges, and perspectives,” IEEE Trans. Intell. Transp. Syst., 2020, doi.10.1109/TITS.2020.3029946

[7] H. Chen and B. Jiang, “A review of fault detection and diagnosis for the traction system in high-speed trains,” IEEE Trans. Intell. Transp. Syst., vol. 21, no. 2, pp. 450–465, Feb. 2020.