PythonPracticeBasedonKeras

It includes very practical application of python with deep learning framework Keras. It includes use some simple models like regression model to predict something like Boston house price. It also includes some more complex models like CNN and LSTM to solve more complicated problems in our daily life. It also includes some ways to save the created models including the important weights in the model so that the created model can be used at once on other similar problems.

Name		Name	Last commit message	Last commit date
Latest commit History 1 Commits
1. Pima Indians Diabetes.py		1. Pima Indians Diabetes.py
10. save model YAML.py		10. save model YAML.py
11. incremental model.py		11. incremental model.py
12. check point.py		12. check point.py
13. auto save model.py		13. auto save model.py
14. load model from checkpoint.py		14. load model from checkpoint.py
15. visulize the process.py		15. visulize the process.py
16. use dropout in input layer.py		16. use dropout in input layer.py
17. use dropout in hidden layer.py		17. use dropout in hidden layer.py
18. linear decay rate.py		18. linear decay rate.py
19. exponential decay rate.py		19. exponential decay rate.py
2. split data manually and evaluate.py		2. split data manually and evaluate.py
20. MNIST.py		20. MNIST.py
21. simple CNN.py		21. simple CNN.py
22. complex CNN.py		22. complex CNN.py
23.1. image data generator-original image.py		23.1. image data generator-original image.py
23.2. feature normalization.py		23.2. feature normalization.py
23.3. ZAC.py		23.3. ZAC.py
23.4. rotation moving cut of image.py		23.4. rotation moving cut of image.py
23.5. save the model.py		23.5. save the model.py
24. classify CIFAR-10.py		24. classify CIFAR-10.py
24.2. complex CNN.py		24.2. complex CNN.py
24.3. Improvement of 24.2.py		24.3. Improvement of 24.2.py
25. IMDB motion analysis(preceton).py		25. IMDB motion analysis(preceton).py
25.2. IMDB (CNN).py		25.2. IMDB (CNN).py
26.1. travel people(preceton).py		26.1. travel people(preceton).py
26.2. travel people (window).py		26.2. travel people (window).py
27. travel people(LSTM).py		27. travel people(LSTM).py
27.2. travel people(LSTM window).py		27.2. travel people(LSTM window).py
27.3. travel people(LSTM time_step).py		27.3. travel people(LSTM time_step).py
28. IMDB(LSTM&CNN).py		28. IMDB(LSTM&CNN).py
29. multiple variables problem.py		29. multiple variables problem.py
3. use kfold to evaluate the model.py		3. use kfold to evaluate the model.py
30.1. create LSTM for Alice.py		30.1. create LSTM for Alice.py
4. use scikit-learn.py		4. use scikit-learn.py
5. parameter selection.py		5. parameter selection.py
6. iris.py		6. iris.py
7. Boston House Price.py		7. Boston House Price.py
8. banking marketing.py		8. banking marketing.py
9. save model json.py		9. save model json.py
README.md		README.md

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