Keras-Applications

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Keras-Applications

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Keras 응용(CNN, RNN, GAN, DNN, ETC...) 사용법 예시

Keras-Applications 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# Keras-Applications

## Contents

1. [CNN_MNIST](https://github.com/gyunggyung/Keras-Applications/blob/master/01_CNN_MNIST.ipynb) : CNN을 이용한 간단한 NMIST 진행

![](https://raw.githubusercontent.com/gyunggyung/Keras-Applications/master/images/01.PNG)

2. [CNN convnets with small datasets](https://github.com/gyunggyung/Keras-Applications/blob/master/02_CNN_convnets_with_small_datasets.ipynb) : 작은 Datasets의 데이터를 데이터 증식을 통해서 80% 이상의 높은 성능의 분류기 제작

![](https://raw.githubusercontent.com/gyunggyung/Keras-Applications/master/images/02.PNG)

3. [CNN use pretrained VGG16](https://github.com/gyunggyung/Keras-Applications/blob/master/03_CNN_use_pretrained_VGG16.ipynb) : 이미 학습된 VGG16과 위에 데이터 증식을 응용해서 작은 Datasets에서 90%이상의 높은 성능을 갖는 분류기 제작

![](https://raw.githubusercontent.com/gyunggyung/Keras-Applications/master/images/03.PNG)

4. [CNN visualizing what convnets learn](https://github.com/gyunggyung/Keras-Applications/blob/master/04_CNN_visualizing_what_convnets_learn.ipynb) : convnets learn과정은 시각화와 CAM(Class Activation Map) 시각화를 통해 CNN 과정을 시각적으로 이해

![](https://raw.githubusercontent.com/gyunggyung/Keras-Applications/master/images/04.PNG)

5. [RNN word embeddings](https://github.com/gyunggyung/Keras-Applications/blob/master/05_RNN_word_embeddings.ipynb) : one hot encoding제작, GloVe로 word embeddings을 만들어서 IMDB 영화의 긍부정 학습

![](https://raw.githubusercontent.com/gyunggyung/Keras-Applications/master/images/05.PNG)

6. [RNN understanding rnn lstm](https://github.com/gyunggyung/Keras-Applications/blob/master/06_RNN_understanding_rnn_lstm.ipynb) : RNN과 LSTM를 이용하여 IMDB 영화의 긍부정 학습을 하며 RNN과 LSTM의 사용방법 이해

![](https://raw.githubusercontent.com/gyunggyung/Keras-Applications/master/images/06.PNG)

7. [RNN advanced usage of rnn](https://github.com/gyunggyung/Keras-Applications/blob/master/07_RNN_advanced_usage_of_rnn.ipynb) : 기온 예측 문제를 통해 RNN의 다양한 사용법 이해 (recurrent dropout, stacking recurrent layer, bidirectional recurrent layer)

![](https://raw.githubusercontent.com/gyunggyung/Keras-Applications/master/images/07.PNG)

8. [RNN processing with convnets](https://github.com/gyunggyung/Keras-Applications/blob/master/08_RNN_processing_with_convnets.ipynb) : 1D convnets을 이용하여 특정 자연어 처리 같은 일부 문제에서 RNN을 대신하는 빠른 모델 제작

![](https://raw.githubusercontent.com/gyunggyung/Keras-Applications/master/images/08.PNG)

> Image : https://arxiv.org/abs/1610.03017

9. [Advanced Keras Functional API](https://github.com/gyunggyung/Keras-Applications/blob/master/09_Advanced_Keras_Functional_API.ipynb) : 함수형 API를 통해서 다중 입력, 다중 출력 등 다양한 모델 제작

![](https://raw.githubusercontent.com/gyunggyung/Keras-Applications/master/images/09.PNG)

10. [Advanced Keras model checking and monitoring](https://github.com/gyunggyung/Keras-Applications/blob/master/10_Advanced_Keras_model_checking_and_monitoring.ipynb) : 콜백을 사용하여 모델의 훈련 과정을 제어하고 텐서보드를 이용하여 모니터링

![](https://raw.githubusercontent.com/gyunggyung/Keras-Applications/master/images/10.PNG)

11. [Advanced Keras performance improvements](https://github.com/gyunggyung/Keras-Applications/blob/master/11_Advanced_Keras_performance_improvements.ipynb) : 정규화, 깊이별 분리 합성곱, 하이퍼파라미터 최적화, 모델 앙상블 등 모델의 성능을 향상시키는 방법 학습

12. [Generation text with lstm](https://github.com/gyunggyung/Keras-Applications/blob/master/12_Generation_text_with_lstm.ipynb) : 언어 모델(LSTM과 샘플링)을 이용해서 니체의 글을 재생성

![](https://raw.githubusercontent.com/gyunggyung/Keras-Applications/master/images/12.gif)

13. [Generation deep dream](https://github.com/gyunggyung/Keras-Applications/blob/master/13_Generation_deep_dream.ipynb) : 컨브넷을 거꾸로 실행하여 입력 이미지를 새롭게 생성하는 딥드림 제작

![](https://raw.githubusercontent.com/gyunggyung/Keras-Applications/master/images/13.PNG)

14. [Generation neural style transfer](https://github.com/gyunggyung/Keras-Applications/blob/master/14_Generation_neural_style_transfer.ipynb) : 유명한 명화의 스타일과 자신이 원하는 사진의 컨텐츠 합성

![](https://raw.githubusercontent.com/gyunggyung/Keras-Applications/master/images/14.PNG)

15. [Generation VAE](https://github.com/gyunggyung/Keras-Applications/blob/master/15_Generation_VAE.ipynb) : VAE를 이용해서 간단한 MNIST 이미지 생성

![](https://raw.githubusercontent.com/gyunggyung/Keras-Applications/master/images/15.PNG)

16. [Generation GAM](https://github.com/gyunggyung/Keras-Applications/blob/master/16_Generation_GAN.ipynb) : DCGAN을 이용해서 간단한 개구리 이미지 생성

![](https://raw.githubusercontent.com/gyunggyung/Keras-Applications/master/images/16.PNG)

## Sequence Models

- [Dinosaurus land](https://github.com/gyunggyung/Sequence-Models-coursera/blob/master/Week%201/Dinosaur%20Island%20--%20Character-level%20language%20model/Dinosaurus%2BIsland%2B--%2BCharacter%2Blevel%2Blanguage%2Bmodel%2Bfinal%2B-%2Bv3.ipynb)

[![](https://github.com/gyunggyung/Keras-Applications/raw/015d452312af24815ead6770770e9eacad7f2395/images/s1.png)](https://github.com/gyunggyung/Keras-Applications/blob/015d452312af24815ead6770770e9eacad7f2395/images/s1.png)

- [Improvise a Jazz Solo](https://github.com/gyunggyung/Sequence-Models-coursera/blob/master/Week%201/Jazz%20improvisation%20with%20LSTM/Improvise%2Ba%2BJazz%2BSolo%2Bwith%2Ban%2BLSTM%2BNetwork%2B-%2Bv3.ipynb)

[![](https://github.com/gyunggyung/Keras-Applications/raw/015d452312af24815ead6770770e9eacad7f2395/images/s2.png)](https://github.com/gyunggyung/Keras-Applications/blob/015d452312af24815ead6770770e9eacad7f2395/images/s2.png)

- [Emojify](https://github.com/gyunggyung/Sequence-Models-coursera/blob/master/Week%202/Emojify/Emojify%2B-%2Bv2.ipynb)

[![](https://github.com/gyunggyung/Keras-Applications/raw/015d452312af24815ead6770770e9eacad7f2395/images/s3.png)](https://github.com/gyunggyung/Keras-Applications/blob/015d452312af24815ead6770770e9eacad7f2395/images/s3.png)

- [Neural Machine Translation](https://github.com/gyunggyung/Sequence-Models-coursera/blob/master/Week%203/Machine%20Translation/Neural%2Bmachine%2Btranslation%2Bwith%2Battention%2B-%2Bv4.ipynb)

[![](https://github.com/gyunggyung/Keras-Applications/raw/015d452312af24815ead6770770e9eacad7f2395/images/s4.png)](https://github.com/gyunggyung/Keras-Applications/blob/015d452312af24815ead6770770e9eacad7f2395/images/s4.png)

- [Trigger Word Detection](https://github.com/gyunggyung/Sequence-Models-coursera/blob/master/Week%203/Trigger%20word%20detection/Trigger%2Bword%2Bdetection%2B-%2Bv1.ipynb)

[![](https://github.com/gyunggyung/Keras-Applications/raw/015d452312af24815ead6770770e9eacad7f2395/images/s5.png)](https://github.com/gyunggyung/Keras-Applications/blob/015d452312af24815ead6770770e9eacad7f2395/images/s5.png)

## Reference

> [Keras examples directory](https://github.com/keras-team/keras/tree/master/examples)  
> [Deep Learning with Python Code](https://github.com/fchollet/deep-learning-with-python-notebooks)  
> [케라스 창시자에게 배우는 딥러닝](https://github.com/rickiepark/deep-learning-with-python-notebooks)  
> [Coursera Sequence Models](https://www.coursera.org/learn/nlp-sequence-models):[Code](https://github.com/gyunggyung/Sequence-Models-coursera)

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