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TensorFlow2.0-Examples

(★ 1,699)

🙄 Difficult algorithm, Simple code.

파일 탐색기

최종 버전 다운로드 (.zip)

- FUNDING.yml
- Sponsor.md
- - activation-checkpoint.ipynb
  - basic_operations-checkpoint.ipynb
  - GradientTape-checkpoint.ipynb
  - variable-checkpoint.ipynb
- activation.ipynb
- activation.py
- basic_operations.ipynb
- basic_operations.py
- GradientTape.ipynb
- GradientTape.py
- helloworld.ipynb
- helloworld.py
- variable.ipynb
- variable.py
- - Linear_Regression-checkpoint.ipynb
  - Logistic_Regression-checkpoint.ipynb
  - Multilayer_Perceptron-checkpoint.ipynb
- - perceptron.png
- CNN.py
- Linear_Regression.ipynb
- Linear_Regression.py
- Logistic_Regression.ipynb
- Logistic_Regression.py
- Multilayer_Perceptron.ipynb
- Multilayer_Perceptron.py
- autoencoder.py
- fpn.py
- main.py
- README
- resnet.py
- vgg16.py
- - __init__.py
  - main.py
- - det1.npy
  - det2.npy
  - det3.npy
  - main.py
  - mtcnn.py
  - multiface.jpg
  - README.md
  - utils.py
- - demo.py
  - kmeans.py
  - README.md
  - rpn.py
  - test.py
  - train.py
  - utils.py
- - ssd.py
- - image_demo.py
  - README.md
  - test.py
  - train.py
  - video_demo.py
- - fcn8s.py
  - parser_voc.py
  - README.md
  - test.py
  - train.py
  - utils.py
- - README.md
  - train.py
  - Unet.py
- - dcgan-checkpoint.ipynb
  - Pix2Pix-checkpoint.ipynb
- - dis.png
  - gan1.png
  - gan2.png
  - gan_diagram.png
  - gen.png
  - xor.png
- dcgan.ipynb
- dcgan.py
- Pix2Pix.ipynb
- Pix2Pix.py
- multi_gpu_train.py
.gitignore
LICENSE
README.md

🚀 설치 가이드

1. 코드 내려받기

git clone https://github.com/YunYang1994/TensorFlow2.0-Examples

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd TensorFlow2.0-Examples

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. Python

쉬움 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

pip install -r 4-Object_Detection/YOLOV3/docs/requirements.txt

requirements.txt 등에 명시된 파이썬 라이브러리를 설치합니다.

jupyter notebook

브라우저에서 노트북(.ipynb) 파일들을 열람하고 실행할 수 있는 Jupyter 화면을 켭니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

// repository documentation

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<h2 align="center"><code>🎉TensorFlow2.0-Examples🎉!</code></h2>
<p align="center">"<i>Talk is cheap, show me the code.</i>" 
  ----- Linus Torvalds</p>
<p align="center">
  <a href="https://github.com/YunYang1994/TensorFlow2.0-Examples/tree/master">
    <img src="https://img.shields.io/badge/Branch-master-green.svg?longCache=true"
        alt="Branch">
  </a>
  <a href="https://github.com/YunYang1994/TensorFlow2.0-Examples/stargazers">
    <img src="https://img.shields.io/github/stars/YunYang1994/TensorFlow2.0-Examples.svg?label=Stars&style=social"
        alt="Stars">
  </a>
    <a href="https://github.com/YunYang1994/TensorFlow2.0-Examples/network/members">
    <img src="https://img.shields.io/github/forks/YunYang1994/TensorFlow2.0-Examples.svg?label=Forks&style=social"
        alt="Forks">
  </a>
  </a>
   <a href="https://github.com/sindresorhus/awesome">
   <img src="https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg"
        alt="Awesome">
  </a>
  </a>
   <a href="https://github.com/YunYang1994/TensorFlow2.0-Examples/blob/master/LICENSE">
   <img src="https://img.shields.io/github/license/mashape/apistatus.svg?maxAge=2592000"
        alt="Awesome">
</p>
<div align="center">
  <sub>Created by
  <a href="https://github.com/YunYang1994">YunYang1994</a>
</div>
<br>
    
This tutorial was designed for easily diving into TensorFlow2.0.  it includes both notebooks and source codes with explanation. **It will be continuously updated !** 🐍🐍🐍🐍🐍🐍

## Contents

#### 1 - Introduction
- **Hello World** ([notebook](https://nbviewer.jupyter.org/github/YunYang1994/tensorflow2.0-examples/blob/master/1-Introduction/helloworld.ipynb)) ([code](1-Introduction/helloworld.py)). Very simple example to learn how to print "hello world" using TensorFlow.
- **Variable** ([notebook](https://nbviewer.jupyter.org/github/YunYang1994/tensorflow2.0-examples/blob/master/1-Introduction/variable.ipynb)) ([code](1-Introduction/variable.py)). Learn to use variable in tensorflow.
- **Basical operation** ([notebook](https://nbviewer.jupyter.org/github/YunYang1994/tensorflow2.0-examples/blob/master/1-Introduction/basic_operations.ipynb)) ([code](1-Introduction/basic_operations.py)). A simple example that covers TensorFlow basic operations.
- **Activation** ([notebook](https://nbviewer.jupyter.org/github/YunYang1994/tensorflow2.0-examples/blob/master/1-Introduction/activation.ipynb)) ([code](1-Introduction/activation.py)). Start to know some activation functions in tensorflow.
- **GradientTape** ([notebook](https://nbviewer.jupyter.org/github/YunYang1994/tensorflow2.0-examples/blob/master/1-Introduction/GradientTape.ipynb)) ([code](1-Introduction/GradientTape.py)). Introduce a key technique for automatic differentiation

#### 2 - Basical Models
- **Linear Regression** ([notebook](https://nbviewer.jupyter.org/github/YunYang1994/tensorflow2.0-examples/blob/master/2-Basical_Models/Linear_Regression.ipynb)) ([code](2-Basical_Models/Linear_Regression.py)). Implement a Linear Regression with TensorFlow.
- **Logistic Regression** ([notebook](https://nbviewer.jupyter.org/github/YunYang1994/tensorflow2.0-examples/blob/master/2-Basical_Models/Logistic_Regression.ipynb)) ([code](2-Basical_Models/Logistic_Regression.py)). Implement a Logistic Regression with TensorFlow.
- **Multilayer Perceptron Layer** ([notebook](https://nbviewer.jupyter.org/github/YunYang1994/tensorflow2.0-examples/blob/master/2-Basical_Models/Multilayer_Perceptron.ipynb)) ([code](2-Basical_Models/Multilayer_Perceptron.py)). Implement Multi-Layer Perceptron Model with TensorFlow.
- **CNN** ([notebook](https://tensorflow.google.cn/tutorials/quickstart/advanced)) ([code](2-Basical_Models/CNN.py)). Implement CNN Model with TensorFlow.

#### 3 - Neural Network Architecture
- **VGG16** ([notebook](https://github.com/YunYang1994/TensorFlow2.0-Examples/tree/master/3-Neural_Network_Architecture)) ([code](https://github.com/YunYang1994/TensorFlow2.0-Examples/tree/master/3-Neural_Network_Architecture/vgg16.py))([paper](https://arxiv.org/pdf/1409.1556.pdf)). VGG16: Very Deep Convolutional Networks for Large-Scale Image Recognition. 
- **Resnet** ([notebook](https://github.com/YunYang1994/TensorFlow2.0-Examples/tree/master/3-Neural_Network_Architecture)) ([code](https://github.com/YunYang1994/TensorFlow2.0-Examples/tree/master/3-Neural_Network_Architecture/resnet.py))([paper](https://arxiv.org/pdf/1512.03385.pdf)). Resnet: Deep Residual Learning for Image Recognition. 🔥🔥🔥
- **AutoEncoder** ([notebook](https://github.com/YunYang1994/TensorFlow2.0-Examples/tree/master/3-Neural_Network_Architecture)) ([code](https://github.com/YunYang1994/TensorFlow2.0-Examples/tree/master/3-Neural_Network_Architecture/autoencoder.py))([paper](http://www.cs.toronto.edu/~hinton/science.pdf)). AutoEncoder: Reducing the Dimensionality of Data with Neural Networks.

<p align="center">
    <img width="65%" src="https://user-images.githubusercontent.com/30433053/68206851-b08d2580-0008-11ea-8b51-061e0cbead62.gif" style="max-width:65%;">
    </a>
</p>

- **FPN** ([notebook](https://github.com/YunYang1994/TensorFlow2.0-Examples/tree/master/3-Neural_Network_Architecture)) ([code](https://github.com/YunYang1994/TensorFlow2.0-Examples/tree/master/3-Neural_Network_Architecture/fpn.py))([paper](https://arxiv.org/abs/1612.03144)). FPN: Feature Pyramid Networks for Object Detection.

#### 4 - Object Detection
- **RPN** ([notebook](https://github.com/YunYang1994/TensorFlow2.0-Examples/tree/master/4-Object_Detection/RPN)) ([code](https://github.com/YunYang1994/TensorFlow2.0-Examples/tree/master/4-Object_Detection/RPN/rpn.py))([paper](https://arxiv.org/pdf/1703.06283.pdf)). RPN:  a Region Proposal Network 🔥🔥🔥🔥🔥

<p align="center">
    <img width="70%" src="https://user-images.githubusercontent.com/30433053/67913231-4e2ac400-fbc7-11e9-9995-94ed6f7181d4.png" style="max-width:70%;">
    </a>
</p>

- **MTCNN** ([notebook](https://github.com/YunYang1994/TensorFlow2.0-Examples/tree/master/4-Object_Detection/MTCNN)) ([code](https://github.com/YunYang1994/TensorFlow2.0-Examples/tree/master/4-Object_Detection/MTCNN/mtcnn.py))([paper](https://arxiv.org/abs/1604.02878)). MTCNN: Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks. *(Face detection and Alignment)* 🔥🔥

<p align="center">
    <img width="40%" src="https://user-images.githubusercontent.com/30433053/68547531-7e6f2f80-041d-11ea-8cfb-0c5a22af0921.jpg" style="max-width:40%;">
    </a>
</p>

- **YOLOv3** ([notebook](https://github.com/YunYang1994/TensorFlow2.0-Examples/tree/master/4-Object_Detection/YOLOV3)) ([code](https://github.com/YunYang1994/TensorFlow2.0-Examples/tree/master/4-Object_Detection/YOLOV3/core/yolov3.py))([paper](https://arxiv.org/pdf/1804.02767.pdf)). YOLOv3: An Incremental Improvement.🔥🔥🔥🔥

<p align="center">
    <img width="65%" src="https://user-images.githubusercontent.com/30433053/67914531-656bb080-fbcb-11e9-9775-302a25faf747.png" style="max-width:65%;">
    </a>
</p>

- **SSD** ([notebook](https://github.com/YunYang1994/TensorFlow2.0-Examples/tree/master/4-Object_Detection/SSD)) ([code](https://github.com/YunYang1994/TensorFlow2.0-Examples/tree/master/4-Object_Detection/SSD/ssd.py))([paper](http://arxiv.org/abs/1512.02325)). SSD: Single Shot MultiBox Detector.🔥🔥🔥🔥 【TO DO】

<p align="center">
    <img width="56%" src="https://user-images.githubusercontent.com/30433053/68290134-5f416c80-00c2-11ea-8cbc-d6010ced4efd.png" style="max-width:56%;">
    </a>
</p>

- **Faster R-CNN** ([notebook](https://github.com/YunYang1994/TensorFlow2.0-Examples/tree/master/4-Object_Detection/Faster-RCNN)) ([code](https://github.com/YunYang1994/TensorFlow2.0-Examples/tree/master/4-Object_Detection/Faster-RCNN/frcnn.py))([paper](http://arxiv.org/abs/1506.01497)). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.🔥🔥🔥🔥 【TO DO】

<p align="center">
    <img width="65%" src="https://user-images.githubusercontent.com/30433053/68546623-54187480-0413-11ea-9396-0a698c5a2580.png" style="max-width:65%;">
    </a>
</p>

#### 5 - Image Segmentation
- **FCN** ([notebook](5-Image_Segmentation/FCN)) ([code](5-Image_Segmentation/FCN/fcn8s.py))([paper](https://arxiv.org/abs/1411.4038)). FCN: Fully Convolutional Networks for Semantic Segmentation. 🔥🔥🔥🔥🔥

<p align="center">
    <img width="60%" src="https://user-images.githubusercontent.com/30433053/67917411-e62eaa80-fbd3-11e9-9fe1-95550cf559d7.png" style="max-width:60%;">
    </a>
</p>

- **Unet** ([notebook](5-Image_Segmentation/Unet)) ([code](5-Image_Segmentation/Unet/train.py))([paper](https://arxiv.org/abs/1505.04597)). U-Net: Convolutional Networks for Biomedical Image Segmentation. 🔥🔥

<p align="center">
    <img width="50%" src="https://user-images.githubusercontent.com/30433053/67922238-2ba7a380-fbe5-11e9-96a0-55c6827bd024.png" style="max-width:50%;">
    </a>
</p>

#### 6 - Generative Adversarial Networks
- **DCGAN** ([notebook](https://nbviewer.jupyter.org/github/YunYang1994/tensorflow2.0-examples/blob/master/6-Generative_Adversarial_Networks/dcgan.ipynb)) ([code](6-Generative_Adversarial_Networks/dcgan.py))([paper](https://arxiv.org/pdf/1511.06434.pdf)).  Deep Convolutional Generative Adversarial Network.
- **Pix2Pix** ([notebook](https://nbviewer.jupyter.org/github/YunYang1994/tensorflow2.0-examples/blob/master/6-Generative_Adversarial_Networks/Pix2Pix.ipynb)) ([code](6-Generative_Adversarial_Networks/Pix2Pix.py))([paper](https://arxiv.org/pdf/1611.07004.pdf)).  Image-to-Image Translation with Conditional Adversarial Networks.

#### 7 - Utils
- **Multiple GPU Training** ([notebook](https://yunyang1994.github.io/posts/TensorFlow-的多卡-GPU-训练机制/#more))([code](7-Utils/multi_gpu_train.py)). Use multiple GPU to train your model.