TensorFlow2.0-Examples

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TensorFlow2.0-Examples

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<h2 align="center"><code>🎉TensorFlow2.0-Examples🎉!</code></h2>
"Talk is cheap, show me the code." 
 ----- Linus Torvalds

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 <a href="https://github.com/YunYang1994">YunYang1994</a>
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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.

- **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 🔥🔥🔥🔥🔥

- **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)* 🔥🔥

- **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.🔥🔥🔥🔥

- **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】

- **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】

#### 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. 🔥🔥🔥🔥🔥

- **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. 🔥🔥

#### 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.

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