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## Basics
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- Multi-layer perceptron (MNIST) - Classification task, see [tutorial\_mnist\_simple.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_mnist_simple.py).
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- Multi-layer perceptron (MNIST) - Classification using Iterator, see [method1](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_mlp_dropout1.py) and [method2](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_mlp_dropout2.py).
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## Computer Vision
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- Multi-layer perceptron (MNIST) - Classification with dropout using iterator, see [method1](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_mlp_dropout1.py) (**use placeholder**) and [method2](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_mlp_dropout2.py) (**use reuse**).
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- Denoising Autoencoder (MNIST). Classification task, see [tutorial_mnist.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_mnist.py).
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- Stacked Denoising Autoencoder and Fine-Tuning (MNIST). Classification task, see [tutorial_mnist.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_mnist.py).
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- Convolutional Network (MNIST). Classification task, see [tutorial_mnist.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_mnist.py).
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- Convolutional Network (CIFAR-10). Classification task, see [tutorial\_cifar10.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_cifar10.py) and [tutorial\_cifar10_tfrecord.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_cifar10_tfrecord.py).
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- VGG 16 (ImageNet). Classification task, see [tl.models.VGG16](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_models_vgg16.py) or [tutorial_vgg16.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_vgg16.py).
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- VGG 19 (ImageNet). Classification task, see [tutorial_vgg19.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_vgg19.py).
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- InceptionV3 (ImageNet). Classification task, see [tutorial\_inceptionV3_tfslim.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_inceptionV3_tfslim.py).
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- SqueezeNet (ImageNet). Model compression, see [tl.models.SqueezeNetV1](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_models_squeezenetv1.py) or [tutorial_squeezenet.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_squeezenet.py)
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- MobileNet (ImageNet). Model compression, see [tl.models.MobileNetV1](https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_models_mobilenetv1.py) or [tutorial_mobilenet.py](https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_mobilenet.py).
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- BinaryNet. Model compression, see [mnist](https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_binarynet_mnist_cnn.py)[cifar10](https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_binarynet_cifar10_tfrecord.py).
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- Ternary Weight Network. Model compression, see [mnist](https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_ternaryweight_mnist_cnn.py)[cifar10](https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_ternaryweight_cifar10_tfrecord.py).
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- DoReFa-Net. Model compression, see [mnist](https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_dorefanet_mnist_cnn.py)[cifar10](https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_dorefanet_cifar10_tfrecord.py).
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- TensorFlow dataset API for object detection see [here](https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_tf_dataset_voc.py).
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- Merge TF-Slim into TensorLayer. [tutorial\_inceptionV3_tfslim.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_inceptionV3_tfslim.py).
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- Merge Keras into TensorLayer. [tutorial_keras.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_keras.py).
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- Data augmentation with TFRecord. Effective way to load and pre-process data, see [tutorial_tfrecord*.py](https://github.com/zsdonghao/tensorlayer/tree/master/example) and [tutorial\_cifar10_tfrecord.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_cifar10_tfrecord.py).
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- Data augmentation with TensorLayer, see [tutorial\_image_preprocess.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_image_preprocess.py).
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- Float 16 half-precision model, see [tutorial\_mnist_float16.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_mnist_float16.py)
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- Distributed Training. [mnist](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_mnist_distributed.py) and [imagenet](https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_imagenet_inceptionV3_distributed.py) by [jorgemf](https://github.com/jorgemf).
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## Vision
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- ArcFace: Additive Angular Margin Loss for Deep Face Recognition, see [InsignFace](https://github.com/auroua/InsightFace_TF).
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- BinaryNet. Model acceleration, see [mnist](https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_binarynet_mnist_cnn.py)[cifar10](https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_binarynet_cifar10_tfrecord.py).
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- Ternary Weight Network. Model acceleration, see [mnist](https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_ternaryweight_mnist_cnn.py)[cifar10](https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_ternaryweight_cifar10_tfrecord.py).
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- DoReFa-Net. Model acceleration, see [mnist](https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_dorefanet_mnist_cnn.py)[cifar10](https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_dorefanet_cifar10_tfrecord.py).
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- Wide ResNet (CIFAR) by [ritchieng](https://github.com/ritchieng/wideresnet-tensorlayer).
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- More CNN implementations of [TF-Slim](https://github.com/tensorflow/models/tree/master/research/slim) can be connected to TensorLayer via SlimNetsLayer.
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-[Spatial Transformer Networks](https://arxiv.org/abs/1506.02025) by [zsdonghao](https://github.com/zsdonghao/Spatial-Transformer-Nets).
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-[U-Net for brain tumor segmentation](https://github.com/zsdonghao/u-net-brain-tumor) by [zsdonghao](https://github.com/zsdonghao/u-net-brain-tumor).
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- Variational Autoencoder (VAE) for (CelebA) by [yzwxx](https://github.com/yzwxx/vae-celebA).
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- Variational Autoencoder (VAE) for (MNIST) by [BUPTLdy](https://github.com/BUPTLdy/tl-vae).
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- Image Captioning - Reimplementation of Google's [im2txt](https://github.com/tensorflow/models/tree/master/research/im2txt) by [zsdonghao](https://github.com/zsdonghao/Image-Captioning).
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## Adversarial Learning
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- DCGAN (CelebA). Generating images by [Deep Convolutional Generative Adversarial Networks](http://arxiv.org/abs/1511.06434) by [zsdonghao](https://github.com/zsdonghao/dcgan).
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-[Generative Adversarial Text to Image Synthesis](https://github.com/zsdonghao/text-to-image) by [zsdonghao](https://github.com/zsdonghao/text-to-image).
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-[Unsupervised Image to Image Translation with Generative Adversarial Networks](https://github.com/zsdonghao/Unsup-Im2Im) by [zsdonghao](https://github.com/zsdonghao/Unsup-Im2Im).
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-[Improved CycleGAN](https://github.com/luoxier/CycleGAN_Tensorlayer) with resize-convolution by [luoxier](https://github.com/luoxier/CycleGAN_Tensorlayer)
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-[Super Resolution GAN](https://arxiv.org/abs/1609.04802) by [zsdonghao](https://github.com/zsdonghao/SRGAN).
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-[DAGAN: Fast Compressed Sensing MRI Reconstruction](https://github.com/nebulaV/DAGAN) by [nebulaV](https://github.com/nebulaV/DAGAN).
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## Natural Language Processing
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- Recurrent Neural Network (LSTM). Apply multiple LSTM to PTB dataset for language modeling, see [tutorial_ptb_lstm.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_ptb_lstm.py) and [tutorial\_ptb\_lstm\_state\_is_tuple.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_ptb_lstm_state_is_tuple.py).
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- Word Embedding (Word2vec). Train a word embedding matrix, see [tutorial\_word2vec_basic.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial\_word2vec_basic.py).
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-[Chatbot in 200 lines of code](https://github.com/zsdonghao/seq2seq-chatbot) for [Seq2Seq](http://tensorlayer.readthedocs.io/en/latest/modules/layers.html#simple-seq2seq).
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- FastText Sentence Classification (IMDB), see [tutorial\_imdb\_fasttext.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_imdb_fasttext.py) by [tomtung](https://github.com/tomtung).
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## Adversarial Learning
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- DCGAN (CelebA). Generating images by [Deep Convolutional Generative Adversarial Networks](http://arxiv.org/abs/1511.06434) by [zsdonghao](https://github.com/zsdonghao/dcgan).
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-[Generative Adversarial Text to Image Synthesis](https://github.com/zsdonghao/text-to-image) by [zsdonghao](https://github.com/zsdonghao/text-to-image).
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-[Unsupervised Image to Image Translation with Generative Adversarial Networks](https://github.com/zsdonghao/Unsup-Im2Im) by [zsdonghao](https://github.com/zsdonghao/Unsup-Im2Im).
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-[Improved CycleGAN](https://github.com/luoxier/CycleGAN_Tensorlayer) with resize-convolution by [luoxier](https://github.com/luoxier/CycleGAN_Tensorlayer)
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-[Super Resolution GAN](https://arxiv.org/abs/1609.04802) by [zsdonghao](https://github.com/zsdonghao/SRGAN).
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-[DAGAN: Fast Compressed Sensing MRI Reconstruction](https://github.com/nebulaV/DAGAN) by [nebulaV](https://github.com/nebulaV/DAGAN).
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## Reinforcement Learning
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- Policy Gradient / Network (Atari Ping Pong), see [tutorial\_atari_pong.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_atari_pong.py).
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-[DAGGER](https://www.cs.cmu.edu/%7Esross1/publications/Ross-AIStats11-NoRegret.pdf) for ([Gym Torcs](https://github.com/ugo-nama-kun/gym_torcs)) by [zsdonghao](https://github.com/zsdonghao/Imitation-Learning-Dagger-Torcs).
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-[TRPO](https://arxiv.org/abs/1502.05477) for continuous and discrete action space by [jjkke88](https://github.com/jjkke88/RL_toolbox).
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## Pretrained Models
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- VGG 16 (ImageNet). Classification task, see [tl.models.VGG16](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_models_vgg16.py) or [tutorial_vgg16.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_vgg16.py).
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- VGG 19 (ImageNet). Classification task, see [tutorial_vgg19.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_vgg19.py).
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- InceptionV3 (ImageNet). Classification task, see [tutorial\_inceptionV3_tfslim.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_inceptionV3_tfslim.py).
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- SqueezeNet (ImageNet). Model acceleration, see [tl.models.SqueezeNetV1](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_models_squeezenetv1.py) or [tutorial_squeezenet.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_squeezenet.py)
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- MobileNet (ImageNet). Model acceleration, see [tl.models.MobileNetV1](https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_models_mobilenetv1.py) or [tutorial_mobilenet.py](https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_mobilenet.py).
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- More CNN implementations of [TF-Slim](https://github.com/tensorflow/models/tree/master/research/slim) can be connected to TensorLayer via SlimNetsLayer.
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- All pretrained models in [here](https://github.com/tensorlayer/pretrained-models).
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## Miscellaneous
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- Distributed Training. [mnist](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_mnist_distributed.py) and [imagenet](https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_imagenet_inceptionV3_distributed.py) by [jorgemf](https://github.com/jorgemf).
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- Merge TF-Slim into TensorLayer. [tutorial\_inceptionV3_tfslim.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_inceptionV3_tfslim.py).
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- Merge Keras into TensorLayer. [tutorial_keras.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_keras.py).
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- Data augmentation with TFRecord. Effective way to load and pre-process data, see [tutorial_tfrecord*.py](https://github.com/zsdonghao/tensorlayer/tree/master/example) and [tutorial\_cifar10_tfrecord.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_cifar10_tfrecord.py).
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- Data augmentation with TensorLayer, see [tutorial\_image_preprocess.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_image_preprocess.py).
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- TensorDB by [fangde](https://github.com/fangde) see [here](https://github.com/akaraspt/tl_paper).
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- A simple web service - [TensorFlask](https://github.com/JoelKronander/TensorFlask) by [JoelKronander](https://github.com/JoelKronander).
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- Float 16 half-precision model, see [tutorial\_mnist_float16.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_mnist_float16.py)
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## Notes
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TensorLayer provides two set of Convolutional layer APIs, see [(Advanced)](http://tensorlayer.readthedocs.io/en/latest/modules/layers.html#convolutional-layer-pro) and [(Basic)](http://tensorlayer.readthedocs.io/en/latest/modules/layers.html#convolutional-layer-simplified) on readthedocs website.
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