From 6b3458dd4deedc1acd833ed97272cf67addb620e Mon Sep 17 00:00:00 2001 From: zsdonghao Date: Fri, 4 May 2018 12:12:34 +0100 Subject: [PATCH 1/2] update example list --- README.md | 60 ++++++++++++++++++++++++------------------- docs/user/example.rst | 60 ++++++++++++++++++++++++------------------- 2 files changed, 67 insertions(+), 53 deletions(-) diff --git a/README.md b/README.md index 90e6b4ef1..846b0c915 100644 --- a/README.md +++ b/README.md @@ -125,30 +125,40 @@ Examples can be found [in this folder](https://github.com/zsdonghao/tensorlayer/ ## Basics - Multi-layer perceptron (MNIST) - Classification task, see [tutorial\_mnist\_simple.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_mnist_simple.py). - - 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). - - -## Computer Vision + - 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**). - Denoising Autoencoder (MNIST). Classification task, see [tutorial_mnist.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_mnist.py). - Stacked Denoising Autoencoder and Fine-Tuning (MNIST). Classification task, see [tutorial_mnist.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_mnist.py). - Convolutional Network (MNIST). Classification task, see [tutorial_mnist.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_mnist.py). - 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). - - 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). - - VGG 19 (ImageNet). Classification task, see [tutorial_vgg19.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_vgg19.py). - - InceptionV3 (ImageNet). Classification task, see [tutorial\_inceptionV3_tfslim.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_inceptionV3_tfslim.py). - - 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) - - 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). - - 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). - - 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). - - 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). + - TensorFlow dataset API for object detection see [here](https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_tf_dataset_voc.py). + - Merge TF-Slim into TensorLayer. [tutorial\_inceptionV3_tfslim.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_inceptionV3_tfslim.py). + - Merge Keras into TensorLayer. [tutorial_keras.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_keras.py). + - 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). + - Data augmentation with TensorLayer, see [tutorial\_image_preprocess.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_image_preprocess.py). + - Float 16 half-precision model, see [tutorial\_mnist_float16.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_mnist_float16.py) + - 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). + +## Vision + - ArcFace: Additive Angular Margin Loss for Deep Face Recognition, see [InsignFace](https://github.com/auroua/InsightFace_TF). + - 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). + - 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). + - 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). - Wide ResNet (CIFAR) by [ritchieng](https://github.com/ritchieng/wideresnet-tensorlayer). - - More CNN implementations of [TF-Slim](https://github.com/tensorflow/models/tree/master/research/slim) can be connected to TensorLayer via SlimNetsLayer. - [Spatial Transformer Networks](https://arxiv.org/abs/1506.02025) by [zsdonghao](https://github.com/zsdonghao/Spatial-Transformer-Nets). - [U-Net for brain tumor segmentation](https://github.com/zsdonghao/u-net-brain-tumor) by [zsdonghao](https://github.com/zsdonghao/u-net-brain-tumor). - Variational Autoencoder (VAE) for (CelebA) by [yzwxx](https://github.com/yzwxx/vae-celebA). - Variational Autoencoder (VAE) for (MNIST) by [BUPTLdy](https://github.com/BUPTLdy/tl-vae). - Image Captioning - Reimplementation of Google's [im2txt](https://github.com/tensorflow/models/tree/master/research/im2txt) by [zsdonghao](https://github.com/zsdonghao/Image-Captioning). + +## Adversarial Learning +- DCGAN (CelebA). Generating images by [Deep Convolutional Generative Adversarial Networks](http://arxiv.org/abs/1511.06434) by [zsdonghao](https://github.com/zsdonghao/dcgan). +- [Generative Adversarial Text to Image Synthesis](https://github.com/zsdonghao/text-to-image) by [zsdonghao](https://github.com/zsdonghao/text-to-image). +- [Unsupervised Image to Image Translation with Generative Adversarial Networks](https://github.com/zsdonghao/Unsup-Im2Im) by [zsdonghao](https://github.com/zsdonghao/Unsup-Im2Im). +- [Improved CycleGAN](https://github.com/luoxier/CycleGAN_Tensorlayer) with resize-convolution by [luoxier](https://github.com/luoxier/CycleGAN_Tensorlayer) +- [Super Resolution GAN](https://arxiv.org/abs/1609.04802) by [zsdonghao](https://github.com/zsdonghao/SRGAN). +- [DAGAN: Fast Compressed Sensing MRI Reconstruction](https://github.com/nebulaV/DAGAN) by [nebulaV](https://github.com/nebulaV/DAGAN). + ## Natural Language Processing - 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). - 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). @@ -158,13 +168,6 @@ Examples can be found [in this folder](https://github.com/zsdonghao/tensorlayer/ - [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). - 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). -## Adversarial Learning -- DCGAN (CelebA). Generating images by [Deep Convolutional Generative Adversarial Networks](http://arxiv.org/abs/1511.06434) by [zsdonghao](https://github.com/zsdonghao/dcgan). -- [Generative Adversarial Text to Image Synthesis](https://github.com/zsdonghao/text-to-image) by [zsdonghao](https://github.com/zsdonghao/text-to-image). -- [Unsupervised Image to Image Translation with Generative Adversarial Networks](https://github.com/zsdonghao/Unsup-Im2Im) by [zsdonghao](https://github.com/zsdonghao/Unsup-Im2Im). -- [Improved CycleGAN](https://github.com/luoxier/CycleGAN_Tensorlayer) with resize-convolution by [luoxier](https://github.com/luoxier/CycleGAN_Tensorlayer) -- [Super Resolution GAN](https://arxiv.org/abs/1609.04802) by [zsdonghao](https://github.com/zsdonghao/SRGAN). -- [DAGAN: Fast Compressed Sensing MRI Reconstruction](https://github.com/nebulaV/DAGAN) by [nebulaV](https://github.com/nebulaV/DAGAN). ## Reinforcement Learning - Policy Gradient / Network (Atari Ping Pong), see [tutorial\_atari_pong.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_atari_pong.py). @@ -176,15 +179,20 @@ Examples can be found [in this folder](https://github.com/zsdonghao/tensorlayer/ - [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). - [TRPO](https://arxiv.org/abs/1502.05477) for continuous and discrete action space by [jjkke88](https://github.com/jjkke88/RL_toolbox). + +## Pretrained Models + - 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). + - VGG 19 (ImageNet). Classification task, see [tutorial_vgg19.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_vgg19.py). + - InceptionV3 (ImageNet). Classification task, see [tutorial\_inceptionV3_tfslim.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_inceptionV3_tfslim.py). + - 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) + - 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). + - More CNN implementations of [TF-Slim](https://github.com/tensorflow/models/tree/master/research/slim) can be connected to TensorLayer via SlimNetsLayer. + - All pretrained models in [here](https://github.com/tensorlayer/pretrained-models). + ## Miscellaneous - - 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). - - Merge TF-Slim into TensorLayer. [tutorial\_inceptionV3_tfslim.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_inceptionV3_tfslim.py). - - Merge Keras into TensorLayer. [tutorial_keras.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_keras.py). - - 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). - - Data augmentation with TensorLayer, see [tutorial\_image_preprocess.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_image_preprocess.py). - TensorDB by [fangde](https://github.com/fangde) see [here](https://github.com/akaraspt/tl_paper). - A simple web service - [TensorFlask](https://github.com/JoelKronander/TensorFlask) by [JoelKronander](https://github.com/JoelKronander). -- Float 16 half-precision model, see [tutorial\_mnist_float16.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_mnist_float16.py) + ## Notes 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. diff --git a/docs/user/example.rst b/docs/user/example.rst index c3f501e00..eb297b4b0 100644 --- a/docs/user/example.rst +++ b/docs/user/example.rst @@ -4,36 +4,46 @@ Examples ============ - Basics ============ - Multi-layer perceptron (MNIST). Classification task, see `tutorial_mnist_simple.py `_. - - Multi-layer perceptron (MNIST). Classification using Iterator, see `method1 `_ and `method2 `_. - -Computer Vision -================== - + - Multi-layer perceptron (MNIST). Classification with dropout using iterator, see `method1 `_ (**use placeholder**) and `method2 `_ (**use reuse**). - Denoising Autoencoder (MNIST). Classification task, see `tutorial_mnist.py `_. - Stacked Denoising Autoencoder and Fine-Tuning (MNIST). A MLP classification task, see `tutorial_mnist.py `_. - Convolutional Network (MNIST). Classification task, see `tutorial_mnist.py `_. - Convolutional Network (CIFAR-10). Classification task, see `tutorial_cifar10.py `_ and `tutorial_cifar10_tfrecord.py `_. - - VGG 16 (ImageNet). Classification task, see `tl.models.VGG16 `__ or `tutorial_vgg16.py `_. - - VGG 19 (ImageNet). Classification task, see `tutorial_vgg19.py `_. - - InceptionV3 (ImageNet). Classification task, see `tutorial_inceptionV3_tfslim.py `_. - - SqueezeNet (ImageNet). Model compression, see `tl.models.SqueezeNetV1 `__ or `tutorial_squeezenet.py `_. - - MobileNet (ImageNet). Model compression, see `tl.models.MobileNetV1 `__ or `tutorial_mobilenet.py `__. + - TensorFlow dataset API for object detection see `here `_. + - Merge TF-Slim into TensorLayer. `tutorial_inceptionV3_tfslim.py `_. + - Merge Keras into TensorLayer. `tutorial_keras.py `_. + - Data augmentation with TFRecord. Effective way to load and pre-process data, see `tutorial_tfrecord*.py `_ and `tutorial_cifar10_tfrecord.py `_. + - Data augmentation with TensorLayer, see `tutorial_image_preprocess.py `_. + - Float 16 half-precision model, see `tutorial_mnist_float16.py `_. + - Distributed Training. `mnist `_ and `imagenet `_ by `jorgemf `__. + +Vision +================== + + - ArcFace: Additive Angular Margin Loss for Deep Face Recognition, see `InsignFace `_. - BinaryNet. Model compression, see `mnist `__ `cifar10 `__. - Ternary Weight Network. Model compression, see `mnist `__ `cifar10 `__. - DoReFa-Net. Model compression, see `mnist `__ `cifar10 `__. - Wide ResNet (CIFAR) by `ritchieng `_. - - More CNN implementations of `TF-Slim `_ can be connected to TensorLayer via SlimNetsLayer. - `Spatial Transformer Networks `_ by `zsdonghao `__. - `U-Net for brain tumor segmentation `_ by `zsdonghao `__. - Variational Autoencoder (VAE) for (CelebA) by `yzwxx `_. - Variational Autoencoder (VAE) for (MNIST) by `BUPTLdy `_. - Image Captioning - Reimplementation of Google's `im2txt `_ by `zsdonghao `__. +Adversarial Learning +======================== + - DCGAN (CelebA). Generating images by `Deep Convolutional Generative Adversarial Networks `_ by `zsdonghao `__. + - `Generative Adversarial Text to Image Synthesis `_ by `zsdonghao `__. + - `Unsupervised Image to Image Translation with Generative Adversarial Networks `_ by `zsdonghao `__. + - `Improved CycleGAN `_ with resize-convolution by `luoxier `_. + - `Super Resolution GAN `_ by `zsdonghao `__. + - `DAGAN: Fast Compressed Sensing MRI Reconstruction `_ by `nebulaV `__. + Natural Language Processing ============================== @@ -45,15 +55,6 @@ Natural Language Processing - `Chatbot in 200 lines of code `_ for `Seq2Seq `_. - FastText Sentence Classification (IMDB), see `tutorial_imdb_fasttext.py `_ by `tomtung `__. -Adversarial Learning -======================== - - DCGAN (CelebA). Generating images by `Deep Convolutional Generative Adversarial Networks `_ by `zsdonghao `__. - - `Generative Adversarial Text to Image Synthesis `_ by `zsdonghao `__. - - `Unsupervised Image to Image Translation with Generative Adversarial Networks `_ by `zsdonghao `__. - - `Improved CycleGAN `_ with resize-convolution by `luoxier `_. - - `Super Resolution GAN `_ by `zsdonghao `__. - - `DAGAN: Fast Compressed Sensing MRI Reconstruction `_ by `nebulaV `__. - Reinforcement Learning ============================== @@ -66,17 +67,22 @@ Reinforcement Learning - `DAGGER `_ for (`Gym Torcs `_) by `zsdonghao `_. - `TRPO `_ for continuous and discrete action space by `jjkke88 `_. +Pretrained Models +================== + + - VGG 16 (ImageNet). Classification task, see `tl.models.VGG16 `__ or `tutorial_vgg16.py `_. + - VGG 19 (ImageNet). Classification task, see `tutorial_vgg19.py `_. + - InceptionV3 (ImageNet). Classification task, see `tutorial_inceptionV3_tfslim.py `_. + - SqueezeNet (ImageNet). Model compression, see `tl.models.SqueezeNetV1 `__ or `tutorial_squeezenet.py `_. + - MobileNet (ImageNet). Model compression, see `tl.models.MobileNetV1 `__ or `tutorial_mobilenet.py `__. + - More CNN implementations of `TF-Slim `_ can be connected to TensorLayer via SlimNetsLayer. + - All pretrained models in `here `_. + Miscellaneous ================= - - Distributed Training. `mnist `_ and `imagenet `_ by `jorgemf `__. - - Merge TF-Slim into TensorLayer. `tutorial_inceptionV3_tfslim.py `_. - - Merge Keras into TensorLayer. `tutorial_keras.py `_. - - Data augmentation with TFRecord. Effective way to load and pre-process data, see `tutorial_tfrecord*.py `_ and `tutorial_cifar10_tfrecord.py `_. - - Data augmentation with TensorLayer, see `tutorial_image_preprocess.py `_. - TensorDB by `fangde `_ see `here `_. - A simple web service - `TensorFlask `_ by `JoelKronander `__. - - Float 16 half-precision model, see `tutorial_mnist_float16.py `_. .. Applications From b8ff4350ab1998d4a8b10ec52b210d9ee1745e88 Mon Sep 17 00:00:00 2001 From: lg Date: Fri, 4 May 2018 22:54:46 +0800 Subject: [PATCH 2/2] fix Duplicate explicit target name --- docs/user/example.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/user/example.rst b/docs/user/example.rst index eb297b4b0..ee46b321d 100644 --- a/docs/user/example.rst +++ b/docs/user/example.rst @@ -76,12 +76,12 @@ Pretrained Models - SqueezeNet (ImageNet). Model compression, see `tl.models.SqueezeNetV1 `__ or `tutorial_squeezenet.py `_. - MobileNet (ImageNet). Model compression, see `tl.models.MobileNetV1 `__ or `tutorial_mobilenet.py `__. - More CNN implementations of `TF-Slim `_ can be connected to TensorLayer via SlimNetsLayer. - - All pretrained models in `here `_. + - All pretrained models in `pretrained-models `_. Miscellaneous ================= - - TensorDB by `fangde `_ see `here `_. + - TensorDB by `fangde `_ see `tl_paper `_. - A simple web service - `TensorFlask `_ by `JoelKronander `__. ..