Update example.rst

docs/user/example.rst

@@ -7,82 +7,83 @@ Examples
 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 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). A MLP 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>`_.
- - 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/zsdonghao/tensorlayer/blob/master/example/tutorial_inceptionV3_tfslim.py>`_ by `jorgemf <https://github.com/jorgemf>`__.
+ - 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 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). A MLP 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>`__.
+ - 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/zsdonghao/tensorlayer/blob/master/example/tutorial_inceptionV3_tfslim.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>`_.
+ - ArcFace: Additive Angular Margin Loss for Deep Face Recognition, see `InsignFace <https://github.com/auroua/InsightFace_TF>`__.
  - 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>`__.
- - Wide ResNet (CIFAR) by `ritchieng <https://github.com/ritchieng/wideresnet-tensorlayer>`_.
- - `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>`__.
+ - Wide ResNet (CIFAR) by `ritchieng <https://github.com/ritchieng/wideresnet-tensorlayer>`__.
+ - `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>`__.
+ - 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>`__.
+ - `BEGAN: Boundary Equilibrium Generative Adversarial Networks <http://arxiv.org/abs/1703.10717>`__ by `2wins <https://github.com/2wins/BEGAN-tensorlayer>`__.
+ - `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_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>`_.
- - Restore Embedding matrix. Restore a pre-train embedding matrix, see `tutorial_generate_text.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_generate_text.py>`_.
- - Text Generation. Generates new text scripts, using LSTM network, see `tutorial_generate_text.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_generate_text.py>`_.
- - Chinese Text Anti-Spam by `pakrchen <https://github.com/pakrchen/text-antispam>`_.
- - `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>`__.
+ - Recurrent Neural Network (LSTM). Apply multiple LSTM to PTB dataset for language modeling, see `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>`__.
+ - Restore Embedding matrix. Restore a pre-train embedding matrix, see `tutorial_generate_text.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_generate_text.py>`__.
+ - Text Generation. Generates new text scripts, using LSTM network, see `tutorial_generate_text.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_generate_text.py>`__.
+ - Chinese Text Anti-Spam by `pakrchen <https://github.com/pakrchen/text-antispam>`__.
+ - `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>`__.
 
 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>`_.
- - Deep Q-Network (Frozen lake), see `tutorial_frozenlake_dqn.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_frozenlake_dqn.py>`_.
- - Q-Table learning algorithm (Frozen lake), see `tutorial_frozenlake_q_table.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_frozenlake_q_table.py>`_.
- - Asynchronous Policy Gradient using TensorDB (Atari Ping Pong) by `nebulaV <https://github.com/akaraspt/tl_paper>`_.
- - AC for discrete action space (Cartpole), see `tutorial_cartpole_ac.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_cartpole_ac.py>`_.
- - A3C for continuous action space (Bipedal Walker), see `tutorial_bipedalwalker_a3c*.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_bipedalwalker_a3c_continuous_action.py>`_.
- - `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>`_.
+ - Policy Gradient / Network (Atari Ping Pong), see `tutorial_atari_pong.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_atari_pong.py>`__.
+ - Deep Q-Network (Frozen lake), see `tutorial_frozenlake_dqn.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_frozenlake_dqn.py>`__.
+ - Q-Table learning algorithm (Frozen lake), see `tutorial_frozenlake_q_table.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_frozenlake_q_table.py>`__.
+ - Asynchronous Policy Gradient using TensorDB (Atari Ping Pong) by `nebulaV <https://github.com/akaraspt/tl_paper>`__.
+ - AC for discrete action space (Cartpole), see `tutorial_cartpole_ac.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_cartpole_ac.py>`__.
+ - A3C for continuous action space (Bipedal Walker), see `tutorial_bipedalwalker_a3c*.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_bipedalwalker_a3c_continuous_action.py>`__.
+ - `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 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>`_.
+ - 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>`__.
- - 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 `pretrained-models <https://github.com/tensorlayer/pretrained-models>`_.
+ - 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 `pretrained-models <https://github.com/tensorlayer/pretrained-models>`__.
 
 Miscellaneous
 =================
 
- - TensorDB by `fangde <https://github.com/fangde>`_ see `tl_paper <https://github.com/akaraspt/tl_paper>`_.
- - A simple web service - `TensorFlask <https://github.com/JoelKronander/TensorFlask>`_ by `JoelKronander <https://github.com/JoelKronander>`__.
+ - TensorDB by `fangde <https://github.com/fangde>`__ see `tl_paper <https://github.com/akaraspt/tl_paper>`__.
+ - A simple web service - `TensorFlask <https://github.com/JoelKronander/TensorFlask>`__ by `JoelKronander <https://github.com/JoelKronander>`__.
 
 ..
   Applications
@@ -95,7 +96,7 @@ Miscellaneous
   1D CNN + LSTM for Biosignal
   ---------------------------------
 
-  Author : `Akara Supratak <https://akaraspt.github.io>`_
+  Author : `Akara Supratak <https://akaraspt.github.io>`__
 
   Introduction
   ^^^^^^^^^^^^