Examples

Basics

• Multi-layer perceptron (MNIST). Classification task, see tutorial_mnist_simple.py.
• 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.
• 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.
• 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.
• BEGAN: Boundary Equilibrium Generative Adversarial Networks by 2wins.
• DAGAN: Fast Compressed Sensing MRI Reconstruction by nebulaV.

Natural Language Processing

• Recurrent Neural Network (LSTM). Apply multiple LSTM to PTB dataset for language modeling, see tutorial_ptb_lstm_state_is_tuple.py.
• Word Embedding (Word2vec). Train a word embedding matrix, see tutorial_word2vec_basic.py.
• Restore Embedding matrix. Restore a pre-train embedding matrix, see tutorial_generate_text.py.
• Text Generation. Generates new text scripts, using LSTM network, see tutorial_generate_text.py.
• Chinese Text Anti-Spam by pakrchen.
• Chatbot in 200 lines of code for Seq2Seq.
• FastText Sentence Classification (IMDB), see tutorial_imdb_fasttext.py by tomtung.

Reinforcement Learning

• Policy Gradient / Network (Atari Ping Pong), see tutorial_atari_pong.py.
• Deep Q-Network (Frozen lake), see tutorial_frozenlake_dqn.py.
• Q-Table learning algorithm (Frozen lake), see tutorial_frozenlake_q_table.py.
• Asynchronous Policy Gradient using TensorDB (Atari Ping Pong) by nebulaV.
• AC for discrete action space (Cartpole), see tutorial_cartpole_ac.py.
• A3C for continuous action space (Bipedal Walker), see tutorial_bipedalwalker_a3c*.py.
• 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 pretrained-models.

Miscellaneous

• TensorDB by fangde see tl_paper.
• A simple web service - TensorFlask by JoelKronander.