Example

Basics

  • Multi-layer perceptron (MNIST). A multi-layer perceptron implementation for MNIST classification task, see tutorial_mnist_simple.py on GitHub.

Computer Vision

  • Denoising Autoencoder (MNIST). A multi-layer perceptron implementation for MNIST classification task, see tutorial_mnist.py on GitHub.
  • Stacked Denoising Autoencoder and Fine-Tuning (MNIST). A multi-layer perceptron implementation for MNIST classification task, see tutorial_mnist.py on GitHub.
  • Convolutional Network (MNIST). A Convolutional neural network implementation for classifying MNIST dataset, see tutorial_mnist.py on GitHub.
  • Convolutional Network (CIFAR-10). A Convolutional neural network implementation for classifying CIFAR-10 dataset, see tutorial_cifar10.py and ``tutorial_cifar10_tfrecord.py``on GitHub.
  • VGG 16 (ImageNet). A Convolutional neural network implementation for classifying ImageNet dataset, see tutorial_vgg16.py on GitHub.
  • VGG 19 (ImageNet). A Convolutional neural network implementation for classifying ImageNet dataset, see tutorial_vgg19.py on GitHub.
  • InceptionV3 (ImageNet). A Convolutional neural network implementation for classifying ImageNet dataset, see tutorial_inceptionV3_tfslim.py on GitHub.
  • Wide ResNet (CIFAR) by ritchieng.
  • More CNN implementations of TF-Slim can be connected to TensorLayer via SlimNetsLayer.

Natural Language Processing

  • Recurrent Neural Network (LSTM). Apply multiple LSTM to PTB dataset for language modeling, see tutorial_ptb_lstm_state_is_tuple.py on GitHub.
  • Word Embedding - Word2vec. Train a word embedding matrix, see tutorial_word2vec_basic.py on GitHub.
  • Restore Embedding matrix. Restore a pre-train embedding matrix, see tutorial_generate_text.py on GitHub.
  • Text Generation. Generates new text scripts, using LSTM network, see tutorial_generate_text.py on GitHub.
  • Machine Translation (WMT). Translate English to French. Apply Attention mechanism and Seq2seq to WMT English-to-French translation data, see tutorial_translate.py on GitHub.

Reinforcement Learning

  • Deep Reinforcement Learning - Pong Game. Teach a machine to play Pong games, see tutorial_atari_pong.py on GitHub.

Applications

Special Examples

  • Merge TF-Slim into TensorLayer. tutorial_inceptionV3_tfslim.py on GitHub.
  • Merge Keras into TensorLayer. tutorial_keras.py on GitHub.
  • MultiplexerLayer. tutorial_mnist_multiplexer.py on GitHub.
  • Data augmentation with TFRecord. Effective way to load and pre-process data, see tutorial_tfrecord*.py and tutorial_cifar10_tfrecord.py on GitHub.
  • Data augmentation with TensorLayer, see tutorial_image_preprocess.py on GitHub.