Example¶
Basics¶
- Multi-layer perceptron (MNIST). A multi-layer perceptron implementation for MNIST classification task, see tutorial_mnist_simple.py.
- Multi-layer perceptron (MNIST) classification using Iterator, see method1 and method2.
Computer Vision¶
- Denoising Autoencoder (MNIST). A multi-layer perceptron implementation for MNIST classification task, see tutorial_mnist.py.
- Stacked Denoising Autoencoder and Fine-Tuning (MNIST). A multi-layer perceptron implementation for MNIST classification task, see tutorial_mnist.py.
- Convolutional Network (MNIST). A Convolutional neural network implementation for classifying MNIST dataset, see tutorial_mnist.py.
- Convolutional Network (CIFAR-10). A Convolutional neural network implementation for classifying CIFAR-10 dataset, see tutorial_cifar10.py and tutorial_cifar10_tfrecord.py.
- VGG 16 (ImageNet). A Convolutional neural network implementation for classifying ImageNet dataset, see tutorial_vgg16.py.
- VGG 19 (ImageNet). A Convolutional neural network implementation for classifying ImageNet dataset, see tutorial_vgg19.py.
- InceptionV3 (ImageNet). A Convolutional neural network implementation for classifying ImageNet dataset, see tutorial_inceptionV3_tfslim.py.
- 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.
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.
Adversarial Learning¶
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 - Gym Torcs by zsdonghao.
Applications¶
- Image Captioning - Reimplementation of Google’s im2txt by zsdonghao.
- A simple web service - TensorFlask by JoelKronander.
Special Examples¶
- 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.