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.
Natural Language Processing¶
- Recurrent Neural Network (LSTM). Apply multiple LSTM to PTB dataset for language modeling, see
tutorial_ptb_lstm.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.