TensorFlow Models

This repository contains machine learning models implemented in TensorFlow. The models are maintained by their respective authors.

To propose a model for inclusion please submit a pull request.

Models

autoencoder -- various autoencoders.
compression -- compressing and decompressing images using pre-trained Residual GRU network.
differential_privacy -- privacy-preserving student models from multiple teachers.
im2txt -- image-to-text neural network for image captioning.
lm_1b -- language modelling on one billion word benchmark.
inception -- deep convolutional networks for computer vision.
namignizer -- recognize and generate names.
neural_gpu -- highly parallel neural computer.
neural_programmer -- neural network augmented with logic and mathematic operations.
next_frame_prediction -- probabilistic future frame synthesis via cross convolutional networks.
real_nvp -- density estimation using real-valued non-volume preserving (real NVP).
resnet -- deep and wide residual networks.
slim -- image classification models in TF-Slim.
street -- identify the name of a street (in France) from an image using Deep RNN.
swivel -- the Swivel algorithm for generating word embeddings.
syntaxnet -- neural models of natural language syntax.
textsum -- sequence-to-sequence with attention model for text summarization.
transformer -- spatial transformer network, which allows the spatial manipulation of data within the network.
tutorials -- models referenced to from the TensorFlow tutorials.
video_prediction -- predicting future video frames with neural advection.