# TensorFlow Models

This repository contains machine learning models implemented in
[TensorFlow](https://tensorflow.org). The models are maintained by their
respective authors.

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


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