Models built with TensorFlow
Python C++ Jupyter Notebook Shell Other
Permalink
Failed to load latest commit information.
.github Create ISSUE_TEMPLATE.md (#124) May 19, 2016
autoencoder merged changes from #25 Mar 19, 2016
compression Update README with results for comparison. Nov 16, 2016
differential_privacy added semi-supervised training of the student using improved-gan (#655) Nov 17, 2016
im2txt Update GraphKeys.VARIABLES to GraphKeys.GLOBAL_VARIABLES. Dec 7, 2016
inception fix the error of "TypeError: ones_initializer() got multiple values f… Dec 21, 2016
lm_1b Fix README Sep 12, 2016
namignizer add the namignizer model (#147) May 25, 2016
neural_gpu Add to neural_gpu documentation. Jun 15, 2016
neural_programmer edits to README Nov 25, 2016
next_frame_prediction Add cross conv model for next frame prediction. Dec 22, 2016
resnet Convert resnet model to use monitored_session Dec 9, 2016
slim Updating README.md Nov 18, 2016
street Updated download instructions to match reality Nov 2, 2016
swivel Add sys.stdout.flush() Oct 18, 2016
syntaxnet Fix POS tagging score of Ling et al.(2005) Oct 19, 2016
textsum Update data.py Dec 8, 2016
transformer Use tf.softmax_cross_entropy_with_logits to calculate loss (#181) Jun 23, 2016
tutorials Remove all references to 'tensorflow.models' which is no longer correct Dec 22, 2016
video_prediction video prediction model code Oct 7, 2016
.gitignore Add a .gitignore file. (#164) Jun 2, 2016
.gitmodules Adding SyntaxNet to tensorflow/models (#63) May 12, 2016
AUTHORS Spatial Transformer model Apr 1, 2016
CONTRIBUTING.md fixed contribution guidelines Jan 20, 2016
LICENSE Update LICENSE Mar 4, 2016
README.md Get back the README Nov 24, 2016
WORKSPACE Consolidate privacy/ and differential_privacy/. Nov 4, 2016

README.md

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
  • inception -- deep convolutional networks for computer vision
  • namignizer -- recognize and generate names
  • neural_gpu -- highly parallel neural computer
  • privacy -- privacy-preserving student models from multiple teachers
  • resnet -- deep and wide residual networks
  • slim -- image classification models in TF-Slim
  • 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
  • im2txt -- image-to-text neural network for image captioning.
  • neural_programmer -- neural network augmented with logic and mathematic operations.