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.

Rohan Jain 199db00e33 Merge pull request #510 from nickj-google/master 9 gadi atpakaļ
.github dc7791d01c Create ISSUE_TEMPLATE.md (#124) 9 gadi atpakaļ
autoencoder a472ac9525 merged changes from #25 10 gadi atpakaļ
compression 199db00e33 Merge pull request #510 from nickj-google/master 9 gadi atpakaļ
differential_privacy 107e72cc78 Add differential privacy training. 9 gadi atpakaļ
im2txt e93fdccac0 Revert "Use open() instead of tf.gfile.FastGFile()" 9 gadi atpakaļ
inception bf51d43420 fix module object has no attribute NodeDef for tensorflow 0.11 (#572) 9 gadi atpakaļ
lm_1b fdc4ce37a4 Fix README 9 gadi atpakaļ
namignizer 76f567df5f add the namignizer model (#147) 9 gadi atpakaļ
neural_gpu a803bf4171 Add to neural_gpu documentation. 9 gadi atpakaļ
privacy c711dc707e added private learning with multiple teachers (#331) 9 gadi atpakaļ
resnet 6515a419aa Update cifar input following data change. 9 gadi atpakaļ
slim 31559b690e Merge pull request #556 from rohitgirdhar/slim_bug_tf11 9 gadi atpakaļ
swivel f3144eb061 Add sys.stdout.flush() 9 gadi atpakaļ
syntaxnet 5eff490de4 Fix POS tagging score of Ling et al.(2005) 9 gadi atpakaļ
textsum 7498acc6e0 Merge pull request #377 from kaiix/textsum-multigpu 9 gadi atpakaļ
transformer d816971032 Use tf.softmax_cross_entropy_with_logits to calculate loss (#181) 9 gadi atpakaļ
video_prediction d67ea24901 video prediction model code 9 gadi atpakaļ
.gitignore 3e6caf5ff0 Add a .gitignore file. (#164) 9 gadi atpakaļ
.gitmodules 32ab5a58dd Adding SyntaxNet to tensorflow/models (#63) 9 gadi atpakaļ
AUTHORS 41c52d60fe Spatial Transformer model 10 gadi atpakaļ
CONTRIBUTING.md d84df16bc3 fixed contribution guidelines 10 gadi atpakaļ
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README.md 4f9d102483 Open source the image-to-text model based on the "Show and Tell" paper. 9 gadi atpakaļ

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.