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.

Arvind Neekalantan 0ec2bebf31 edits to README 9 rokov pred
.github dc7791d01c Create ISSUE_TEMPLATE.md (#124) 9 rokov pred
autoencoder a472ac9525 merged changes from #25 10 rokov pred
compression b181b9885c Update README with results for comparison. 9 rokov pred
differential_privacy a66b9e13c9 added semi-supervised training of the student using improved-gan (#655) 9 rokov pred
im2txt 0cba7a4b5d Remove comment that TensorFlow must be built from source. 9 rokov pred
inception bf51d43420 fix module object has no attribute NodeDef for tensorflow 0.11 (#572) 9 rokov pred
lm_1b fdc4ce37a4 Fix README 9 rokov pred
namignizer 76f567df5f add the namignizer model (#147) 9 rokov pred
neural_gpu a803bf4171 Add to neural_gpu documentation. 9 rokov pred
neural_programmer 0ec2bebf31 edits to README 9 rokov pred
resnet d93ffd0b69 Allow softplacement for ResNet 9 rokov pred
slim ea207d8a4d Updating README.md 9 rokov pred
street f42469ef90 Updated download instructions to match reality 9 rokov pred
swivel f3144eb061 Add sys.stdout.flush() 9 rokov pred
syntaxnet 5eff490de4 Fix POS tagging score of Ling et al.(2005) 9 rokov pred
textsum 5e875226bc Explicitly set state_is_tuple=False. 9 rokov pred
transformer d816971032 Use tf.softmax_cross_entropy_with_logits to calculate loss (#181) 9 rokov pred
video_prediction d67ea24901 video prediction model code 9 rokov pred
.gitignore 3e6caf5ff0 Add a .gitignore file. (#164) 9 rokov pred
.gitmodules 32ab5a58dd Adding SyntaxNet to tensorflow/models (#63) 9 rokov pred
AUTHORS 41c52d60fe Spatial Transformer model 10 rokov pred
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README.md 5860966afe Get back the README 9 rokov pred
WORKSPACE ac0829fa2b Consolidate privacy/ and differential_privacy/. 9 rokov pred

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.