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‎.gitignore

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# PyCharm
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.idea/
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# For mac
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.DS_Store
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samples/outreach/blogs/segmentation_blogpost/carvana-image-masking-challenge/

‎CODEOWNERS

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official/* @nealwu @k-w-w @jhseu @itsmeolivia
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research/adversarial_crypto/* @dave-andersen
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research/adversarial_text/* @rsepassi
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research/adv_imagenet_models/* @AlexeyKurakin
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research/attention_ocr/* @alexgorban
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research/audioset/* @plakal @dpwe
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research/autoaugment/* @barretzoph
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research/autoencoders/* @snurkabill
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research/cognitive_mapping_and_planning/* @s-gupta
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research/compression/* @nmjohn
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research/delf/* @andrefaraujo
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research/differential_privacy/* @panyx0718
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research/domain_adaptation/* @bousmalis @ddohan
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research/im2txt/* @cshallue
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research/inception/* @shlens @vincentvanhoucke
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research/learned_optimizer/* @olganw @nirum
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research/learning_to_remember_rare_events/* @lukaszkaiser @ofirnachum
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research/lfads/* @jazcollins @susillo
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research/lm_1b/* @oriolvinyals @panyx0718
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research/namignizer/* @knathanieltucker
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research/neural_gpu/* @lukaszkaiser
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research/neural_programmer/* @arvind2505
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research/next_frame_prediction/* @panyx0718
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research/object_detection/* @jch1 @tombstone @derekjchow @jesu9 @dreamdragon
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research/pcl_rl/* @ofirnachum
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research/ptn/* @xcyan @arkanath @hellojas @honglaklee
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research/real_nvp/* @laurent-dinh
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research/rebar/* @gjtucker
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research/resnet/* @panyx0718
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research/skip_thoughts/* @cshallue
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research/slim/* @sguada @nathansilberman
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research/street/* @theraysmith
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research/swivel/* @waterson
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research/syntaxnet/* @calberti @andorardo @bogatyy @markomernick
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research/textsum/* @panyx0718 @peterjliu
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research/transformer/* @daviddao
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research/video_prediction/* @cbfinn
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tutorials/embedding/* @zffchen78 @a-dai
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tutorials/image/* @sherrym @shlens
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tutorials/rnn/* @lukaszkaiser @ebrevdo
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* @tensorflow/tf-garden-team
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/official/ @tensorflow/tf-garden-team @karmel
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/research/adversarial_crypto/ @dave-andersen
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/research/adversarial_logit_pairing/ @AlexeyKurakin
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/research/adversarial_text/ @rsepassi @a-dai
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/research/adv_imagenet_models/ @AlexeyKurakin
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/research/attention_ocr/ @alexgorban
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/research/audioset/ @plakal @dpwe
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/research/autoaugment/* @barretzoph
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/research/autoencoders/ @snurkabill
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/research/brain_coder/ @danabo
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/research/cognitive_mapping_and_planning/ @s-gupta
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/research/compression/ @nmjohn
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/research/deep_contextual_bandits/ @rikel
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/research/deeplab/ @aquariusjay @yknzhu @gpapan
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/research/delf/ @andrefaraujo
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/research/differential_privacy/ @ilyamironov @ananthr
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/research/domain_adaptation/ @bousmalis @dmrd
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/research/efficient-hrl/ @ofirnachum
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/research/gan/ @joel-shor
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/research/global_objectives/ @mackeya-google
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/research/im2txt/ @cshallue
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/research/inception/ @shlens @vincentvanhoucke
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/research/keypointnet/ @mnorouzi
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/research/learned_optimizer/ @olganw @nirum
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/research/learning_to_remember_rare_events/ @lukaszkaiser @ofirnachum
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/research/learning_unsupervised_learning/ @lukemetz @nirum
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/research/lexnet_nc/ @vered1986 @waterson
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/research/lfads/ @jazcollins @susillo
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/research/lm_1b/ @oriolvinyals @panyx0718
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/research/lm_commonsense/ @thtrieu
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/research/marco/ @vincentvanhoucke
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/research/maskgan/ @a-dai
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/research/morph_net/ @gariel-google
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/research/namignizer/ @knathanieltucker
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/research/neural_gpu/ @lukaszkaiser
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/research/neural_programmer/ @arvind2505
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/research/next_frame_prediction/ @panyx0718
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/research/object_detection/ @jch1 @tombstone @derekjchow @jesu9 @dreamdragon @pkulzc
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/research/pcl_rl/ @ofirnachum
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/research/ptn/ @xcyan @arkanath @hellojas @honglaklee
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/research/real_nvp/ @laurent-dinh
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/research/rebar/ @gjtucker
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/research/resnet/ @panyx0718
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/research/seq2species/ @apbusia @depristo
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/research/skip_thoughts/ @cshallue
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/research/slim/ @sguada @nathansilberman
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/research/steve/ @buckman-google
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/research/street/ @theraysmith
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/research/swivel/ @waterson
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/research/syntaxnet/ @calberti @andorardo @bogatyy @markomernick
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/research/tcn/ @coreylynch @sermanet
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/research/tensorrt/ @karmel
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/research/textsum/ @panyx0718 @peterjliu
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/research/transformer/ @daviddao
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/research/vid2depth/ @rezama
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/research/video_prediction/ @cbfinn
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/research/fivo/ @dieterichlawson
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/samples/ @MarkDaoust @lamberta
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/samples/languages/java/ @asimshankar
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/tutorials/embedding/ @zffchen78 @a-dai
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/tutorials/image/ @sherrym @shlens
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/tutorials/image/cifar10_estimator/ @tfboyd @protoget
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/tutorials/rnn/ @lukaszkaiser @ebrevdo

‎ISSUE_TEMPLATE.md

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Also, please understand that many of the models included in this repository are experimental and research-style code. If you open a GitHub issue, here is our policy:
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1. It must be a bug or a feature request.
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1. It must be a bug, a feature request, or a significant problem with documentation (for small docs fixes please send a PR instead).
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2. The form below must be filled out.
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**Here's why we have that policy**: TensorFlow developers respond to issues. We want to focus on work that benefits the whole community, e.g., fixing bugs and adding features. Support only helps individuals. GitHub also notifies thousands of people when issues are filed. We want them to see you communicating an interesting problem, rather than being redirected to Stack Overflow.

‎README.md

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# TensorFlow Models
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This repository contains a number of different models implemented in [TensorFlow](https://tensorflow.org):
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This repository contains a number of different models implemented in [TensorFlow](https://www.tensorflow.org):
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The [official models](official) are a collection of example models that use TensorFlow's high-level APIs. They are intended to be well-maintained, tested, and kept up to date with the latest stable TensorFlow API. They should also be reasonably optimized for fast performance while still being easy to read. We especially recommend newer TensorFlow users to start here.
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The [research models](research) are a large collection of models implemented in TensorFlow by researchers.
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The [research models](https://github.com/tensorflow/models/tree/master/research) are a large collection of models implemented in TensorFlow by researchers. They are not officially supported or available in release branches; it is up to the individual researchers to maintain the models and/or provide support on issues and pull requests.
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The [tutorial models](tutorials) are models described in the [TensorFlow tutorials](https://www.tensorflow.org/tutorials/).
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The [samples folder](samples) contains code snippets and smaller models that demonstrate features of TensorFlow, including code presented in various blog posts.
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The [tutorials folder](tutorials) is a collection of models described in the [TensorFlow tutorials](https://www.tensorflow.org/tutorials/).
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## Contribution guidelines
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If you want to contribute to models, be sure to review the [contribution guidelines](CONTRIBUTING.md).
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## License
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[Apache License 2.0](LICENSE)

‎official/Dockerfile.cpu

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# Docker image for running examples in Tensorflow models.
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# base_image depends on whether we are running on GPUs or non-GPUs
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FROM ubuntu:latest
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RUN apt-get update && apt-get install -y --no-install-recommends \
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ca-certificates \
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build-essential \
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git \
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python \
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python-pip \
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python-setuptools
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RUN pip install tf-nightly
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# Checkout tensorflow/models at HEAD
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RUN git clone https://github.com/tensorflow/models.git /tensorflow_models
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‎official/Dockerfile.gpu

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# Docker image for running examples in Tensorflow models.
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# base_image depends on whether we are running on GPUs or non-GPUs
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FROM nvidia/cuda:9.0-cudnn7-runtime-ubuntu16.04
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RUN apt-get update && apt-get install -y --no-install-recommends \
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ca-certificates \
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build-essential \
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git \
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python \
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python-pip \
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python-setuptools
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RUN pip install tf-nightly-gpu
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# Checkout tensorflow/models at HEAD
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RUN git clone https://github.com/tensorflow/models.git /tensorflow_models
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‎official/README.md

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# TensorFlow Official Models
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The TensorFlow official models are a collection of example models that use TensorFlow's high-level APIs. They are intended to be well-maintained, tested, and kept up to date with the latest stable TensorFlow API. They should also be reasonably optimized for fast performance while still being easy to read.
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The TensorFlow official models are a collection of example models that use TensorFlow's high-level APIs. They are intended to be well-maintained, tested, and kept up to date with the latest TensorFlow API. They should also be reasonably optimized for fast performance while still being easy to read.
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Below is the list of models contained in the garden:
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These models are used as end-to-end tests, ensuring that the models run with the same speed and performance with each new TensorFlow build.
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[mnist](mnist): A basic model to classify digits from the MNIST dataset.
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## Tensorflow releases
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The master branch of the models are **in development**, and they target the [nightly binaries](https://github.com/tensorflow/tensorflow#installation) built from the [master branch of TensorFlow](https://github.com/tensorflow/tensorflow/tree/master). We aim to keep them backwards compatible with the latest release when possible (currently TensorFlow 1.5), but we cannot always guarantee compatibility.
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[resnet](resnet): A deep residual network that can be used to classify both CIFAR-10 and ImageNet's dataset of 1000 classes.
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**Stable versions** of the official models targeting releases of TensorFlow are available as tagged branches or [downloadable releases](https://github.com/tensorflow/models/releases). Model repository version numbers match the target TensorFlow release, such that [branch r1.4.0](https://github.com/tensorflow/models/tree/r1.4.0) and [release v1.4.0](https://github.com/tensorflow/models/releases/tag/v1.4.0) are compatible with [TensorFlow v1.4.0](https://github.com/tensorflow/tensorflow/releases/tag/v1.4.0).
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[wide_deep](wide_deep): A model that combines a wide model and deep network to classify census income data.
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If you are on a version of TensorFlow earlier than 1.4, please [update your installation](https://www.tensorflow.org/install/).
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More models to come!
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## Requirements
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Please follow the below steps before running models in this repo:
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1. Add the top-level ***/models*** folder to the Python path with the command:
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```
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export PYTHONPATH="$PYTHONPATH:/path/to/models"
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```
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2. Install dependencies:
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```
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pip3 install --user -r official/requirements.txt
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```
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or
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```
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pip install --user -r official/requirements.txt
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```
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To make Official Models easier to use, we are planning to create a pip installable Official Models package. This is being tracked in [#917](https://github.com/tensorflow/models/issues/917).
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## Available models
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**NOTE:** Please make sure to follow the steps in the [Requirements](#requirements) section.
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* [boosted_trees](boosted_trees): A Gradient Boosted Trees model to classify higgs boson process from HIGGS Data Set.
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* [mnist](mnist): A basic model to classify digits from the MNIST dataset.
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* [resnet](resnet): A deep residual network that can be used to classify both CIFAR-10 and ImageNet's dataset of 1000 classes.
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* [transformer](transformer): A transformer model to translate the WMT English to German dataset.
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* [wide_deep](wide_deep): A model that combines a wide model and deep network to classify census income data.
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* More models to come!
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If you would like to make any fixes or improvements to the models, please [submit a pull request](https://github.com/tensorflow/models/compare).
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## New Models
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The team is actively working to add new models to the repository. Every model should follow the following guidelines, to uphold the
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our objectives of readable, usable, and maintainable code.
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**General guidelines**
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* Code should be well documented and tested.
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* Runnable from a blank environment with relative ease.
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* Trainable on: single GPU/CPU (baseline), multiple GPUs, TPU
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* Compatible with Python 2 and 3 (using [six](https://pythonhosted.org/six/) when necessary)
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* Conform to [Google Python Style Guide](https://github.com/google/styleguide/blob/gh-pages/pyguide.md)
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**Implementation guidelines**
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These guidelines exist so the model implementations are consistent for better readability and maintainability.
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* Use [common utility functions](utils)
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* Export SavedModel at the end of training.
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* Consistent flags and flag-parsing library ([read more here](utils/flags/guidelines.md))
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* Produce benchmarks and logs ([read more here](utils/logs/guidelines.md))

‎official/__init__.py

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‎official/benchmark/__init__.py

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