Evaluate multiple checkpoints.

.gitignore

@@ -87,3 +87,6 @@ ENV/
 
 # Rope project settings
 .ropeproject
+
+# PyCharm
+.idea/

.gitmodules

@@ -1,3 +1,3 @@
 [submodule "tensorflow"]
-	path = syntaxnet/tensorflow
+	path = research/syntaxnet/tensorflow
 	url = https://github.com/tensorflow/tensorflow.git

CODEOWNERS

@@ -0,0 +1,52 @@
+* @tensorflow/tf-garden-team
+/official/ @tensorflow/tf-garden-team @karmel
+/research/adversarial_crypto/ @dave-andersen
+/research/adversarial_text/ @rsepassi @a-dai
+/research/adv_imagenet_models/ @AlexeyKurakin
+/research/attention_ocr/ @alexgorban
+/research/audioset/ @plakal @dpwe
+/research/autoencoders/ @snurkabill
+/research/brain_coder/ @danabo
+/research/cognitive_mapping_and_planning/ @s-gupta
+/research/compression/ @nmjohn
+/research/deeplab/ @aquariusjay @yknzhu @gpapan
+/research/delf/ @andrefaraujo
+/research/differential_privacy/ @panyx0718 @mironov
+/research/domain_adaptation/ @bousmalis @dmrd
+/research/gan/ @joel-shor
+/research/im2txt/ @cshallue
+/research/inception/ @shlens @vincentvanhoucke
+/research/learned_optimizer/ @olganw @nirum
+/research/learning_to_remember_rare_events/ @lukaszkaiser @ofirnachum
+/research/learning_unsupervised_learning/ @lukemetz @nirum
+/research/lexnet_nc/ @vered1986 @waterson
+/research/lfads/ @jazcollins @susillo
+/research/lm_1b/ @oriolvinyals @panyx0718
+/research/maskgan/ @a-dai
+/research/namignizer/ @knathanieltucker
+/research/neural_gpu/ @lukaszkaiser
+/research/neural_programmer/ @arvind2505
+/research/next_frame_prediction/ @panyx0718
+/research/object_detection/ @jch1 @tombstone @derekjchow @jesu9 @dreamdragon @pkulzc
+/research/pcl_rl/ @ofirnachum
+/research/ptn/ @xcyan @arkanath @hellojas @honglaklee
+/research/real_nvp/ @laurent-dinh
+/research/rebar/ @gjtucker
+/research/resnet/ @panyx0718
+/research/skip_thoughts/ @cshallue
+/research/slim/ @sguada @nathansilberman
+/research/street/ @theraysmith
+/research/swivel/ @waterson
+/research/syntaxnet/ @calberti @andorardo @bogatyy @markomernick
+/research/tcn/ @coreylynch @sermanet
+/research/tensorrt/ @karmel
+/research/textsum/ @panyx0718 @peterjliu
+/research/transformer/ @daviddao
+/research/video_prediction/ @cbfinn
+/research/fivo/ @dieterichlawson
+/samples/ @MarkDaoust
+/samples/languages/java/ @asimshankar
+/tutorials/embedding/ @zffchen78 @a-dai
+/tutorials/image/ @sherrym @shlens
+/tutorials/image/cifar10_estimator/ @tfboyd @protoget
+/tutorials/rnn/ @lukaszkaiser @ebrevdo

CONTRIBUTING.md

@@ -1,7 +1,7 @@
 # Contributing guidelines
 
 If you have created a model and would like to publish it here, please send us a
-pull request. For those just getting started with pull reuests, GitHub has a
+pull request. For those just getting started with pull requests, GitHub has a
 [howto](https://help.github.com/articles/using-pull-requests/).
 
 The code for any model in this repository is licensed under the Apache License

ISSUE_TEMPLATE.md

@@ -4,7 +4,7 @@ http://stackoverflow.com/questions/tagged/tensorflow
 
 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:
 
-1. It must be a bug or a feature request.
+1. It must be a bug, a feature request, or a significant problem with documentation (for small docs fixes please send a PR instead).
 2. The form below must be filled out.
 
 **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

@@ -1,40 +1,11 @@
 # 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.
+This repository contains a number of different models implemented in [TensorFlow](https://www.tensorflow.org):
 
-Currently, the models are compatible with TensorFlow 1.0 or later. If you are
-running TensorFlow 0.12 or earlier, please
-[upgrade your installation](https://www.tensorflow.org/install).
+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.
 
+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.
 
-## Models
-- [adversarial_crypto](adversarial_crypto): protecting communications with adversarial neural cryptography.
-- [adversarial_text](adversarial_text): semi-supervised sequence learning with adversarial training.
-- [attention_ocr](attention_ocr): a model for real-world image text extraction.
-- [autoencoder](autoencoder): various autoencoders.
-- [cognitive_mapping_and_planning](cognitive_mapping_and_planning): implementation of a spatial memory based mapping and planning architecture for visual navigation.
-- [compression](compression): compressing and decompressing images using a pre-trained Residual GRU network.
-- [differential_privacy](differential_privacy): privacy-preserving student models from multiple teachers.
-- [domain_adaptation](domain_adaptation): domain separation networks.
-- [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.
-- [skip_thoughts](skip_thoughts): recurrent neural network sentence-to-vector encoder.
-- [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.
+The [samples folder](samples) contains code snippets and smaller models that demonstrate features of TensorFlow, including code presented in various blog posts.
+
+The [tutorials folder](tutorials) is a collection of models described in the [TensorFlow tutorials](https://www.tensorflow.org/tutorials/).