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Move the research models into a research subfolder (tensorflow#2430)
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[submodule "tensorflow"] | ||
path = syntaxnet/tensorflow | ||
path = research/syntaxnet/tensorflow | ||
url = https://github.com/tensorflow/tensorflow.git |
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adversarial_crypto/* @dave-andersen | ||
adversarial_text/* @rsepassi | ||
adv_imagenet_models/* @AlexeyKurakin | ||
attention_ocr/* @alexgorban | ||
audioset/* @plakal @dpwe | ||
autoencoders/* @snurkabill | ||
cognitive_mapping_and_planning/* @s-gupta | ||
compression/* @nmjohn | ||
differential_privacy/* @panyx0718 | ||
domain_adaptation/* @bousmalis @ddohan | ||
im2txt/* @cshallue | ||
inception/* @shlens @vincentvanhoucke | ||
learned_optimizer/* @olganw @nirum | ||
learning_to_remember_rare_events/* @lukaszkaiser @ofirnachum | ||
lfads/* @jazcollins @susillo | ||
lm_1b/* @oriolvinyals @panyx0718 | ||
namignizer/* @knathanieltucker | ||
neural_gpu/* @lukaszkaiser | ||
neural_programmer/* @arvind2505 | ||
next_frame_prediction/* @panyx0718 | ||
object_detection/* @jch1 @tombstone @derekjchow @jesu9 @dreamdragon | ||
pcl_rl/* @ofirnachum | ||
ptn/* @xcyan @arkanath @hellojas @honglaklee | ||
real_nvp/* @laurent-dinh | ||
rebar/* @gjtucker | ||
resnet/* @panyx0718 | ||
skip_thoughts/* @cshallue | ||
slim/* @sguada @nathansilberman | ||
street/* @theraysmith | ||
swivel/* @waterson | ||
syntaxnet/* @calberti @andorardo | ||
textsum/* @panyx0718 @peterjliu | ||
transformer/* @daviddao | ||
official/* @nealwu @jhseu @itsmeolivia | ||
research/adversarial_crypto/* @dave-andersen | ||
research/adversarial_text/* @rsepassi | ||
research/adv_imagenet_models/* @AlexeyKurakin | ||
research/attention_ocr/* @alexgorban | ||
research/audioset/* @plakal @dpwe | ||
research/autoencoders/* @snurkabill | ||
research/cognitive_mapping_and_planning/* @s-gupta | ||
research/compression/* @nmjohn | ||
research/differential_privacy/* @panyx0718 | ||
research/domain_adaptation/* @bousmalis @ddohan | ||
research/im2txt/* @cshallue | ||
research/inception/* @shlens @vincentvanhoucke | ||
research/learned_optimizer/* @olganw @nirum | ||
research/learning_to_remember_rare_events/* @lukaszkaiser @ofirnachum | ||
research/lfads/* @jazcollins @susillo | ||
research/lm_1b/* @oriolvinyals @panyx0718 | ||
research/namignizer/* @knathanieltucker | ||
research/neural_gpu/* @lukaszkaiser | ||
research/neural_programmer/* @arvind2505 | ||
research/next_frame_prediction/* @panyx0718 | ||
research/object_detection/* @jch1 @tombstone @derekjchow @jesu9 @dreamdragon | ||
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 | ||
research/textsum/* @panyx0718 @peterjliu | ||
research/transformer/* @daviddao | ||
research/video_prediction/* @cbfinn | ||
tutorials/embedding/* @zffchen78 @a-dai | ||
tutorials/image/* @sherrym @shlens | ||
tutorials/rnn/* @lukaszkaiser @ebrevdo | ||
video_prediction/* @cbfinn | ||
|
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# 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://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](research) are a large collection of models implemented in TensorFlow by researchers. | ||
|
||
## 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. | ||
- [audioset](audioset): Models and supporting code for use with [AudioSet](http://g.co.audioset). | ||
- [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. | ||
- [lfads](lfads): sequential variational autoencoder for analyzing neuroscience data. | ||
- [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. | ||
- [object_detection](object_detection): localizing and identifying multiple objects in a single image. | ||
- [pcl_rl](pcl_rl): code for several reinforcement learning algorithms, including Path Consistency Learning. | ||
- [ptn](ptn): perspective transformer nets for 3D object reconstruction. | ||
- [qa_kg](qa_kg): module networks for question answering on knowledge graphs. | ||
- [real_nvp](real_nvp): density estimation using real-valued non-volume preserving (real NVP) transformations. | ||
- [rebar](rebar): low-variance, unbiased gradient estimates for discrete latent variable models. | ||
- [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 [tutorial models](tutorials) are models described in the [TensorFlow tutorials](https://www.tensorflow.org/tutorials/). |
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# TensorFlow Research Models | ||
|
||
This folder contains machine learning models implemented by researchers in | ||
[TensorFlow](https://tensorflow.org). The models are maintained by their | ||
respective authors. To propose a model for inclusion, please submit a pull | ||
request. | ||
|
||
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). | ||
|
||
|
||
## 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. | ||
- [audioset](audioset): Models and supporting code for use with [AudioSet](http://g.co.audioset). | ||
- [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. | ||
- [lfads](lfads): sequential variational autoencoder for analyzing neuroscience data. | ||
- [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. | ||
- [object_detection](object_detection): localizing and identifying multiple objects in a single image. | ||
- [pcl_rl](pcl_rl): code for several reinforcement learning algorithms, including Path Consistency Learning. | ||
- [ptn](ptn): perspective transformer nets for 3D object reconstruction. | ||
- [qa_kg](qa_kg): module networks for question answering on knowledge graphs. | ||
- [real_nvp](real_nvp): density estimation using real-valued non-volume preserving (real NVP) transformations. | ||
- [rebar](rebar): low-variance, unbiased gradient estimates for discrete latent variable models. | ||
- [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. | ||
- [video_prediction](video_prediction): predicting future video frames with neural advection. |
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