Merge pull request #1 from tensorflow/master

update to tensorflow/model master

ISSUE_TEMPLATE.md

@@ -0,0 +1,37 @@
+Please go to Stack Overflow for help and support:
+
+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.
+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.
+
+------------------------
+
+### System information
+- **What is the top-level directory of the model you are using**:
+- **Have I written custom code (as opposed to using a stock example script provided in TensorFlow)**:
+- **OS Platform and Distribution (e.g., Linux Ubuntu 16.04)**:
+- **TensorFlow installed from (source or binary)**:
+- **TensorFlow version (use command below)**:
+- **Bazel version (if compiling from source)**:
+- **CUDA/cuDNN version**:
+- **GPU model and memory**:
+- **Exact command to reproduce**:
+
+You can collect some of this information using our environment capture script:
+
+https://github.com/tensorflow/tensorflow/tree/master/tools/tf_env_collect.sh
+
+You can obtain the TensorFlow version with
+
+python -c "import tensorflow as tf; print(tf.GIT_VERSION, tf.VERSION)"
+
+### Describe the problem
+Describe the problem clearly here. Be sure to convey here why it's a bug in TensorFlow or a feature request.
+
+### Source code / logs
+Include any logs or source code that would be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files should be attached. Try to provide a reproducible test case that is the bare minimum necessary to generate the problem.

README.md

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 This repository contains machine learning models implemented in
 [TensorFlow](https://tensorflow.org). The models are maintained by their
-respective authors.
+respective authors. To propose a model for inclusion, please submit a pull
+request.
 
-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
-- [autoencoder](autoencoder) -- various autoencoders
-- [differential_privacy](differential_privacy) -- privacy-preserving student models from multiple teachers
-- [im2txt](im2txt) -- image-to-text neural network for image captioning.
-- [inception](inception) -- deep convolutional networks for computer vision
-- [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.
-- [resnet](resnet) -- deep and wide residual networks
-- [slim](slim) -- image classification models in TF-Slim
-- [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
+- [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.

adversarial_crypto/README.md

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+# Learning to Protect Communications with Adversarial Neural Cryptography
+
+This is a slightly-updated model used for the paper
+["Learning to Protect Communications with Adversarial Neural
+Cryptography"](https://arxiv.org/abs/1610.06918).
+
+> We ask whether neural networks can learn to use secret keys to protect
+> information from other neural networks. Specifically, we focus on ensuring
+> confidentiality properties in a multiagent system, and we specify those
+> properties in terms of an adversary. Thus, a system may consist of neural
+> networks named Alice and Bob, and we aim to limit what a third neural
+> network named Eve learns from eavesdropping on the communication between
+> Alice and Bob. We do not prescribe specific cryptographic algorithms to
+> these neural networks; instead, we train end-to-end, adversarially.
+> We demonstrate that the neural networks can learn how to perform forms of
+> encryption and decryption, and also how to apply these operations
+> selectively in order to meet confidentiality goals.
+
+This code allows you to train an encoder/decoder/adversary triplet
+and evaluate their effectiveness on randomly generated input and key
+pairs.
+
+## Prerequisites
+
+The only software requirements for running the encoder and decoder is having
+Tensorflow installed.
+
+Requires Tensorflow r0.12 or later.
+
+## Training and evaluating
+
+After installing TensorFlow and ensuring that your paths are configured
+appropriately:
+
+```
+python train_eval.py
+```
+
+This will begin training a fresh model.  If and when the model becomes
+sufficiently well-trained, it will reset the Eve model multiple times
+and retrain it from scratch, outputting the accuracy thus obtained
+in each run.
+
+## Model differences from the paper
+
+The model has been simplified slightly from the one described in
+the paper - the convolutional layer width was reduced by a factor
+of two.  In the version in the paper, there was a nonlinear unit
+after the fully-connected layer;  that nonlinear has been removed
+here.  These changes improve the robustness of training.  The
+initializer for the convolution layers has switched to the
+tf.contrib.layers default of xavier_initializer instead of
+a simpler truncated_normal.
+
+## Contact information
+
+This model repository is maintained by David G. Andersen
+([dave-andersen](https://github.com/dave-andersen)).