Merge branch 'master' into master

README.md

@@ -1,8 +1,15 @@
 ## Edge Machine Learning
 
-This repository provides code for machine learning algorithms for edge devices developed at [Microsoft Research India](https://www.microsoft.com/en-us/research/project/resource-efficient-ml-for-the-edge-and-endpoint-iot-devices/). 
+This repository provides code for machine learning algorithms for edge devices
+developed at [Microsoft Research
+India](https://www.microsoft.com/en-us/research/project/resource-efficient-ml-for-the-edge-and-endpoint-iot-devices/). 
 
-Machine learning models for edge devices need to have a small footprint in terms of storage, prediction latency, and energy. One example of a ubiquitous real-world application where such models are desirable is resource-scarce devices and sensors in the Internet of Things (IoT) setting. Making real-time predictions locally on IoT devices without connecting to the cloud requires models that fit in a few kilobytes.
+Machine learning models for edge devices need to have a small footprint in
+terms of storage, prediction latency, and energy. One example of a ubiquitous
+real-world application where such models are desirable is resource-scarce
+devices and sensors in the Internet of Things (IoT) setting. Making real-time
+predictions locally on IoT devices without connecting to the cloud requires
+models that fit in a few kilobytes.
 
 This repository contains algorithms that shine in this setting in terms of both model size and compute, namely:
  - **Bonsai**: Strong and shallow non-linear tree based classifier.
@@ -10,11 +17,25 @@ This repository contains algorithms that shine in this setting in terms of both
  - **EMI-RNN**: Training routine to recover the critical signature from time series data for faster and accurate RNN predictions.
  - **Fast(G)RNN**: **F**ast, **A**ccurate, **S**table and **T**iny (**G**ated) RNN cells.
  
-These algorithms can train models for classical supervised learning problems with memory requirements that are orders of magnitude lower than other modern ML algorithms. The trained models can be loaded onto edge devices such as IoT devices/sensors, and used to make fast and accurate predictions completely offline.
+These algorithms can train models for classical supervised learning problems
+with memory requirements that are orders of magnitude lower than other modern
+ML algorithms. The trained models can be loaded onto edge devices such as IoT
+devices/sensors, and used to make fast and accurate predictions completely
+offline.
 
-The tf directrory contains code, examples and scripts for all these algorithms in TensorFlow. The cpp directory has training and inference code for Bonsai and ProtoNN algorithms in C++. Please see install/run instruction in the Readme pages within these directories. The Applications directory contains the code for the GesturePod - a gesture recognition system.
+The `tf` directrory contains code, examples and scripts for all these algorithms
+in TensorFlow. The `cpp` directory has training and inference code for Bonsai and
+ProtoNN algorithms in C++. Please see install/run instruction in the Readme
+pages within these directories. The `applications` directory has code/demonstrations
+of applications of the EdgeML algorithms.
+
+For details, please see our [wiki
+page](https://github.com/Microsoft/EdgeML/wiki/) and our ICML'17 publications
+on [Bonsai](docs/publications/Bonsai.pdf) and
+[ProtoNN](docs/publications/ProtoNN.pdf) algorithms, NIPS'18 publications on
+[EMI-RNN](docs/publications/emi-rnn-preprint-01.pdf) and
+[Fast(G)RNN](docs/publications/FastGRNN.pdf).  
 
-For details, please see our [wiki page](https://github.com/Microsoft/EdgeML/wiki/) and our ICML'17 publications on [Bonsai](docs/publications/Bonsai.pdf) and [ProtoNN](docs/publications/ProtoNN.pdf) algorithms, NIPS'18 publications on [EMI-RNN](docs/publications/EMI-RNN.pdf) and [Fast(G)RNN](docs/publications/FastGRNN.pdf).  
 
 Core Contributors:
   - [Aditya Kusupati](https://adityakusupati.github.io/)
@@ -23,10 +44,18 @@ Core Contributors:
   - [Don Dennis](https://dkdennis.xyz)
   - [Harsha Vardhan Simhadri](http://harsha-simhadri.org)
 
-We welcome contributions, comments, and criticism. For questions, please [email Harsha](mailto:harshasi@microsoft.com).
+We welcome contributions, comments, and criticism. For questions, please [email
+Harsha](mailto:harshasi@microsoft.com).
 
-[People](https://github.com/Microsoft/EdgeML/wiki/People/) who have contributed to this [project](https://www.microsoft.com/en-us/research/project/resource-efficient-ml-for-the-edge-and-endpoint-iot-devices/).
+[People](https://github.com/Microsoft/EdgeML/wiki/People/) who have contributed
+to this
+[project](https://www.microsoft.com/en-us/research/project/resource-efficient-ml-for-the-edge-and-endpoint-iot-devices/).
 
 
-### Microsoft Open Source Code of Conduct
-This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.
+### Microsoft Open Source Code of Conduct This project has adopted the
+[Microsoft Open Source Code of
+Conduct](https://opensource.microsoft.com/codeofconduct/). For more information
+see the [Code of Conduct
+FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or contact
+[opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional
+questions or comments.

cpp/run_ProtoNNPredict_usps10.sh

@@ -6,8 +6,8 @@
 
 
 test_file="-I usps10/test.txt"
-model_file="-M usps10/ProtoNNResults/ProtoNNTrainer_pd_15_protPerClass_0_prot_200_spW_1.000000_spZ_1.000000_spB_1.000000_gammaNumer_1.000000_normal_3_seed_42_bs_1024_it_2_ep_20/model"
-normalization_file="-n usps10/ProtoNNResults/ProtoNNTrainer_pd_15_protPerClass_0_prot_200_spW_1.000000_spZ_1.000000_spB_1.000000_gammaNumer_1.000000_normal_3_seed_42_bs_1024_it_2_ep_20/minMaxParams"
+model_file="-M usps10/ProtoNNResults/ProtoNNTrainer_pd_15_protPerClass_0_prot_200_spW_1.000000_spZ_1.000000_spB_1.000000_gammaNumer_1.000000_normal_3_seed_42_bs_1024_it_20_ep_20/model"
+normalization_file="-n usps10/ProtoNNResults/ProtoNNTrainer_pd_15_protPerClass_0_prot_200_spW_1.000000_spZ_1.000000_spB_1.000000_gammaNumer_1.000000_normal_3_seed_42_bs_1024_it_20_ep_20/minMaxParams"
 output_dir="-O usps10/ProtoNNResults"
 input_format="-F 0"
 ntest="-e 2007"

tf/README.md

@@ -5,11 +5,15 @@ algorithms developed as part of EdgeML. Currently, the following algorithms are
 available in Tensorflow:
 
 1. [Bonsai](../docs/publications/Bonsai.pdf)
-2. [EMI-RNN](../docs/publications/EMI-RNN.pdf)
+2. [EMI-RNN](../docs/publications/emi-rnn-preprint-01.pdf)
 3. [Fast(G)RNN](../docs/publications/FastGRNN.pdf)
 4. [ProtoNN](../docs/publications/ProtoNN.pdf)
 
-The TensorFlow compute graphs for these algoriths are packaged as `edgeml.graph`. Trainers for these algorithms are in `edgeml.trainer`. Usage directions and examples for these algorithms are provided in `examples` directory. To get started with any of the provided algorithms, please follow the notebooks in the the `examples` directory.
+The TensorFlow compute graphs for these algoriths are packaged as
+`edgeml.graph`. Trainers for these algorithms are in `edgeml.trainer`. Usage
+directions and examples for these algorithms are provided in `examples`
+directory. To get started with any of the provided algorithms, please follow
+the notebooks in the the `examples` directory.
 
 ## Dependencies