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node-tensorflow - Node.js + TensorFlow

  •    Javascript

TensorFlow is Google's machine learning runtime. It is implemented as C++ runtime, along with Python framework to support building a variety of models, especially neural networks for deep learning. It is interesting to be able to use TensorFlow in a node.js application using just JavaScript (or TypeScript if that's your preference). However, the Python functionality is vast (several ops, estimator implementations etc.) and continually expanding. Instead, it would be more practical to consider building Graphs and training models in Python, and then consuming those for runtime use-cases (like prediction or inference) in a pure node.js and Python-free deployment. This is what this node module enables.

sbnet - Sparse Blocks Networks

  •    Python

This repository releases code for our paper SBNet: Sparse Blocks Network for Fast Inference. Please refer to our blog post for more context. Installation was tested under Ubuntu 14.04 and 16.04 with TensorFlow 1.2, cuDNN 6.0 and cuDNN 5.0. Note that since by default Tensorflow 1.2 comes with cuDNN 5.0, we used a custom build to upgrade to a more recent version so we could compare with PyTorch implementation using the same version of cuDNN). Tensorflow 1.4 currently has a build issue with custom ops, so when compiling for 1.4 you may need to follow some suggestions from this thread.

cordova-plugin-tensorflow - On-device image recognition via TensorFlow/Inception

  •    Objective-C++

The plugin provides a TensorFlow class that can be used to initialize graphs and run the inference algorithm. To use a custom model, follow the steps to retrain the model and optimize it for mobile use. Put the .pb and .txt files in a HTTP-accessible zip file, which will be downloaded via the FileTransfer plugin. If you use the generic Inception model it will be downloaded from the TensorFlow website on first use.

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