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Would you like to build/train a model using Keras/Python? And would you like to run the prediction (forward pass) on your model in C++ without linking your application against TensorFlow? Then frugally-deep is exactly for you. frugally-deep * is a small header-only library written in modern and pure C++. * is very easy to integrate and use. * depends only on FunctionalPlus, Eigen and json - also header-only libraries. * supports inference (model.predict) not only for sequential models but also for computational graphs with a more complex topology, created with the functional API. * re-implements a (small) subset of TensorFlow, i.e., the operations needed to support prediction. * results in a much smaller binary size than linking against TensorFlow. * works out-of-the-box also when compiled into a 32-bit executable. (Of course, 64 bit is fine too.) * avoids temporarily allocating (potentially large chunks of) additional RAM during convolutions (by not materializing the im2col input matrix). * utterly ignores even the most powerful GPU in your system and uses only one CPU core per prediction. ;-) * but is quite fast on one CPU core, and you can run multiple predictions in parallel, thus utilizing as many CPUs as you like to improve the overall prediction throughput of your application/pipeline.
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