Solving XOR problem using tiny-dnn

REVISED August 13th 2017

Almost all mature deep neural network (DNN) libraries e.g. Tensor Flow, Theano, Caffe, and etc are written in python, not in C/C++. We will hardly find library for DNN written in C/C++. Even if we find one, it requires heavy resources. Fortunately, we now have tiny-dnn. tiny-dnn is a C++11 implementation of deep learning. Nothing needs to be compiled, header only. It is suitable for deep learning on limited computational resource, embedded systems and IoT devices.

For new tiny-dnn user, it may hard to get used with the environment because the examples provided are directly designated to solve MNIST or CIFAR problem. On this post, I try to give example to solve simple problem (XOR) using tiny-dnn. It may sound excessive to use DNN framework only to solve XOR problem,. But for the sake of better understanding of framework structure, I think it’s okay to do so.

Installing IDLAK Deep Neural Network Text to Speech Synthesizer

I was on duty to create text-to-speech (TTS) engine. After researching about the latest technology, the state-of-art of TTS had come to Deep Neural Network (DNN) scheme, instead of insisting on Hidden Markov Model (HMM). The main reason of migrating the scheme in to DNN was just the powerfullness of it.

One of the framework of doing DNN TTS is IDLAK. It was fresh from the oven. Their team had just presented their paper on Interspeech 2016. IDLAK is branch of well-known automatic-speech-recognizer (ASR) engine KALDI.

Here I am documenting the whole steps building the engine.