Chainer class introduction


Chainer is a library for deep learning. You can implement current trend network e.g. CNN (Convolutional Neural Network), RNN (Recurrent Neural Network) etc. Chainer official document * The post is written in 2016 July, with Chainer version 1.10, but Chainer is still in active development and some of the functionality specification may change in the future. Variable, functions, links and Chain At first, please read Introduction to Chainer. To summarize, input – output relationship of deep neural network is maintained by computational graph internally, which is constructed using Variable, functions, links and Chain. Once deep neural network is constructed, forward/backward propagation can be executed for training. VariableVariable will be used […]

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SeRanet: Quick start guide

This post explains SeRanet project, super resolution software through deep learning. Preparation Dependencies – third party library Install python, pip The software is written in python, and I’m using python version 2.7.6. If you are using OS Ubuntu 14.04, python2 is pre-installed by default. So you don’t need to install explicitly. The version of python can be checked by typing 

 in the terminal. If you can’t find python, then try below.

  To install third party python library, pip command is often used. To install pip, type below in command line

Install popular libraries, numpy, scipy, matplotlib numpy, scipy, matplotlib are widely used for data processing in python. […]

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Source code reading of waifu2x

Memo for self study. SRCNN – Super resolution by deep convolutional neural network Recently many application is developed using deep learning. waifu2x is a image super resolution application using convolutional neural network. The source code is open at github. It is developed using torch7, so the Lua programming language is used. I have never used Lua, but it is similar to python, so reading Lua source code was not so difficult without further study.  waifu2x supports upscaling (“scale”) and noise reduction (“noise”), but I will focus on scaling function here.  * Actually same CNN network architecture is used for upscaling, and noise reduction, they work in same way. Main difference is only training set used during training.  […]

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