MNIST dataset introduction

  MNIST dataset MNIST (Mixed National Institute of Standards and Technology) database is dataset for handwritten digits, distributed by Yann Lecun’s THE MNIST DATABASE of handwritten digits website. Wikipedia The dataset consists of pair, “handwritten digit image” and “label”. Digit ranges from 0 to 9, meaning 10 patterns in total. handwritten digit image: This is […]

Continue reading →

Why Chainer?

I will list up good points of Chainer as an opinion from one Chainer enthusiast. Features Easy environment setup Environment setup is easy, execute one command pip install chainer that’s all and easy. Some deep learning framework is written in C/C++ and requires to build by your own. It will take hours to only setup your develop […]

Continue reading →

Chainer basic module introduction 2

[Update 2017.06.11] Update This post is just a copy of chainer_module2.ipynb on github, you can execute interactively using jupyter notebook. Advanced memo is written as “Note”. You can skip reading this for the first time reading. In previous tutorial, we learned Variable Link Function Chain Let’s try training the model (Chain) in this tutorial.In this […]

Continue reading →

Chainer basic module introduction

[Update 2017.06.11] Add Chainer v2 code.   This post is just a copy of chainer_module1.ipynb on github, you can execute interactively using jupyter notebook. Advanced memo is written as “Note”. You can skip reading this for the first time reading.  In this tutorial, basic chainer modules are introduced and explained Variable Link Function Chain For other […]

Continue reading →

Chainer modules

    Comparing with caffe If you are using caffe, it is easy to get accustomed to chainer modules. Several variable’s functionality is similar and you can see below table for its correspondence. Chainer Caffe Comment datasets Data layers Input data can be formatted to this class for the model input.It covers most of the […]

Continue reading →