[PYTHON] Introduction to Deep Learning for the first time (Chainer) Japanese character recognition Chapter 1 [Environment construction]

Hello Licht. I got the ** Japanese character recognition data set ** sold at Environmental Research Institute, so for deep learning beginners using the data set I will publish the tutorial. We will try to develop a Japanese character recognition engine.

As you can see from the image below, it is a tutorial that guarantees the collapse of Gestaltzerfall, but I would like to do my best without fail. rihito.png

This article is ・ I want to start Deep Learning! ・ I want to do tutorials other than mnist number recognition! ・ I want to learn about Deep Learning related technologies! ・ I want to develop Japanese OCR by myself!

I am writing for those who say. This is explained in the outline below.

chapter title
Chapter 1 Building a Deep Learning environment based on chainer
Chapter 2 Creating a Deep Learning Predictive Model by Machine Learning
Chapter 3 Character recognition using a model
Chapter 4 Improvement of recognition accuracy by expanding data
Chapter 5 Introduction to neural networks and explanation of source code
Chapter 6 Improvement of learning efficiency by selecting Optimizer
Chapter 7 TTA,Improvement of learning efficiency by Batch Normalization

If you are completely new to Deep Learning, please try up to Chapter 4 because you want to see moving objects anyway. Chapters 5 and below are for those who want to know more about Deep Learning.

Introduction

Why chainer?

** chainer is domestic OSS **. Best of all, it's easy to use and understand, and even if you ask a question about chainer on Google Group, it will respond immediately for free.

environment

The main part is based on the assumption that it is a Mac, but I will explain each of them according to Windows at any time (although the only difference is the environment preparation). ・ Machine spec: Memory 4GB or more -Python2.7 series, pip must be installed

Environment preparation (Mac)

At the terminal

sudo pip install chainer

Enter chainer1.6.0, filelock2.0.5, nose1.3.7, numpy1.10.4, protobuf 2.6.1 in bulk.

sudo pip install scipy

To install scipy 0.17.0.

Also, please install Opencv 2.4.X series by referring to this article.

Environment preparation (Windows)

At the command prompt

pip install chainer

Enter chainer1.6.0, filelock2.0.5, nose1.3.7, numpy1.10.4, protobuf 2.6.1 in bulk.

pip install scipy

To install scipy 0.17.0. Start the command prompt in administrator mode if necessary. Also, please install Opencv 2.4.X series by referring to this article.

Data preparation (Mac, Windows)

Purchase (1000 yen) the Hiragana dataset from the Environmental Research Institute website and download it. Create a directory called "HIRAGANA_NN" on your desktop and unzip it.

-DESKTOP -HIRAGANA_NN -304a -304b ・ ・ (Reference) It is OK if it looks like the image below. desktop_directory.png

In addition, directories such as 304a show Unicode of each hiragana, and the contents are as follows. inside_folder.png

You are now ready. I would like to move on to machine learning from the next chapter 2!

chapter title
Chapter 1 Building a Deep Learning environment based on chainer
Chapter 2 Creating a Deep Learning Predictive Model by Machine Learning
Chapter 3 Character recognition using a model
Chapter 4 Improvement of recognition accuracy by expanding data
Chapter 5 Introduction to neural networks and explanation of source code
Chapter 6 Improvement of learning efficiency by selecting Optimizer
Chapter 7 TTA,Improvement of learning efficiency by Batch Normalization

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