Giter Site home page Giter Site logo

keras-docs-ja's Introduction

Japanese translation of the Keras documentation

This is the repository for the translated .md sources files of keras.io. The translation project.


Keras documentationの日本語訳化

翻訳ガイドライン

  • 翻訳対象は本文とコード中のコメント
  • 本文は敬体(です・ます調)
  • 句読点は「,.」を用いる
  • 引用符(',")は基本的にそのまま
  • 記号「,.()?!:;」は全角
  • 文中のシンタックスハイライト(syntax highlight)の前後に空白は入れない.
  • 用語の訳は対訳表に従う.

※ 翻訳は英語から日本語へのただの変換作業ではなく,英文の意味を読み取り,日本語として表現する創作作業です. 英語の言い回しに引きずられることなく自然な日本語で表現しましょう.


対訳表

  • 構文キーワードなどはそのまま英語表記とする.
  • 検索性のため,python/numpy/keras特有の単語はそのまま英語表記とする.
English 日本語
arguments 引数
boolean 真理値
data augumentation データ拡張
deep learning 深層学習
float 浮動小数点数
Functional API Functional API
Fuzz factor 微小量
input shape 入力のshape
index インデックス
int 整数
layer レイヤー
loss function 損失関数
metrics 評価関数(値)
nD tensor n階テンソル
Numpy Array Numpy 配列
objective 目的関数
optimizer 最適化(アルゴリズム)
output shape 出力のshape
regularizer 正則化
return 戻り値
recurrent recurrent
See something ~~を参照
Sequential Model Sequentialモデル
shape shape
str 文字列
target ターゲット
testing テスト
training 訓練
1--9 1--9

keras-docs-ja's People

Contributors

7680x4320 avatar aeroastro avatar amaotone avatar aotree avatar contaconta avatar cympfh avatar dumble009 avatar equiv avatar fchollet avatar henry0312 avatar hex4d avatar kouml avatar kshina76 avatar masstomato avatar miyamotok0105 avatar mtjuney avatar nannoki avatar naoyashiga avatar nzw0301 avatar odanado avatar saitouena avatar tomato27 avatar tomo-makes avatar ujitoko avatar wakamatz avatar wakame1367 avatar whitphx avatar xolmon avatar ynqa avatar yoshikig avatar

Stargazers

 avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar

Watchers

 avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar

keras-docs-ja's Issues

which axis to concatenate when setting is channel first and channel last

The Keras document onConcatenate said concatenate(axis = -1). Does it work the same for both condition when channel = first and channel = last ?

For example I have three dataset with these shape [None,1,16,8,8], [None,1,16,8,8] and [None,1,16,8,8](batch_size,timestep,channel,height,width)
Above setting channel is first

The same dataset with different channel position [None,1,8,8,16], [None,1,8,8,16] and [None,1,8,8,16](batch_size,timestep,height,width,channel)
this setting channel is last

Now Keras document said concatenate more than two tensors instruct this way
keras.layers.concatenate(axis=-1)

Search does not work

Search function doesn't work. It works in English doc site.
Results are empty anytime.

Where can I fix this problem?

I guess the following error in browser is one of the causes.

[ERROR] GET https://keras.io/ja/mkdocs/js/require.js

I'd appreciate your help.

Unification of Japanses terminology

I want to unify terminology for more useful docs.

I found some different written forms

  • Numpy Array -> Numpy配列
  • . -> .
  • , -> ,
  • : -> :
  • ; -> ;
  • ( ) -> ()
  • str -> 文字列
  • boolean -> 真理値
  • int -> 整数
  • float -> 浮動小数点数
  • optimizer -> 最適化(アルゴリズム)
  • Arguments -> 引数
  • input shape -> 入力のshape
  • output shape -> 出力のshape
  • Return -> 戻り値
  • Sequential Model -> Sequentialモデル
  • Functional API -> Functional API
  • Recurrent -> Recurrent
  • metrics -> 評価値
  • EOS -> です/ます
  • See something -> ~~を参照
  • whether insert space or not before/after syntax highlight in sentence -> No
  • Data augmentation -> データ拡張
  • objective -> 目的関数
  • loss function -> 損失関数
  • training -> 学習
  • testing -> テスト
  • validation -> 検証
  • index -> インデックス
  • target -> ターゲット
  • layer -> レイヤー
  • shape -> shape
  • numbers (1 or 一) -> 1
  • regularizer -> 正則化
  • nD tensor -> n階テンソル
  • Fuzz factor -> 微小量

For example, shape is translated to "形状", "形" and "型" for now.

I think that translation to follow Japanses Python docs is easy for built-in.

thanks.

見出しに対するページ内リンクの修正方法

やりたいこと

ページ内リンクのリンク先が間違っている箇所があったので、それを修正したいです。
https://keras.io/ja/getting-started/faq/ の「中間レイヤーの出力を得るには?」のリンク先が別の見出しになっていました。
(間違っているリンクは他にもある)

• 誤: "#_1"
• 正: "#_7"

質問

リンク先の修正は、次のように- [中間レイヤーの出力を得るには?](#_7)に書き換えるだけで良いでしょうか?
https://raw.githubusercontent.com/keras-team/keras-docs-ja/master/sources/getting-started/faq.md

「中間レイヤーの出力を得るには?」の見出しのid属性値"_7"が、どこで設定されているのかが分からなかったので、他にも修正すべき箇所があるかもしれないと考えています。

Translation for keras 2, PR freeze

We are currently working for translation of keras 2, so we want not to send this master repository.
To contribute, please send PR to here.

  • I will send a PR to master branch after translation for keras 2 completes, since I want to decrease cost of PR's review for @fchollet
  • I will prioritize the finish of translation for keras 2. (: not including #40 )

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.