Google ColaboratoryのLinuxコマンドに慣れる
「Google Colaboratory」はLinuxサーバです。ファイル操作がCLI(コマンド操作)のため、Windows環境などのGUI操作の経験しかないと戸惑います。まずは、簡単なLinuxコマンドから始め、徐々に慣れていきましょう。ここではコマンドの説明ではなく、あるタスクに取り組むことによって、慣れていくことを目指します。Google Colaboratoryには、プリインストールされたMNISTデータセット(手書き数字)があります。コマンドベースでファイル操作を行いながら、MNISTを画面上に表示する手順になっています。それでは、Linux上でのファイル操作を試してみましょう。
AI開発環境(Google Colab)
OSコマンドとPythonコードの区別をつけます
MNISTデータを見る
「Google Colaboratory」にはプリインストールされたMNISTデータがあります。画像表示する手順を実行しながら、ディレクトリ操作を覚えます。
- CSVファイルが入っているディレクトリを確認する
(ファイル名は「mnist_train_small.csv」です) - そのCSVファイルがあるディレクトリまで移動する
- もう一度CSVファイルがあるか確認する
- ファイルの生データを見てみる
- MNISTデータを画像表示する
1.CSVファイルが入っているディレクトリを確認する
◆カレントディレクトリを確認する
ls -l
total 4
drwxr-xr-x 1 root root 4096 Nov 18 14:36 sample_data
--
「sample_data」ディレクトリのみ存在している
◆「sample_data」ディレクトリ配下を確認する
(引数にディレクトリ名を指定)
ls -l sample_data
total 55504
-rwxr-xr-x 1 root root 1697 Jan 1 2000 anscombe.json
-rw-r--r-- 1 root root 301141 Nov 18 14:36 california_housing_test.csv
-rw-r--r-- 1 root root 1706430 Nov 18 14:36 california_housing_train.csv
-rw-r--r-- 1 root root 18289443 Nov 18 14:36 mnist_test.csv
-rw-r--r-- 1 root root 36523880 Nov 18 14:36 mnist_train_small.csv
-rwxr-xr-x 1 root root 930 Jan 1 2000 README.md
--
「mnist_train_small.csv」ファイルがある
2.そのCSVファイルがあるディレクトリまで移動する
◆「sample_data」ディレクトリに移動する
%cd sample_data
/content/sample_data
--
「/content/sample_data」ディレクトリに移動した
3.もう一度CSVファイルがあるか確認する
◆カレントディレクトリの確認
(移動したので、引数なしで確認)
ls -l
total 55504
-rwxr-xr-x 1 root root 1697 Jan 1 2000 anscombe.json
-rw-r--r-- 1 root root 301141 Nov 18 14:36 california_housing_test.csv
-rw-r--r-- 1 root root 1706430 Nov 18 14:36 california_housing_train.csv
-rw-r--r-- 1 root root 18289443 Nov 18 14:36 mnist_test.csv
-rw-r--r-- 1 root root 36523880 Nov 18 14:36 mnist_train_small.csv
-rwxr-xr-x 1 root root 930 Jan 1 2000 README.md
--
ディレクトリ移動完了
4.ファイルの生データを見てみる
◆先頭5行のみ確認する
cat mnist_train_small.csv|head -5
6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,24,67,67,18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,131,252,252,66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,159,250,232,30,32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15,222,252,108,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,147,252,183,5,0,0,0,0,0,0,0,20,89,89,73,0,0,0,0,0,0,0,0,0,0,0,0,48,247,252,159,0,0,0,0,0,0,0,79,236,252,252,249,198,16,0,0,0,0,0,0,0,0,0,41,193,252,199,22,0,0,0,0,0,12,135,248,252,252,252,252,252,100,0,0,0,0,0,0,0,0,0,100,252,252,88,0,0,0,0,0,11,171,252,252,235,175,178,252,252,224,0,0,0,0,0,0,0,0,15,209,252,233,12,0,0,0,0,49,177,252,252,89,26,0,2,166,252,252,0,0,0,0,0,0,0,0,96,253,253,59,0,0,0,0,11,177,255,253,92,0,0,0,0,155,253,128,0,0,0,0,0,0,0,0,143,252,252,10,0,0,0,12,171,252,216,110,13,0,0,0,3,180,232,24,0,0,0,0,0,0,0,0,143,252,170,2,0,0,0,135,252,209,19,0,0,0,0,0,12,252,132,0,0,0,0,0,0,0,0,0,249,252,96,0,0,0,21,248,246,34,0,0,0,0,5,61,234,152,3,0,0,0,0,0,0,0,0,0,253,252,44,0,0,0,145,252,104,0,0,0,46,114,184,252,149,34,0,0,0,0,0,0,0,0,0,0,253,252,82,0,0,31,239,252,66,39,89,165,243,252,233,126,5,0,0,0,0,0,0,0,0,0,0,0,249,252,244,126,98,143,252,252,237,240,253,252,243,174,17,0,0,0,0,0,0,0,0,0,0,0,0,0,119,239,252,252,252,252,252,252,252,252,228,179,17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,46,66,66,66,66,66,66,66,66,29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
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5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,206,210,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,47,179,248,239,122,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,78,192,237,237,206,237,249,254,239,123,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,48,229,254,254,235,254,254,174,114,26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,155,254,132,35,23,35,35,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,232,232,23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,131,254,140,0,12,63,51,27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,149,254,126,61,172,254,254,254,166,82,14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,149,254,230,254,250,208,160,245,251,254,237,135,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,149,254,254,241,92,0,0,0,52,133,248,254,225,46,0,0,0,0,0,0,0,0,0,0,0,0,0,0,149,255,241,61,0,0,0,0,0,0,22,153,254,242,89,0,0,0,0,0,0,0,0,0,0,0,0,0,26,127,49,0,0,0,0,0,0,0,0,20,153,254,227,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,19,228,252,57,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,211,254,61,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,211,254,61,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,49,246,241,32,0,0,0,0,0,0,0,0,0,0,0,0,0,33,159,105,0,0,0,0,0,0,0,42,217,254,179,0,0,0,0,0,0,0,0,0,0,0,0,0,26,218,254,71,0,0,0,0,0,18,122,248,254,215,13,0,0,0,0,0,0,0,0,0,0,0,0,0,39,241,254,208,97,91,43,128,185,251,254,252,184,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,93,242,254,254,254,254,254,254,254,202,71,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
--
先頭列は画像のアノテーション(どの数字か)
2列目以降が画像の生データ
2行目「5」を次項で表示します
5.MNISTデータを画像表示する
◆CSVファイルを開き、データを読み込む
pandas、matplotlib…機械学習ライブラリ
4行目…CSVファイル読み込み
5行目…サイズ確認
import pandas as pd
import matplotlib.pyplot as plt
data = pd.read_csv('./mnist_train_small.csv', header=None)
data.shape
(20000, 785)
--
20,000行、1+(28*28)=785列
◆CSVデータの2行目にある「5」を画像表示する
1行目…イメージデータに変換(正方形にする=1×784→28×28)
plt(matplotlib)に送り込んで表示
im = data.iloc[1,1:].to_numpy().reshape(28,28)
plt.imshow(im)
plt.show()

◆元のディレクトリに戻る
%cd -
/content
--
初期ディレクトリの「/content」に戻っていることを確認
その他に、現在のディレクトリを表示する「pwd」、一つ上のディレクトリに移動する「cd ../」などもあります。CSV読み込みに登場する「pandas」については、 『StratifiedKFold(sklearn)サンプルコードでイメージを掴む』 に使用例を載せています。
ファイル操作する
上記でディレクトリ操作のイメージがつかめたと思います。次はファイル操作をやってみます。
ここからはセルに複数のOSコマンドを入力するため、コマンド先頭に「!」を付けます。
1.「Googleドライブ」にファイルをアップロードする
◆例として次のファイルを作成してアップロード
| ファイル名 | a.txt |
| 内容 | Hello World!! |
| アップロード先 | 「Googleドライブ」の最上位フォルダ |
2.「Googleドライブ」をマウントする
◆「Googleドライブ」をマウントする
from google.colab import drive
drive.mount('/content/drive')
Mounted at /content/drive
--
ユーザ認証後、マウントされたメッセージが出力された
3.「Googleドライブ」のファイルを確認する
◆「a.txt」があることを確認する
!ls -l /content/drive/MyDrive/a.txt
!cat /content/drive/MyDrive/a.txt
-rw------- 1 root root 13 Dec 5 09:10 /content/drive/MyDrive/a.txt
Hello World!!
--
アップロードされたファイルがあり、「Hello World!!」と書かている
4.「Google Colaboratory」にコピーする
◆「Googleドライブ」のファイルを「Google Colaboratory」のカレントディレクトリにコピーする
!cp -p /content/drive/MyDrive/a.txt ./
!ls -l
total 12
-rw------- 1 root root 13 Dec 5 09:10 a.txt
drwx------ 5 root root 4096 Dec 5 09:11 drive
drwxr-xr-x 1 root root 4096 Nov 18 14:36 sample_data
--
コピーした「a.txt」がある
5.ディレクトリを作成する
◆「test」の名前でディレクトリを作成
!mkdir test
!ls -l
total 16
-rw------- 1 root root 13 Dec 5 09:10 a.txt
drwx------ 5 root root 4096 Dec 5 09:11 drive
drwxr-xr-x 1 root root 4096 Nov 18 14:36 sample_data
drwxr-xr-x 2 root root 4096 Dec 5 09:12 test
--
ディレクトリ「test」ができた
6.作成したディレクトリにファイルを移動する
◆「a.txt」を「test」ディレクトリ内に移動
!mv a.txt test
!ls -l test
total 4
-rw------- 1 root root 13 Dec 5 09:10 a.txt
--
新規作成した空のディレクトリに「a.txt」が移動されていることを確認
◆「a.txt」を「test」ディレクトリ内に移動
!mv a.txt test
!ls -l test
total 4
-rw------- 1 root root 13 Dec 5 09:10 a.txt
--
新規作成した空のディレクトリに「a.txt」が移動されていることを確認
以上