April 2020
Intermediate to advanced
344 pages
8h 22m
Japanese
Content preview from Pythonではじめる教師なし学習 ―機械学習の可能性を広げるラベルなしデータの利用
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172
8
章 オートエンコーダハンズオン
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic: Area under the \
curve = {0:0.2f}'.format(areaUnderROC))
plt.legend(loc="lower right")
plt.show()
if returnPreds==True:
return preds
8.2
オートエンコーダの構成要素
まず、入力層、
1
層の隠れ層、出力層で構成される、簡単なオートエンコーダを作ってみよう。元の
特徴量行列
x
をオートエンコーダに入力する。これが入力層となる。次に、入力層に重み付けして加算
したものに活性化関数を適用し、隠れ層を作る。重み付け加算と活性化関数が、オートエンコーダの
エンコーダ部分を構成する。これを
f
とする。隠れ層は
h
と書く。隠れ層の値は
f(x)
に等しく、新しく
学習した表現を表す。
次に、重み付け加算と活性化関数を隠れ層（すなわち新たに学習した表現）に対して適用して、元の
観測点を再構成する。この重み付け加算と活性化関数を
g
と呼び、これがオートエンコーダのデコーダ
部分となる。出力層は
r
と書き、
g(h)
と等しく、新たに再構成された観測点を表す。新たに再構成され
た観測点
r
と元の
x
とを比較して再構成誤差を計算する。
8.3
活性化関数
この隠れ層
1
層のオートエンコーダで用いるノード数を決める前に、 ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.