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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8.12
ノイズ除去オートエンコーダ
195
noise_factor = 0.50
X_train_AE_noisy = X_train_AE.copy() + noise_factor * \
np.random.normal(loc=0.0, scale=1.0, size=X_train_AE.shape)
X_test_AE_noisy = X_test_AE.copy() + noise_factor * \
np.random.normal(loc=0.0, scale=1.0, size=X_test_AE.shape)
8.12
ノイズ除去オートエンコーダ
元の歪んでいないデータセットの場合と比べると、ノイズを含んだクレジットカード不正データセッ
トに過剰適合してしまった場合の害ははるかに大きい。データセットにノイズが十分に含まれているの
で、ノイズを含んだデータに過剰適合してしまうと、不正トランザクションを通常のトランザクション
と分離することが難しくなるからだ。
これは明らかだろう。われわれが必要としているのは、観測点の大半を再構成できる程度にはデー
タに適合しているが、間違ってノイズまでも再現してしまうほどには適合していないオートエンコーダ
だ。つまり、背後にある構造は学習するが、データ内のノイズは忘れるようなオートエンコーダが欲し
いのだ。
これまでに試してうまくいった方法をいくつか試してみよう。まず、線形活性化関数と
1
層
27
ノード
の隠れ層を持つ未完備オートエンコーダ、次に、
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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.
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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.