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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200
8
章 オートエンコーダハンズオン
8.12.3
ReLU
活性化関数を用いた
2
層ノイズ除去過完備オートエンコーダ
最後に、同じオートエンコーダの活性化関数を線形関数から
ReLU
に変更したらどうなるか見てみよ
う。元のデータセットに対しては、非線形活性化関数を持つオートエンコーダは、線形活性化関数の
ものよりも性能が良くなかったことを思い出そう。
model = Sequential()
model.add(Dense(units=40, activation='relu', \
activity_regularizer=regularizers.l1(10e-5), input_dim=29))
model.add(Dropout(0.05))
model.add(Dense(units=29, activation='relu'))
このオートエンコーダの損失を下に示す。また、図 8-11に、平均適合率、適合率
-
再現率曲線、
auROC
曲線を示す。
Train on 190820 samples, validate on 190820 samples
Epoch 1/10
190820/190820 [==============================] - 29s 153us/step - loss: 0.3049
- acc: 0.6454 - val_loss: 0.0841 - val_acc: 0.8873
Epoch 2/10
190820/190820 [==============================] ...
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