April 2020
Intermediate to advanced
816 pages
18h 32m
Chinese
Content preview from 精通機器學習
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用正則化來避免過擬
|
359
•
其次
，
作者介紹了一種稱為
MC Dropout
的強大技術
，
它可以提升任何訓練後的
dropout
模型的效果
，
且完全不需要重新訓練它
，
甚至修改它
，
為模型的不確定性提供
更好的衡量標準
，
也令人驚訝地容易實作
。
如果你覺得上面的說法看起來像
「
怪招
（one weird trick）」
廣告
，
看一下接下來的程式
。
它是完整的
MC Dropout
實作
，
可改善我們之前訓練好的
dropout
模型
，
而且不需要再次
訓練它
：
y_probas = np.stack([model(X_test_scaled, training=True)
for
sample
in
range(100)])
y_proba = y_probas.mean(axis=0)
我們只對測試組進行
100
次預測
，
設定
training=True
來確保
Dropout
層是活躍的
。
因為
dropout
是活躍的
，
所有的預測都會不同
。
之前談過
，
predict()
會回傳一個矩陣
，
在裡
面每一個實例有一列
，
每個類別有一行
。
因為測試組有
10,000
個實例
，
以及
10
個類別
，
這個矩陣的外形是
[10000, 10]。
我們把
100
個這種矩陣疊在一起
，
所以
y_probas
是個外
形為
[100, 10000, 10]
的陣列
。
計算第一維
（
axis=0
）
的平均值可以得到
y_proba
，
它是
外形為
[10000, 10]
的陣列
，
就像我們進行一次預測之後得到的東西
。
全部就這樣
！
啟動
dropout，
並計算多個預測結果的平均值可以得到一個
Monte Carlo ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
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.