April 2020
Intermediate to advanced
816 pages
18h 32m
Chinese
Content preview from 精通機器學習
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多輸出分類
|
105
這段程式假設所有標籤的重要性一樣
，
但是事實不一定如此
，
例如
，
如果
Alice
的照片比
Bob
或
Charlie
的多
，
你可能會讓
Alice
照片的分類器分數有更多權重
。
有一種簡單的做法
是讓每一個標籤的權重等於它的
support（
也就是有該標籤的實例數量
）。
為此
，
你只要在
上面的程式中設定
average="weighted"
即可
4
。
多輸出分類
我們要討論的最後一種分類是
多輸出多類別分類
（
或簡稱
多輸出分類
）。
它只不過是更
廣泛的多標籤分類
，
各個標籤都可以有多個類別
（
也就是它的值可以超過兩個
）。
為了說明
，
我們來建立一個移除圖像雜訊的系統
。
它會接收一張有雜訊的數位圖像
，
並且
會
（
希望如此
）
輸出一張乾淨的數位圖像
，
圖像是用像素強度陣列來表示的
，
就像
MNIST
的圖片那樣
。
請注意
，
分類器的輸出是多標籤的
（
每個像素一個標籤
），
而且各個標籤可
以是多個值
（
像素強度的範圍是
0
到
255）。
因此它是個多輸出分類系統
。
分類與回歸之間的界限有時很模糊
，
就像這個範例
。
我們可以說
，
預測像素
強度比較像回歸
，
而不是分類
。
此外
，
多輸出系統並非只能進行分類任務
；
你甚至可以讓系統為每個實例輸出多個標籤
，
包括類別標籤與值標籤
。
我們先取得
MNIST
圖片
，
並且用
NumPy
的
randint()
函式將雜訊加到它們的像素強度
。
目標圖片是原始圖片
：
noise = np.random.randint(0, 100, (len(X_train), 784))
X_train_mod = X_train + noise
noise = np.random.randint(0, ...
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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.