April 2020
Intermediate to advanced
816 pages
18h 32m
Chinese
Content preview from 精通機器學習
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自製模型和訓練演算法
|
379
Keras
會在訓練期間為各個批次計算這個評量標準
，
並且從
epoch
開始時就追蹤它的均
值
。
雖然通常你希望它幫你做這些事情
，
但有時並非如此
！
例如
，
考慮一個二元分類器的
precision。
第
3
章說過
，precision
是
true positive
的數量除以陽性
（positive）
預測的數量
（
包括
true positive
和
false positive）。
如果模型在第一批次做了五個陽性預測
，
其中的四
個是正確的
，
它的
precision
就是
80%。
如果模型在第二批次做了三個陽性預測
，
但它們都
是錯的
，
第二批次的
precision
就是
0%。
如果你直接計算這兩個
precision
的均值
，
你會得
到
40%，
但是它
不是
模型處理這兩個批次時的
precision。
事實上
，
在八個陽性預測中
（5
+ 3）
總共有四個
true positive（4 + 0），
所以整體的
precision
是
50%，
不是
40%。
我們需
要一個可以追蹤
true positive
數量以及
false positive
數量
，
並且在你請求時可以計算它們
的比率的物件
，
這恰恰是
keras.metrics.Precision
類別的功能
：
>>>
precision = keras.metrics.Precision()
>>>
precision([0, 1, 1, 1, 0, 1, 0, 1], [1, 1, 0, 1, 0, 1, 0, 1])
<tf.Tensor: id=581729, shape=(), dtype=float32, ...
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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.