January 2019
Beginner to intermediate
576 pages
14h 31m
Chinese
Content preview from 深度學習｜內行人的做法
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遞迴神經網路（RNN）
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評估與評分時遮罩的作用
我們在對 RNN 進行評分與評估時（也就是當我們在評估模型的正確率時），遮罩陣列也
有一定的作用。在多對一的情況下，每個樣本都只會有單一個輸出，因此評估時必須考
慮到這一點。
使用 Evaluation 物件類別
在評估過程中，我們會把輸出遮罩陣列傳遞給
Evaluation
物件，如範例 7-3 所示。
範例
7-3
Evaluation
的設定
Evaluation.evalTimeSeries(INDArray labels, INDArray predicted, INDArray outputMask)
表 7-4 針對範例 7-3 其中的每個變數提供了相應的說明。
表 7-4 變數說明
變數 說明 維度
labels 訓練資料的實際標籤 3 維
predicted 網路所生成的標籤 3 維
outputMask 輸出的遮罩陣列 2 維
我們可以注意到，輸入遮罩陣列對於評估來說並不是必須的，因為在評估時只關心網路
的輸出。
用 MultiLayerNetwork 對新資料進行評分
我們在計算模型的分數時，遮罩陣列也會對分數造成一定的影響。這裡所計算的分數，
與我們之前所看過的並不相同。我們在這裡所計算的是損失函數的值，而不是像正確
率或 F1 這類的衡量值。但不管是採用哪一種衡量方式，我們都希望分數所評估的是那
些實際存在的時間步驟，而不是那些填充（原本不存在）的時間步驟。我們可以使用
MultiLayerNetwork
這個物件類別，來對時間序列資料進行評分，相應的程式碼如下： ...
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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.