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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第四章：深度網路的主要架構
網路資料表達方式的大小，並協助控制過度套入的情況。池化層會針對輸入的每個深度
切片獨立進行操作。
常見的降採樣（
Downsampling
）操作
最常見的池化降採樣操作方式，就是「
取最大值
」。另一種常見的操作方
式，則是「
取平均
」。
如果池化層使用
max()
操作，在空間維度（寬度與高度）上調整輸入資料的大小，這種
操作方式就叫做「
最大池化
（
max pooling
）」。比方說，假設過濾器大小為 2 × 2，
max()
操作就是取過濾器區域中四個數字裡最大的那一個。這個操作並不會影響到深度維度。
池化層會使用一些過濾器，對輸入立方體執行降採樣操作。過濾器會沿著輸入資料的空
間維度執行降採樣操作。比方說，如果輸入圖片的寬度和高度是 32 × 32 像素，輸出圖
片的寬度和高度都會變得比較小一點（例如 16 × 16 像素）。池化層最常見的設定方式，
就是套用步幅為 2 的 2 × 2 過濾器。這樣一來就會在空間維度（寬度和高度）上，把輸
入立方體中的每個深度切片降採樣兩倍。這種降採樣操作，會導致激活值少掉 75％。
雖然池化層並沒有與層相關的參數，但確實另有額外的超參數。之所以沒有與層相關的
參數，是因為它計算的是輸入立方體的固定函數。在池化層中使用零填充的做法，也並
不常見。
全連結層
我們使用此層來計算類別分數，然後把類別分數當成整個網路的輸出（也就是整個網路
最末端的輸出層）。輸出立方體的尺寸是 [1 × 1 × N]，其中 N 是我們所要輸出的類別數
量。若以之前討論過的
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