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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卷積神經網路（CNN）
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139
層與層之間連結方式的演變
另一個不同之處，就是卷積架構中層與層之間連結的方式。同一層的神經元只會與前一
層其中一小部分的神經元相連結。與傳統多層網路一樣的是，CNN 保留了以層為主的架
構，但層的型態卻有點不同。每一層都會使用某些可微分函數（可能有參數，也可能沒
有參數），把前一層的 3D 輸入立方體，轉換成神經元激活值 3D 輸出立方體，如圖 4-10
中所示。
輸入層
輸入層就是我們載入、儲存圖片原始輸入資料，以便在網路中進行處理之所在。這裡的
輸入資料必須指定寬度、高度以及通道的數量。由於每個像素都有 RGB 值，因此通道
的數量通常就是三。
圖 4-10 輸入層 3D 立方體
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