CNN (Convolutional Neural Networks) Abstract
CNN通常用來進行影象處理，經歷一系列卷積層,非線性層,池化層和完全連線層,最終得到輸出，輸出通常是一個單獨分類或者一組分類的概率.
Fully Connected Layer & Convolution Layer & Pooling Layer & Rectified Linear Unit
卷積層與全連線層的主要區別是卷積層保留了基本的空間結構(Spatial Structure)
全連線層(FC Layer)
Fully Connected
卷積層(Conv)
Conv通過filter輸出一個啟用對映(activation map) Conv經歷相較全連線來說主要有以下幾個特點:
- 區域性連線
- 保留空間結構
- 引數共享 (filter)
池化層(POOL)
同時也被叫做downsampling層．最受歡迎的就是最大池化層(max-pooling,如圖).
1. 減小輸入卷空間維度，也可以控制過擬合
2. 對於每個啟用層進行單獨處理
線性整流函式(ReLU)
在每個卷積層之後，通常會立即應用一個非線性層（或啟用層）。其目的是給一個在卷積層中剛經過線性計算操作（只是陣列元素依次（element wise）相乘與求和）的系統引入非線性特徵。通常意義下，線性整流函式指代數學中的斜坡函式，即
f(x) = max(0, x)
CNN Typical architectures
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