4Convolutional Neural Networks

4.1Edge detection example

kernel = filter

CNN的计算过程：

计算元素积

将权重矩阵中的每一个元素和6x6的矩阵中的元素做乘积，可以得到4x4矩阵中的目标值，比如第一个乘积是

3x1 + 0 + (-1x1) + 1x1 + 0x5 + (-1x8) + 2x1 + 0 + (-1x2) = -5, so the first block of the 4x4 matrix is -5

4.2more edge detection

horizontal edge detection and vertical detection

sobel filter and scharr filter as well as the self-definded filter w1-w9

4.3padding

use 0 to pad the extra blanks

n + 2p - f + 1

n is the length/width of the original matrix

p is the extra blanks on the single edge

f is the length/width of the kernel

according to this equation, the size of final width / length of the matrix can be computed.

in order to keep input size same as the output size, p and f should be well computed.

common kernel size like 1x1,3x3,5x5,7x7

4.4stride convolutions

for the summary of convolutions

such as there is an image of 7x7, and the stride is 2, the filter is 3x3, the padding is 0

so (7+2x0-3)/2+1=3, the final matrix is 3x3

4.5convolutions over volume

coursera4 Deeplearning

原文：https://www.cnblogs.com/yuyukun/p/12683137.html