011-lec-001. basic of cnn @ Suppose you have image(32*32*3) width*height*depth(rgb color) One element represents one value Suppose you have filter(5*5*3) Filter creates one number Color information will be processed in final step @ Imagine you have 5 input values $$$x_{1}, x_{2}, x_{3}, x_{4}, x_{5}$$$ And you use Wx+b formular to create 'one number' $$$\hat{y}=w_{1}x_{1}+w_{2}x_{2}+w_{3}x_{3}+w_{4}x_{4}+w_{5}x_{5}+b$$$ Here, in cnn, all weights in above formular are elements of filter @ You also can use relu function as activation function ReLu(Wx+b) @ You slide filter along with all other elements of image @ Then, think of how many numbers you can get from sliding filter Figuring out these numbers are important, when you define shape of weight, when you build model @ Suppose 7*7 image, 3*3 filter, 1 stride Shape of output is 5*5 Suppose 7*7 image, 3*3 filter, 2 stride Shape of output is 3*3 You can generalize above step 7: image 3: filter 2: stride 1: fixed constant ((7-3)/2)+1 @ As you perform sliding filter, image becomes smaller and smaller, which means you lose data of image To resolve this, you can use 'padding' when you use cnn Padding is putting 0 along with most outer area There are 2 benefits of padding 1. Padding prevents rapidly losing image data 1. Padding lets you to know most outer area of image @ 7*7 image, 3*3 filter, 1 stride, 1 pixel padding 7*7 image becomes 9*9 image ((9-3)/1)+1=7, therefore 7*7 This means your raw image is 7*7, and processed output image will become also 7*7 @ Sliding filter1 creates output1 Sliding filter2 creates output2 ... Sliding filter6 creates output6 If you add all outputs from above, shape of summed output will be (28,28,6) 28,28: using this formular ((9-3)/1)+1=7 6: number of filters, number of outputs