Deep Learning Study Ch#04

CNN : NN인데 image에 특화했다.

back propagation

convnet -> conveolutional layer, relu, pooling layer, fully connected layer

image가 가진 특징만 뽑아?

trend towards smaller filters and deeper architectures

trend towards getting rid of POOL/FC layers

conv layer and pool layer

set of pixel을 가지고 weight를 준다

mpeg, jpeg 압축알고리즘을 보면 화면을 블럭단위로 계산하니까

마찬가지로 정한 pixel의 값이 크게 바뀌지 않으니까. 그걸로 optimize하자

regular 3-layer Neural network vs ConvNet

Convolution Layer

image 32x32x3 image, -> 5x5x3 filter

convolve the filter with the image

3d: width height depth

32x32x3 image, 5x5x3 filter w

W^Tx _ b

activation map: convolve(slide) over all spatial locations

number of filter is also hyperparameter

stack up activation maps

32x32x3 image -> 5x5x3 filter -> 28x28 activation map

6 filters produce 6 independet activation map

example 7x7 image -> 3x3 filter

5x5 output

hyper parameter: size of filter, stride,

7x7 input, 3x3 filter, 2 stride,

if stride 3, won’t fit -> cannot apply 3x3 filter on 7x7 input

0 padding ->

which filter get?

fitler도 learning의 대상, Filter가 Weight값이다.

Random initialze하고 back propagation해서 learn한다

data preprocessing도 learning한다.

example of learning (N-F)/stride + 1

w1h1d1 -> F -> w2h2d2

fx, sx, px - filter, stride, zeropadding

Pooling Layer

• make the presentations smaller and more manageable
• operates over each activation map independently

Max pooling

• 4x4 -> 2x2 filter:2 stride:2
• max pooling: get the biggest number
• trend not to use it
• pooling -> only to reduce the computation?

small stride first, large stride later

Pooling Layer

앙상블,