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 presenatations smaller and more manageable
    • operates over each activation map indendently
  • Max pooling
    • 4x4 -> 2x2 filter:2 stride:2
    • max pooling: get the biggest number
    • trend not to use it
    • polling -> only to reduce the computation?
  • small stride first, large stride later

  • Pooling Layer
  • 앙상블,
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