stem/AI/Neural Networks/CNN/Convolutional Layer.md
andy 8f0b604256 vault backup: 2023-05-26 18:29:17
Affected files:
.obsidian/graph.json
.obsidian/workspace-mobile.json
.obsidian/workspace.json
STEM/AI/Neural Networks/Activation Functions.md
STEM/AI/Neural Networks/CNN/CNN.md
STEM/AI/Neural Networks/CNN/Convolutional Layer.md
STEM/AI/Neural Networks/CNN/Examples.md
STEM/AI/Neural Networks/CNN/GAN/CycleGAN.md
STEM/AI/Neural Networks/CNN/GAN/DC-GAN.md
STEM/AI/Neural Networks/CNN/GAN/GAN.md
STEM/AI/Neural Networks/CNN/GAN/StackGAN.md
STEM/AI/Neural Networks/CNN/GAN/cGAN.md
STEM/AI/Neural Networks/CNN/Inception Layer.md
STEM/AI/Neural Networks/CNN/Max Pooling.md
STEM/AI/Neural Networks/CNN/Normalisation.md
STEM/AI/Neural Networks/CV/Data Manipulations.md
STEM/AI/Neural Networks/CV/Datasets.md
STEM/AI/Neural Networks/CV/Filters.md
STEM/AI/Neural Networks/CV/Layer Structure.md
STEM/AI/Neural Networks/Weight Init.md
STEM/img/alexnet.png
STEM/img/cgan-example.png
STEM/img/cgan.png
STEM/img/cnn-cv-layer-arch.png
STEM/img/cnn-descriptor.png
STEM/img/cnn-normalisation.png
STEM/img/code-vector-math-for-control-results.png
STEM/img/cvmfc.png
STEM/img/cyclegan-results.png
STEM/img/cyclegan.png
STEM/img/data-aug.png
STEM/img/data-whitening.png
STEM/img/dc-gan.png
STEM/img/fine-tuning-freezing.png
STEM/img/gabor.png
STEM/img/gan-arch.png
STEM/img/gan-arch2.png
STEM/img/gan-results.png
STEM/img/gan-training-discriminator.png
STEM/img/gan-training-generator.png
STEM/img/googlenet-auxilliary-loss.png
STEM/img/googlenet-inception.png
STEM/img/googlenet.png
STEM/img/icv-pos-neg-examples.png
STEM/img/icv-results.png
STEM/img/inception-layer-arch.png
STEM/img/inception-layer-effect.png
STEM/img/lenet-1989.png
STEM/img/lenet-1998.png
STEM/img/max-pooling.png
STEM/img/stackgan-results.png
STEM/img/stackgan.png
STEM/img/under-over-fitting.png
STEM/img/vgg-arch.png
STEM/img/vgg-spec.png
STEM/img/word2vec.png
2023-05-26 18:29:17 +01:00

553 B

Design Parameters

  • Size of input image
    • 256 x 256 x 1
    • Towards top end of supportable
  • Padding
    • Thickness of border 0s
  • Kernel size
    • 7 x 7 x 1 x n
      • N is for multiple filters per layer
    • Main design decision
      • 12 x 12/15 x 15 in early layers
      • Lower in later filters
      • Dataset-dependent
  • Stride
    • Interval to sample
    • 1
      • Every subsequent pixel
      • Same size out as in
    • 2
      • Every other subsequent pixel
      • Out image is half input size
  • Size of computable output
    • 252 x 252 x 1 x n
      • Depends on padding and striding