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ncnn/benchmark/alexnet.param

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  1. 7767517

  2. 15 15

  3. Input                    data                     0 1 data 0=227 1=227 2=3

  4. Convolution              conv1                    1 1 data conv1_relu1 0=96 1=11 3=4 5=1 6=34848 9=1

  5. LRN                      norm1                    1 1 conv1_relu1 norm1 2=0.000100

  6. Pooling                  pool1                    1 1 norm1 pool1 1=3 2=2

  7. ConvolutionDepthWise     conv2                    1 1 pool1 conv2_relu2 0=256 1=5 4=2 5=1 6=307200 7=2 9=1

  8. LRN                      norm2                    1 1 conv2_relu2 norm2 2=0.000100

  9. Pooling                  pool2                    1 1 norm2 pool2 1=3 2=2

  10. Convolution              conv3                    1 1 pool2 conv3_relu3 0=384 1=3 4=1 5=1 6=884736 9=1

  11. ConvolutionDepthWise     conv4                    1 1 conv3_relu3 conv4_relu4 0=384 1=3 4=1 5=1 6=663552 7=2 9=1

  12. ConvolutionDepthWise     conv5                    1 1 conv4_relu4 conv5_relu5 0=256 1=3 4=1 5=1 6=442368 7=2 9=1

  13. Pooling                  pool5                    1 1 conv5_relu5 pool5 1=3 2=2

  14. InnerProduct             fc6                      1 1 pool5 fc6_drop6 0=4096 1=1 2=37748736 9=1

  15. InnerProduct             fc7                      1 1 fc6_drop6 fc7_drop7 0=4096 1=1 2=16777216 9=1

  16. InnerProduct             fc8                      1 1 fc7_drop7 fc8 0=1000 1=1 2=4096000

  17. Softmax                  prob                     1 1 fc8 output