SHARE
TWEET

Untitled

a guest Aug 24th, 2017 51 Never
Not a member of Pastebin yet? Sign Up, it unlocks many cool features!
  1. name: "resnet_cifar10"
  2. layer {
  3.   name: "Data1"
  4.   type: "Data"
  5.   top: "Data1"
  6.   top: "Data2"
  7.   include {
  8.     phase: TRAIN
  9.   }
  10.   transform_param {
  11.     mean_file: "examples/xor/mean.binaryproto"
  12.     crop_size: 28
  13.     mirror:true
  14.   }
  15.   data_param {
  16.     source: "examples/xor/cifar10_train_lmdb"
  17.     batch_size: 100
  18.     backend: LMDB
  19.   }
  20. }
  21. layer {
  22.   name: "Data1"
  23.   type: "Data"
  24.   top: "Data1"
  25.   top: "Data2"
  26.   include {
  27.     phase: TEST
  28.   }
  29.   transform_param {
  30.     mean_file: "examples/xor/mean.binaryproto"
  31.   }
  32.   data_param {
  33.     source: "examples/xor/cifar10_test_lmdb"
  34.     batch_size: 100
  35.     backend: LMDB
  36.   }
  37. }
  38. layer {
  39.   name: "Convolution1"
  40.   type: "Convolution"
  41.   bottom: "Data1"
  42.   top: "Convolution1"
  43.   param {
  44.     lr_mult: 1
  45.     decay_mult: 1
  46.   }
  47.   param {
  48.     lr_mult: 2
  49.     decay_mult: 0
  50.   }
  51.   convolution_param {
  52.     num_output: 16
  53.     pad: 1
  54.     kernel_size: 3
  55.     stride: 1
  56.     weight_filler {
  57.       type: "gaussian"
  58.       std: 0.118
  59.     }
  60.     bias_filler {
  61.       type: "constant"
  62.       value: 0
  63.     }
  64.   }
  65. }
  66. layer {
  67.   name: "BatchNorm1"
  68.   type: "BatchNorm"
  69.   bottom: "Convolution1"
  70.   top: "Convolution1"
  71.   param {
  72.     lr_mult: 0
  73.     decay_mult: 0
  74.   }
  75.   param {
  76.     lr_mult: 0
  77.     decay_mult: 0
  78.   }
  79.   param {
  80.     lr_mult: 0
  81.     decay_mult: 0
  82.   }
  83. }
  84. layer {
  85.   name: "Scale1"
  86.   type: "Scale"
  87.   bottom: "Convolution1"
  88.   top: "Convolution1"
  89.   scale_param {
  90.     bias_term: true
  91.   }
  92. }
  93. layer {
  94.   name: "ReLU1"
  95.   type: "ReLU"
  96.   bottom: "Convolution1"
  97.   top: "Convolution1"
  98. }
  99. layer {
  100.   name: "Convolution2"
  101.   type: "Convolution"
  102.   bottom: "Convolution1"
  103.   top: "Convolution2"
  104.   param {
  105.     lr_mult: 1
  106.     decay_mult: 1
  107.   }
  108.   param {
  109.     lr_mult: 2
  110.     decay_mult: 0
  111.   }
  112.   convolution_param {
  113.     num_output: 16
  114.     pad: 1
  115.     kernel_size: 3
  116.     stride: 1
  117.     weight_filler {
  118.       type: "gaussian"
  119.       std: 0.118
  120.     }
  121.     bias_filler {
  122.       type: "constant"
  123.       value: 0
  124.     }
  125.   }
  126. }
  127. layer {
  128.   name: "BatchNorm2"
  129.   type: "BatchNorm"
  130.   bottom: "Convolution2"
  131.   top: "Convolution2"
  132.   param {
  133.     lr_mult: 0
  134.     decay_mult: 0
  135.   }
  136.   param {
  137.     lr_mult: 0
  138.     decay_mult: 0
  139.   }
  140.   param {
  141.     lr_mult: 0
  142.     decay_mult: 0
  143.   }
  144. }
  145. layer {
  146.   name: "Scale2"
  147.   type: "Scale"
  148.   bottom: "Convolution2"
  149.   top: "Convolution2"
  150.   scale_param {
  151.     bias_term: true
  152.   }
  153. }
  154. layer {
  155.   name: "ReLU2"
  156.   type: "ReLU"
  157.   bottom: "Convolution2"
  158.   top: "Convolution2"
  159. }
  160. layer {
  161.   name: "Convolution3"
  162.   type: "Convolution"
  163.   bottom: "Convolution2"
  164.   top: "Convolution3"
  165.   param {
  166.     lr_mult: 1
  167.     decay_mult: 1
  168.   }
  169.   param {
  170.     lr_mult: 2
  171.     decay_mult: 0
  172.   }
  173.   convolution_param {
  174.     num_output: 16
  175.     pad: 1
  176.     kernel_size: 3
  177.     stride: 1
  178.     weight_filler {
  179.       type: "gaussian"
  180.       std: 0.118
  181.     }
  182.     bias_filler {
  183.       type: "constant"
  184.       value: 0
  185.     }
  186.   }
  187. }
  188. layer {
  189.   name: "BatchNorm3"
  190.   type: "BatchNorm"
  191.   bottom: "Convolution3"
  192.   top: "Convolution3"
  193.   param {
  194.     lr_mult: 0
  195.     decay_mult: 0
  196.   }
  197.   param {
  198.     lr_mult: 0
  199.     decay_mult: 0
  200.   }
  201.   param {
  202.     lr_mult: 0
  203.     decay_mult: 0
  204.   }
  205. }
  206. layer {
  207.   name: "Scale3"
  208.   type: "Scale"
  209.   bottom: "Convolution3"
  210.   top: "Convolution3"
  211.   scale_param {
  212.     bias_term: true
  213.   }
  214. }
  215. layer {
  216.   name: "Eltwise1"
  217.   type: "Eltwise"
  218.   bottom: "Convolution1"
  219.   bottom: "Convolution3"
  220.   top: "Eltwise1"
  221.   eltwise_param {
  222.     operation: SUM
  223.   }
  224. }
  225. layer {
  226.   name: "ReLU3"
  227.   type: "ReLU"
  228.   bottom: "Eltwise1"
  229.   top: "Eltwise1"
  230. }
  231. layer {
  232.   name: "Convolution4"
  233.   type: "Convolution"
  234.   bottom: "Eltwise1"
  235.   top: "Convolution4"
  236.   param {
  237.     lr_mult: 1
  238.     decay_mult: 1
  239.   }
  240.   param {
  241.     lr_mult: 2
  242.     decay_mult: 0
  243.   }
  244.   convolution_param {
  245.     num_output: 16
  246.     pad: 1
  247.     kernel_size: 3
  248.     stride: 1
  249.     weight_filler {
  250.       type: "gaussian"
  251.       std: 0.118
  252.     }
  253.     bias_filler {
  254.       type: "constant"
  255.       value: 0
  256.     }
  257.   }
  258. }
  259. layer {
  260.   name: "BatchNorm4"
  261.   type: "BatchNorm"
  262.   bottom: "Convolution4"
  263.   top: "Convolution4"
  264.   param {
  265.     lr_mult: 0
  266.     decay_mult: 0
  267.   }
  268.   param {
  269.     lr_mult: 0
  270.     decay_mult: 0
  271.   }
  272.   param {
  273.     lr_mult: 0
  274.     decay_mult: 0
  275.   }
  276. }
  277. layer {
  278.   name: "Scale4"
  279.   type: "Scale"
  280.   bottom: "Convolution4"
  281.   top: "Convolution4"
  282.   scale_param {
  283.     bias_term: true
  284.   }
  285. }
  286. layer {
  287.   name: "ReLU4"
  288.   type: "ReLU"
  289.   bottom: "Convolution4"
  290.   top: "Convolution4"
  291. }
  292. layer {
  293.   name: "Convolution5"
  294.   type: "Convolution"
  295.   bottom: "Convolution4"
  296.   top: "Convolution5"
  297.   param {
  298.     lr_mult: 1
  299.     decay_mult: 1
  300.   }
  301.   param {
  302.     lr_mult: 2
  303.     decay_mult: 0
  304.   }
  305.   convolution_param {
  306.     num_output: 16
  307.     pad: 1
  308.     kernel_size: 3
  309.     stride: 1
  310.     weight_filler {
  311.       type: "gaussian"
  312.       std: 0.118
  313.     }
  314.     bias_filler {
  315.       type: "constant"
  316.       value: 0
  317.     }
  318.   }
  319. }
  320. layer {
  321.   name: "BatchNorm5"
  322.   type: "BatchNorm"
  323.   bottom: "Convolution5"
  324.   top: "Convolution5"
  325.   param {
  326.     lr_mult: 0
  327.     decay_mult: 0
  328.   }
  329.   param {
  330.     lr_mult: 0
  331.     decay_mult: 0
  332.   }
  333.   param {
  334.     lr_mult: 0
  335.     decay_mult: 0
  336.   }
  337. }
  338. layer {
  339.   name: "Scale5"
  340.   type: "Scale"
  341.   bottom: "Convolution5"
  342.   top: "Convolution5"
  343.   scale_param {
  344.     bias_term: true
  345.   }
  346. }
  347. layer {
  348.   name: "Eltwise2"
  349.   type: "Eltwise"
  350.   bottom: "Eltwise1"
  351.   bottom: "Convolution5"
  352.   top: "Eltwise2"
  353.   eltwise_param {
  354.     operation: SUM
  355.   }
  356. }
  357. layer {
  358.   name: "ReLU5"
  359.   type: "ReLU"
  360.   bottom: "Eltwise2"
  361.   top: "Eltwise2"
  362. }
  363. layer {
  364.   name: "Convolution6"
  365.   type: "Convolution"
  366.   bottom: "Eltwise2"
  367.   top: "Convolution6"
  368.   param {
  369.     lr_mult: 1
  370.     decay_mult: 1
  371.   }
  372.   param {
  373.     lr_mult: 2
  374.     decay_mult: 0
  375.   }
  376.   convolution_param {
  377.     num_output: 16
  378.     pad: 1
  379.     kernel_size: 3
  380.     stride: 1
  381.     weight_filler {
  382.       type: "gaussian"
  383.       std: 0.118
  384.     }
  385.     bias_filler {
  386.       type: "constant"
  387.       value: 0
  388.     }
  389.   }
  390. }
  391. layer {
  392.   name: "BatchNorm6"
  393.   type: "BatchNorm"
  394.   bottom: "Convolution6"
  395.   top: "Convolution6"
  396.   param {
  397.     lr_mult: 0
  398.     decay_mult: 0
  399.   }
  400.   param {
  401.     lr_mult: 0
  402.     decay_mult: 0
  403.   }
  404.   param {
  405.     lr_mult: 0
  406.     decay_mult: 0
  407.   }
  408. }
  409. layer {
  410.   name: "Scale6"
  411.   type: "Scale"
  412.   bottom: "Convolution6"
  413.   top: "Convolution6"
  414.   scale_param {
  415.     bias_term: true
  416.   }
  417. }
  418. layer {
  419.   name: "ReLU6"
  420.   type: "ReLU"
  421.   bottom: "Convolution6"
  422.   top: "Convolution6"
  423. }
  424. layer {
  425.   name: "Convolution7"
  426.   type: "Convolution"
  427.   bottom: "Convolution6"
  428.   top: "Convolution7"
  429.   param {
  430.     lr_mult: 1
  431.     decay_mult: 1
  432.   }
  433.   param {
  434.     lr_mult: 2
  435.     decay_mult: 0
  436.   }
  437.   convolution_param {
  438.     num_output: 16
  439.     pad: 1
  440.     kernel_size: 3
  441.     stride: 1
  442.     weight_filler {
  443.       type: "gaussian"
  444.       std: 0.118
  445.     }
  446.     bias_filler {
  447.       type: "constant"
  448.       value: 0
  449.     }
  450.   }
  451. }
  452. layer {
  453.   name: "BatchNorm7"
  454.   type: "BatchNorm"
  455.   bottom: "Convolution7"
  456.   top: "Convolution7"
  457.   param {
  458.     lr_mult: 0
  459.     decay_mult: 0
  460.   }
  461.   param {
  462.     lr_mult: 0
  463.     decay_mult: 0
  464.   }
  465.   param {
  466.     lr_mult: 0
  467.     decay_mult: 0
  468.   }
  469. }
  470. layer {
  471.   name: "Scale7"
  472.   type: "Scale"
  473.   bottom: "Convolution7"
  474.   top: "Convolution7"
  475.   scale_param {
  476.     bias_term: true
  477.   }
  478. }
  479. layer {
  480.   name: "Eltwise3"
  481.   type: "Eltwise"
  482.   bottom: "Eltwise2"
  483.   bottom: "Convolution7"
  484.   top: "Eltwise3"
  485.   eltwise_param {
  486.     operation: SUM
  487.   }
  488. }
  489. layer {
  490.   name: "ReLU7"
  491.   type: "ReLU"
  492.   bottom: "Eltwise3"
  493.   top: "Eltwise3"
  494. }
  495. layer {
  496.   name: "Convolution8"
  497.   type: "Convolution"
  498.   bottom: "Eltwise3"
  499.   top: "Convolution8"
  500.   param {
  501.     lr_mult: 1
  502.     decay_mult: 1
  503.   }
  504.   param {
  505.     lr_mult: 2
  506.     decay_mult: 0
  507.   }
  508.   convolution_param {
  509.     num_output: 32
  510.     pad: 0
  511.     kernel_size: 1
  512.     stride: 2
  513.     weight_filler {
  514.       type: "gaussian"
  515.       std: 0.25
  516.     }
  517.     bias_filler {
  518.       type: "constant"
  519.       value: 0
  520.     }
  521.   }
  522. }
  523. layer {
  524.   name: "BatchNorm8"
  525.   type: "BatchNorm"
  526.   bottom: "Convolution8"
  527.   top: "Convolution8"
  528.   param {
  529.     lr_mult: 0
  530.     decay_mult: 0
  531.   }
  532.   param {
  533.     lr_mult: 0
  534.     decay_mult: 0
  535.   }
  536.   param {
  537.     lr_mult: 0
  538.     decay_mult: 0
  539.   }
  540. }
  541. layer {
  542.   name: "Scale8"
  543.   type: "Scale"
  544.   bottom: "Convolution8"
  545.   top: "Convolution8"
  546.   scale_param {
  547.     bias_term: true
  548.   }
  549. }
  550. layer {
  551.   name: "Convolution9"
  552.   type: "Convolution"
  553.   bottom: "Eltwise3"
  554.   top: "Convolution9"
  555.   param {
  556.     lr_mult: 1
  557.     decay_mult: 1
  558.   }
  559.   param {
  560.     lr_mult: 2
  561.     decay_mult: 0
  562.   }
  563.   convolution_param {
  564.     num_output: 32
  565.     pad: 1
  566.     kernel_size: 3
  567.     stride: 2
  568.     weight_filler {
  569.       type: "gaussian"
  570.       std: 0.083
  571.     }
  572.     bias_filler {
  573.       type: "constant"
  574.       value: 0
  575.     }
  576.   }
  577. }
  578. layer {
  579.   name: "BatchNorm9"
  580.   type: "BatchNorm"
  581.   bottom: "Convolution9"
  582.   top: "Convolution9"
  583.   param {
  584.     lr_mult: 0
  585.     decay_mult: 0
  586.   }
  587.   param {
  588.     lr_mult: 0
  589.     decay_mult: 0
  590.   }
  591.   param {
  592.     lr_mult: 0
  593.     decay_mult: 0
  594.   }
  595. }
  596. layer {
  597.   name: "Scale9"
  598.   type: "Scale"
  599.   bottom: "Convolution9"
  600.   top: "Convolution9"
  601.   scale_param {
  602.     bias_term: true
  603.   }
  604. }
  605. layer {
  606.   name: "ReLU8"
  607.   type: "ReLU"
  608.   bottom: "Convolution9"
  609.   top: "Convolution9"
  610. }
  611. layer {
  612.   name: "Convolution10"
  613.   type: "Convolution"
  614.   bottom: "Convolution9"
  615.   top: "Convolution10"
  616.   param {
  617.     lr_mult: 1
  618.     decay_mult: 1
  619.   }
  620.   param {
  621.     lr_mult: 2
  622.     decay_mult: 0
  623.   }
  624.   convolution_param {
  625.     num_output: 32
  626.     pad: 1
  627.     kernel_size: 3
  628.     stride: 1
  629.     weight_filler {
  630.       type: "gaussian"
  631.       std: 0.083
  632.     }
  633.     bias_filler {
  634.       type: "constant"
  635.       value: 0
  636.     }
  637.   }
  638. }
  639. layer {
  640.   name: "BatchNorm10"
  641.   type: "BatchNorm"
  642.   bottom: "Convolution10"
  643.   top: "Convolution10"
  644.   param {
  645.     lr_mult: 0
  646.     decay_mult: 0
  647.   }
  648.   param {
  649.     lr_mult: 0
  650.     decay_mult: 0
  651.   }
  652.   param {
  653.     lr_mult: 0
  654.     decay_mult: 0
  655.   }
  656. }
  657. layer {
  658.   name: "Scale10"
  659.   type: "Scale"
  660.   bottom: "Convolution10"
  661.   top: "Convolution10"
  662.   scale_param {
  663.     bias_term: true
  664.   }
  665. }
  666. layer {
  667.   name: "Eltwise4"
  668.   type: "Eltwise"
  669.   bottom: "Convolution8"
  670.   bottom: "Convolution10"
  671.   top: "Eltwise4"
  672.   eltwise_param {
  673.     operation: SUM
  674.   }
  675. }
  676. layer {
  677.   name: "ReLU9"
  678.   type: "ReLU"
  679.   bottom: "Eltwise4"
  680.   top: "Eltwise4"
  681. }
  682. layer {
  683.   name: "Convolution11"
  684.   type: "Convolution"
  685.   bottom: "Eltwise4"
  686.   top: "Convolution11"
  687.   param {
  688.     lr_mult: 1
  689.     decay_mult: 1
  690.   }
  691.   param {
  692.     lr_mult: 2
  693.     decay_mult: 0
  694.   }
  695.   convolution_param {
  696.     num_output: 32
  697.     pad: 1
  698.     kernel_size: 3
  699.     stride: 1
  700.     weight_filler {
  701.       type: "gaussian"
  702.       std: 0.083
  703.     }
  704.     bias_filler {
  705.       type: "constant"
  706.       value: 0
  707.     }
  708.   }
  709. }
  710. layer {
  711.   name: "BatchNorm11"
  712.   type: "BatchNorm"
  713.   bottom: "Convolution11"
  714.   top: "Convolution11"
  715.   param {
  716.     lr_mult: 0
  717.     decay_mult: 0
  718.   }
  719.   param {
  720.     lr_mult: 0
  721.     decay_mult: 0
  722.   }
  723.   param {
  724.     lr_mult: 0
  725.     decay_mult: 0
  726.   }
  727. }
  728. layer {
  729.   name: "Scale11"
  730.   type: "Scale"
  731.   bottom: "Convolution11"
  732.   top: "Convolution11"
  733.   scale_param {
  734.     bias_term: true
  735.   }
  736. }
  737. layer {
  738.   name: "ReLU10"
  739.   type: "ReLU"
  740.   bottom: "Convolution11"
  741.   top: "Convolution11"
  742. }
  743. layer {
  744.   name: "Convolution12"
  745.   type: "Convolution"
  746.   bottom: "Convolution11"
  747.   top: "Convolution12"
  748.   param {
  749.     lr_mult: 1
  750.     decay_mult: 1
  751.   }
  752.   param {
  753.     lr_mult: 2
  754.     decay_mult: 0
  755.   }
  756.   convolution_param {
  757.     num_output: 32
  758.     pad: 1
  759.     kernel_size: 3
  760.     stride: 1
  761.     weight_filler {
  762.       type: "gaussian"
  763.       std: 0.083
  764.     }
  765.     bias_filler {
  766.       type: "constant"
  767.       value: 0
  768.     }
  769.   }
  770. }
  771. layer {
  772.   name: "BatchNorm12"
  773.   type: "BatchNorm"
  774.   bottom: "Convolution12"
  775.   top: "Convolution12"
  776.   param {
  777.     lr_mult: 0
  778.     decay_mult: 0
  779.   }
  780.   param {
  781.     lr_mult: 0
  782.     decay_mult: 0
  783.   }
  784.   param {
  785.     lr_mult: 0
  786.     decay_mult: 0
  787.   }
  788. }
  789. layer {
  790.   name: "Scale12"
  791.   type: "Scale"
  792.   bottom: "Convolution12"
  793.   top: "Convolution12"
  794.   scale_param {
  795.     bias_term: true
  796.   }
  797. }
  798. layer {
  799.   name: "Eltwise5"
  800.   type: "Eltwise"
  801.   bottom: "Eltwise4"
  802.   bottom: "Convolution12"
  803.   top: "Eltwise5"
  804.   eltwise_param {
  805.     operation: SUM
  806.   }
  807. }
  808. layer {
  809.   name: "ReLU11"
  810.   type: "ReLU"
  811.   bottom: "Eltwise5"
  812.   top: "Eltwise5"
  813. }
  814. layer {
  815.   name: "Convolution13"
  816.   type: "Convolution"
  817.   bottom: "Eltwise5"
  818.   top: "Convolution13"
  819.   param {
  820.     lr_mult: 1
  821.     decay_mult: 1
  822.   }
  823.   param {
  824.     lr_mult: 2
  825.     decay_mult: 0
  826.   }
  827.   convolution_param {
  828.     num_output: 32
  829.     pad: 1
  830.     kernel_size: 3
  831.     stride: 1
  832.     weight_filler {
  833.       type: "gaussian"
  834.       std: 0.083
  835.     }
  836.     bias_filler {
  837.       type: "constant"
  838.       value: 0
  839.     }
  840.   }
  841. }
  842. layer {
  843.   name: "BatchNorm13"
  844.   type: "BatchNorm"
  845.   bottom: "Convolution13"
  846.   top: "Convolution13"
  847.   param {
  848.     lr_mult: 0
  849.     decay_mult: 0
  850.   }
  851.   param {
  852.     lr_mult: 0
  853.     decay_mult: 0
  854.   }
  855.   param {
  856.     lr_mult: 0
  857.     decay_mult: 0
  858.   }
  859. }
  860. layer {
  861.   name: "Scale13"
  862.   type: "Scale"
  863.   bottom: "Convolution13"
  864.   top: "Convolution13"
  865.   scale_param {
  866.     bias_term: true
  867.   }
  868. }
  869. layer {
  870.   name: "ReLU12"
  871.   type: "ReLU"
  872.   bottom: "Convolution13"
  873.   top: "Convolution13"
  874. }
  875. layer {
  876.   name: "Convolution14"
  877.   type: "Convolution"
  878.   bottom: "Convolution13"
  879.   top: "Convolution14"
  880.   param {
  881.     lr_mult: 1
  882.     decay_mult: 1
  883.   }
  884.   param {
  885.     lr_mult: 2
  886.     decay_mult: 0
  887.   }
  888.   convolution_param {
  889.     num_output: 32
  890.     pad: 1
  891.     kernel_size: 3
  892.     stride: 1
  893.     weight_filler {
  894.       type: "gaussian"
  895.       std: 0.083
  896.     }
  897.     bias_filler {
  898.       type: "constant"
  899.       value: 0
  900.     }
  901.   }
  902. }
  903. layer {
  904.   name: "BatchNorm14"
  905.   type: "BatchNorm"
  906.   bottom: "Convolution14"
  907.   top: "Convolution14"
  908.   param {
  909.     lr_mult: 0
  910.     decay_mult: 0
  911.   }
  912.   param {
  913.     lr_mult: 0
  914.     decay_mult: 0
  915.   }
  916.   param {
  917.     lr_mult: 0
  918.     decay_mult: 0
  919.   }
  920. }
  921. layer {
  922.   name: "Scale14"
  923.   type: "Scale"
  924.   bottom: "Convolution14"
  925.   top: "Convolution14"
  926.   scale_param {
  927.     bias_term: true
  928.   }
  929. }
  930. layer {
  931.   name: "Eltwise6"
  932.   type: "Eltwise"
  933.   bottom: "Eltwise5"
  934.   bottom: "Convolution14"
  935.   top: "Eltwise6"
  936.   eltwise_param {
  937.     operation: SUM
  938.   }
  939. }
  940. layer {
  941.   name: "ReLU13"
  942.   type: "ReLU"
  943.   bottom: "Eltwise6"
  944.   top: "Eltwise6"
  945. }
  946. layer {
  947.   name: "Convolution15"
  948.   type: "Convolution"
  949.   bottom: "Eltwise6"
  950.   top: "Convolution15"
  951.   param {
  952.     lr_mult: 1
  953.     decay_mult: 1
  954.   }
  955.   param {
  956.     lr_mult: 2
  957.     decay_mult: 0
  958.   }
  959.   convolution_param {
  960.     num_output: 64
  961.     pad: 0
  962.     kernel_size: 1
  963.     stride: 2
  964.     weight_filler {
  965.       type: "gaussian"
  966.       std: 0.176776695297
  967.     }
  968.     bias_filler {
  969.       type: "constant"
  970.       value: 0
  971.     }
  972.   }
  973. }
  974. layer {
  975.   name: "BatchNorm15"
  976.   type: "BatchNorm"
  977.   bottom: "Convolution15"
  978.   top: "Convolution15"
  979.   param {
  980.     lr_mult: 0
  981.     decay_mult: 0
  982.   }
  983.   param {
  984.     lr_mult: 0
  985.     decay_mult: 0
  986.   }
  987.   param {
  988.     lr_mult: 0
  989.     decay_mult: 0
  990.   }
  991. }
  992. layer {
  993.   name: "Scale15"
  994.   type: "Scale"
  995.   bottom: "Convolution15"
  996.   top: "Convolution15"
  997.   scale_param {
  998.     bias_term: true
  999.   }
  1000. }
  1001. layer {
  1002.   name: "BinBatchNorm16"
  1003.   type: "BatchNorm"
  1004.   bottom: "Eltwise6"
  1005.   top: "Eltwise6_1"
  1006. }
  1007.  
  1008. layer {
  1009.   name: "Binactiv16"
  1010.   type: "BinActiv"
  1011.   bottom: "Eltwise6_1"
  1012.   top: "bin-eltwise6"
  1013.    binactiv_param{
  1014.   no_k: true
  1015.   }
  1016. }
  1017.  
  1018. layer {
  1019.   name: "BinConvolution16"
  1020.   type: "BinaryConvolution"
  1021.   bottom: "bin-eltwise6"
  1022.   top: "Convolution16"
  1023.   param {
  1024.     lr_mult: 1
  1025.     decay_mult: 1
  1026.   }
  1027.   param {
  1028.     lr_mult: 2
  1029.     decay_mult: 0
  1030.   }
  1031.   convolution_param {
  1032.     num_output: 64
  1033.     pad: 1
  1034.     kernel_size: 3
  1035.     stride: 2
  1036.     weight_filler {
  1037.       type: "gaussian"
  1038.       std: 0.059
  1039.     }
  1040.     bias_filler {
  1041.       type: "constant"
  1042.       value: 0
  1043.     }
  1044.   }
  1045. }
  1046.  
  1047.  
  1048. layer {
  1049.   name: "BinBatchNorm17"
  1050.   type: "BatchNorm"
  1051.   bottom: "Convolution16"
  1052.   top: "Convolution16"
  1053. }
  1054.  
  1055. layer {
  1056.   name: "Binactiv17"
  1057.   type: "BinActiv"
  1058.   bottom: "Convolution16"
  1059.   top: "B-Convolution16"
  1060.    binactiv_param{
  1061.   no_k: true
  1062.   }
  1063. }
  1064. layer {
  1065.   name: "BinConvolution17"
  1066.   type: "BinaryConvolution"
  1067.   bottom: "B-Convolution16"
  1068.   top: "Convolution17"
  1069.   param {
  1070.     lr_mult: 1
  1071.     decay_mult: 1
  1072.   }
  1073.   param {
  1074.     lr_mult: 2
  1075.     decay_mult: 0
  1076.   }
  1077.   convolution_param {
  1078.     num_output: 64
  1079.     pad: 1
  1080.     kernel_size: 3
  1081.     stride: 1
  1082.     weight_filler {
  1083.       type: "gaussian"
  1084.       std: 0.059
  1085.     }
  1086.     bias_filler {
  1087.       type: "constant"
  1088.       value: 0
  1089.     }
  1090.   }
  1091. }
  1092. layer {
  1093.   name: "Eltwise7"
  1094.   type: "Eltwise"
  1095.   bottom: "Convolution15"
  1096.   bottom: "Convolution17"
  1097.   top: "Eltwise7"
  1098.   eltwise_param {
  1099.     operation: SUM
  1100.   }
  1101. }
  1102. layer {
  1103.   name: "PReLU15"
  1104.   type: "PReLU"
  1105.   bottom: "Eltwise7"
  1106.   top: "Eltwise7"
  1107. }
  1108. layer {
  1109.   name: "BatchNorm18"
  1110.   type: "BatchNorm"
  1111.   bottom: "Eltwise7"
  1112.   top: "Eltwise7"
  1113. }
  1114.  
  1115. layer {
  1116.   name: "Binactiv18"
  1117.   type: "BinActiv"
  1118.   bottom: "Eltwise7"
  1119.   top: "B-Eltwise7"
  1120.   binactiv_param{
  1121.   no_k: true
  1122.   }
  1123. }
  1124.  
  1125. layer {
  1126.   name: "BinConvolution18"
  1127.   type: "BinaryConvolution"
  1128.   bottom: "B-Eltwise7"
  1129.   top: "Convolution18"
  1130.   param {
  1131.     lr_mult: 1
  1132.     decay_mult: 1
  1133.   }
  1134.   param {
  1135.     lr_mult: 2
  1136.     decay_mult: 0
  1137.   }
  1138.   convolution_param {
  1139.     num_output: 64
  1140.     pad: 1
  1141.     kernel_size: 3
  1142.     stride: 1
  1143.     weight_filler {
  1144.       type: "gaussian"
  1145.       std: 0.059
  1146.     }
  1147.     bias_filler {
  1148.       type: "constant"
  1149.       value: 0
  1150.     }
  1151.   }
  1152. }
  1153.  
  1154.  
  1155. layer {
  1156.   name: "BatchNorm19"
  1157.   type: "BatchNorm"
  1158.   bottom: "Convolution18"
  1159.   top: "Convolution18"
  1160. }
  1161.  
  1162. layer {
  1163.   name: "Binactiv19"
  1164.   type: "BinActiv"
  1165.   bottom: "Convolution18"
  1166.   top: "B-Convolution18"
  1167.    binactiv_param{
  1168.   no_k: true
  1169.   }
  1170. }
  1171.  
  1172. layer {
  1173.   name: "BinConvolution19"
  1174.   type: "BinaryConvolution"
  1175.   bottom: "B-Convolution18"
  1176.   top: "Convolution19"
  1177.   param {
  1178.     lr_mult: 1
  1179.     decay_mult: 1
  1180.   }
  1181.   param {
  1182.     lr_mult: 2
  1183.     decay_mult: 0
  1184.   }
  1185.   convolution_param {
  1186.     num_output: 64
  1187.     pad: 1
  1188.     kernel_size: 3
  1189.     stride: 1
  1190.     weight_filler {
  1191.       type: "gaussian"
  1192.       std: 0.059
  1193.     }
  1194.     bias_filler {
  1195.       type: "constant"
  1196.       value: 0
  1197.     }
  1198.   }
  1199. }
  1200.  
  1201. layer {
  1202.   name: "Eltwise8"
  1203.   type: "Eltwise"
  1204.   bottom: "Eltwise7"
  1205.   bottom: "Convolution19"
  1206.   top: "Eltwise8"
  1207.   eltwise_param {
  1208.     operation: SUM
  1209.   }
  1210. }
  1211. layer {
  1212.   name: "PReLU17"
  1213.   type: "PReLU"
  1214.   bottom: "Eltwise8"
  1215.   top: "Eltwise8"
  1216. }
  1217. layer {
  1218.   name: "BatchNorm20"
  1219.   type: "BatchNorm"
  1220.   bottom: "Eltwise8"
  1221.   top: "BEltwise8"
  1222. }
  1223. layer {
  1224.   name: "Binactiv20"
  1225.   type: "BinActiv"
  1226.   bottom: "BEltwise8"
  1227.   top: "B-Eltwise8"
  1228.   binactiv_param{
  1229.     no_k:true
  1230.   }
  1231. }
  1232. layer {
  1233.   name: "BinConvolution20"
  1234.   type: "BinaryConvolution"
  1235.   bottom: "B-Eltwise8"
  1236.   top: "Convolution20"
  1237.   param {
  1238.     lr_mult: 1
  1239.     decay_mult: 1
  1240.   }
  1241.   param {
  1242.     lr_mult: 2
  1243.     decay_mult: 0
  1244.   }
  1245.   convolution_param {
  1246.     num_output: 64
  1247.     pad: 1
  1248.     kernel_size: 3
  1249.     stride: 1
  1250.     weight_filler {
  1251.       type: "gaussian"
  1252.       std: 0.059
  1253.     }
  1254.     bias_filler {
  1255.       type: "constant"
  1256.       value: 0
  1257.     }
  1258.   }
  1259. }
  1260.  
  1261.  
  1262. layer {
  1263.   name: "BatchNorm21"
  1264.   type: "BatchNorm"
  1265.   bottom: "Convolution20"
  1266.   top: "Convolution20"
  1267. }
  1268. layer {
  1269.   name: "Binactiv21"
  1270.   type: "BinActiv"
  1271.   bottom: "Convolution20"
  1272.   top: "B-Convolution20"
  1273.   binactiv_param{
  1274.     no_k:true
  1275.   }
  1276. }
  1277.  
  1278. layer {
  1279.   name: "BinConvolution21"
  1280.   type: "BinaryConvolution"
  1281.   bottom: "B-Convolution20"
  1282.   top: "Convolution21"
  1283.   param {
  1284.     lr_mult: 1
  1285.     decay_mult: 1
  1286.   }
  1287.   param {
  1288.     lr_mult: 2
  1289.     decay_mult: 0
  1290.   }
  1291.   convolution_param {
  1292.     num_output: 64
  1293.     pad: 1
  1294.     kernel_size: 3
  1295.     stride: 1
  1296.     weight_filler {
  1297.       type: "gaussian"
  1298.       std: 0.059
  1299.     }
  1300.     bias_filler {
  1301.       type: "constant"
  1302.       value: 0
  1303.     }
  1304.   }
  1305. }
  1306.  
  1307. layer {
  1308.   name: "Eltwise9"
  1309.   type: "Eltwise"
  1310.   bottom: "Eltwise8"
  1311.   bottom: "Convolution21"
  1312.   top: "Eltwise9"
  1313.   eltwise_param {
  1314.     operation: SUM
  1315.   }
  1316. }
  1317. layer {
  1318.   name: "PReLU19"
  1319.   type: "PReLU"
  1320.   bottom: "Eltwise9"
  1321.   top: "Eltwise9"
  1322. }
  1323. layer {
  1324.   name: "Pooling1"
  1325.   type: "Pooling"
  1326.   bottom: "Eltwise9"
  1327.   top: "Pooling1"
  1328.   pooling_param {
  1329.     pool: AVE
  1330.     global_pooling: true
  1331.   }
  1332. }
  1333. layer {
  1334.   name: "InnerProduct1"
  1335.   type: "InnerProduct"
  1336.   bottom: "Pooling1"
  1337.   top: "InnerProduct1"
  1338.   param {
  1339.     lr_mult: 1
  1340.     decay_mult: 1
  1341.   }
  1342.   param {
  1343.     lr_mult: 2
  1344.     decay_mult: 1
  1345.   }
  1346.   inner_product_param {
  1347.     num_output: 10
  1348.     weight_filler {
  1349.       type: "gaussian"
  1350.       std: 0.01
  1351.     }
  1352.     bias_filler {
  1353.       type: "constant"
  1354.       value: 0
  1355.     }
  1356.   }
  1357. }
  1358. layer {
  1359.   name: "SoftmaxWithLoss1"
  1360.   type: "SoftmaxWithLoss"
  1361.   bottom: "InnerProduct1"
  1362.   bottom: "Data2"
  1363.   top: "SoftmaxWithLoss1"
  1364. }
  1365. layer {
  1366.   name: "Accuracy1"
  1367.   type: "Accuracy"
  1368.   bottom: "InnerProduct1"
  1369.   bottom: "Data2"
  1370.   top: "Accuracy1"
  1371.   include {
  1372.     phase: TEST
  1373.   }
  1374. }
RAW Paste Data
Top