1.先验证剩余模型（1:vgg16、2:resnet20、3:fashion_lenet5、4:svhn_lenet5）onehot_refine_200_2000的矩阵经过降维之后是否能达到和lenet145一样惊艳的效果


    		 1平均值 1最大值 1最小值 1方差 2平均值 2最大值 2最小值 2方差 3平均值 3最大值 3最小值 3方差 4平均值 4最大值 4最小值 4方差
    5 0.740589474 2.248 0 0.247771316 0.757515789 6 0 0.942563808 0.893010526 4.046 0 0.422708347 4.675210526 8.947 1.792 2.391439935
    6 0.724378947 3.709 0 0.626279267 1.510484211 6.93 0 0.855478523 2.367473684 5.316 1.329 0.443260102 4.476252632 10.204 2.384 4.169062105
    7 0.751863158 2.945 0 0.416027087 0.777505263 3.39 0 0.376995955 0.530347368 2.247 0 0.2164449 4.159315789 9.792 2.289 2.190974132
    8 0.567326316 2.439 0 0.291456136 0.365147368 1.961 0 0.115020062 1.6382 2.878 0 0.700995886 4.277936842 7.792 2.458 2.16826928
    9 0.793052632 8.474 0 2.485374134 1.068884211 4.918 0 0.91478926 0.558578947 2.198 0 0.16518496 4.570968421 9.124 3.265 0.934614873
    10 2.718768421 8 1.647 0.713631778 0.544252632 2.56 0 0.245795726 1.945378947 4.76 0.621 1.021884467 5.201989474 10.077 3.948 1.161112116
    11 2.793010526 5.883 1.6 0.370812937 0.695915789 4.227 0 1.202680014 1.815684211 4.082 1.029 0.198789227 4.685252632 9.124 3.131 1.335107031
    12 2.270621053 4.127 0.705 0.722178046 1.464894737 4.938 0 1.082229505 1.968894737 3.51 0 0.444277357 3.598515789 10.407 2.272 1.916254629
    13 3.096010526 6.193 1.611 0.451414726 2.418515789 4.918 0.948 1.222774776 1.338231579 5.474 0.117 0.832848388 4.483852632 7.408 3.001 1.262053536
    14 1.009368421 3.704 0 0.41090958 1.803557895 4.094 0 0.511157068 2.258936842 6.78 1.071 0.748519659 2.026631579 3.792 1.22 0.283672064
    15 0.763252632 5 0 0.595609999 1.459736842 3.115 0 0.163317036 2.430084211 6.123 1.181 0.746071803 2.027284211 8.484 0.923 1.614454603
    16 0.674884211 3.75 0 0.354594102 0.873168421 1.957 0 0.254264182 3.051305263 11.133 1.648 2.886148086 4.870084211 9.259 3.611 1.374509656
    17 1.279326316 3.306 0 0.417743315 0.559515789 5.773 0 0.901017639 3.262431579 9.172 1.982 1.065415403 2.963989474 5.555 2.005 0.654151631
    18 0.582505263 1.886 0 0.143924208 0.604073684 1.818 0 0.111574426 3.342736842 11.133 1.87 2.917561141 3.461084211 9.753 1.125 0.881864056
    19 0.901 4.167 0 0.686005832 0.528884211 2.439 0 0.170148544 3.758852632 7.211 2.487 0.941864568 3.419684211 6.25 2.475 0.550954974
    20 0.471821053 1.756 0 0.156592421 0.737126316 2.176 0 0.189872216 2.937747368 5.25 1.528 0.74350162 3.025568421 12.281 1.859 3.109390624
    21 0.603431579 1.504 0 0.094120814 0.396810526 3.636 0 0.242635754 2.510263158 11.836 1.207 3.395018489 3.653326316 8.772 2.084 0.835635357
    22 0.596578947 1.175 0 0.125514054 0.837073684 3.571 0 0.46596651 2.6558 8.792 0.865 1.694445192 2.755221053 5.264 1.792 0.290117499
    23 0.9652 3.704 0 0.671732855 0.562694737 7.692 0 1.55875518 3.7862 9.389 2.1 2.205033802 3.849494737 11.999 1.653 1.955776313
    24 0.831863158 4.051 0 0.415437781 1.595736842 3.356 0 0.438273731 1.498473684 9.091 0.2 3.112441512 2.718294737 13.723 1.667 3.366406334
    25 0.570557895 3.391 0 0.648006541 0.425494737 4.478 0 0.597322629 1.739168421 5.455 0.377 0.714804203 3.075147368 6.826 1.686 0.600442757
    26 0.580863158 4.388 0 0.656891508 0.762863158 4.474 0 0.435749108 2.152442105 4.76 1.189 0.312986878 3.306052632 10.791 1.215 1.238287376
    27 1.263778947 6.963 0.095 1.060769646 0.384505263 6.169 0 0.791657534 1.208178947 4.616 0 0.468654926 2.441442105 8.578 1.216 0.879424205
    28 1.687473684 2.89 0 0.513448439 0.577884211 8.527 0 2.15897545 1.173242105 3.448 0 0.383542394 3.943336842 8.064 2.439 0.607934287
    29 0.731578947 3.633 0 0.328437191 0.544347368 5.391 0 0.542399364 1.120842105 7.018 0 1.111825101 3.501894737 14.286 1.907 2.915162389
    30 1.244126316 5.328 0 0.594003731 0.758147368 7.936 0 1.797397199 1.487610526 8.621 0.26 1.719332448 3.210642105 14.286 1.625 3.143315156
    31 0.683431579 8.196 0 1.582727887 1.180294737 12.698 0 3.902364018 2.1322 8.475 0.749 1.728003571 2.836821053 7.812 1.176 0.720062926
    32 1.258768421 4.167 0 0.725367399 1.027021053 3.423 0 1.016975284 1.534968421 9.76 0.643 2.299645399 4.844115789 12.5 2.458 2.36816566
    33 2.321926316 4.172 0 0.578747163 1.229589474 7.692 0 1.093361442 1.864263158 5.882 0.821 0.522960615 4.470610526 13.636 1.819 4.129724048
    34 0.466789474 4.918 0 0.390507977 0.624210526 7.692 0 1.219461598 1.870757895 10.345 0 1.910724562 4.634336842 15.151 3.075 4.837536918
    35 1.349589474 11.291 0 4.185881105 1.217894737 9.091 0 1.612704052 1.347347368 8.093 0 1.289222837 4.082557895 16.371 2.126 5.541035215
    36 0.646515789 4.507 0 0.393450018 1.136873684 10.294 0 2.3590899 0.451578947 3.953 0 0.397298349 3.005568421 12.556 1.103 3.381670477
    37 0.858694737 8.153 0 1.479102823 1.683042105 9.977 0 1.872427451 1.193442105 4.348 0 0.62549391 3.444821053 9.125 1.673 1.944790084
    38 1.339694737 10.205 0 3.066392549 1.400789474 10.294 0 2.069335029 2.502210526 14.681 0.467 4.828091156 3.472568421 14.363 1.682 6.536721487
    39 0.964231579 8.153 0 1.509837673 1.397968421 8.527 0 1.224982178 2.011073684 8.332 0.67 2.725403795 4.579621053 18.055 2.857 6.991261351
    40 0.944263158 11.765 0 3.645862447 1.126715789 4.662 0 1.091607782 1.905094737 14.135 0.064 5.376184002 3.313747368 17.567 1.6 7.222622947
    41 0.623073684 5.883 0 0.726308616 1.463410526 9.201 0 1.636111063 2.689052632 12.308 1.269 4.226300934 3.1354 8.22 0.433 1.937378577
    42 1.624305263 12.857 0 6.116174991 1.105810526 11.606 0 3.472554406 0.533684211 3.048 0 0.321673984 2.938421053 15.069 0.878 6.021628581
    43 0.802463158 8.824 0 2.221980964 2.370957895 4.583 0 0.918427514 1.930631579 10.769 0.803 2.675998801 3.270715789 14.458 0.791 7.208617635
    44 3.703568421 20.29 1.01 15.60775425 1.684178947 7.097 0 0.807247747 0.754894737 6.061 0 0.917008305 4.1112 14.458 2.092 4.326992749
    45 1.600357895 13.044 0 5.104870903 0.573621053 6.667 0 1.19543493 0.541863158 3.03 0 0.219453802 3.062652632 13.125 1.414 3.6734933
    46 2.295252632 13.709 0.316 4.839829452 1.133536842 11.688 0 4.864747807 1.486252632 7.462 0 1.234800315 4.376052632 21.246 1.995 14.94259321
    47 1.455610526 11.807 0 4.270879059 1.198715789 11.688 0 4.712201614 0.786589474 7.353 0 1.165708621 2.774452632 17.35 0.42 8.114272395
    48 2.035347368 14.009 0.593 5.681389069 1.399589474 10.39 0 2.559840726 2.129315789 5.474 0.755 0.410714216 3.695126316 12.5 1.898 5.307067752
    49 1.465947368 10.437 0.15 3.765985945 0.901989474 11.539 0 4.497321758 1.043326316 6.52 0 1.034811841 3.655315789 17.5 1.634 11.83795276
    50 2.352852632 13.487 0.719 5.375030568 1.138389474 11.539 0 4.457905922 0.326136842 2.329 0 0.206708939 5.148631579 21.25 2.671 14.28049076
    51 3.345494737 22.82 0.752 18.9807317 1.272652632 11.19 0 3.581417995 3.188978947 10.315 1.226 1.896858926 2.020694737 9.756 0 2.600074191
    52 1.405452632 14.474 0 7.432012921 1.322115789 17.008 0 10.32702134 1.620389474 7.929 0 1.088246406 5.168610526 18.519 2.987 10.4463775
    53 1.585231579 12.987 0 5.026952283 1.879957895 14.97 0 7.114846714 0.573863158 7.538 0 1.571601971 2.855557895 9.756 1.083 1.530628478
    54 1.495536842 16.883 0 10.9158293 1.391421053 12.561 0 5.912029065 2.333978947 8.527 0.985 1.222208947 2.856515789 15.077 0.463 7.73437185
    55 1.204136842 7.895 0.078 1.168496939 2.381957895 21.725 0 20.9657412 1.349536842 9.333 0 2.314367259 2.984178947 13.426 1.493 6.403251852
    56 3.504084211 21.07 1.415 13.17303465 1.202894737 4.793 0 0.490927736 1.034515789 8 0 1.947421008 3.953378947 15.577 1.984 8.197556109
    57 2.170631579 13.924 0.409 5.115410064 2.040873684 12.791 0 4.554928531 1.706284211 9.211 0.476 2.113594688 4.388852632 17.652 2.521 7.045510652
    58 3.489410526 20.253 1.112 11.78249896 1.651063158 14.943 0 8.907458417 1.037905263 5.194 0 0.680744802 2.848136842 16.092 1.133 7.092554623
    59 3.113421053 12.658 1.283 3.62004837 1.684652632 14.773 0 8.712219616 2.164968421 10.257 1.086 2.888747736 3.044768421 18.181 0.791 10.22278854
    60 1.603336842 11.857 0 4.516401087 1.678 15.73 0 10.41611562 1.058242105 7.792 0.243 1.183817657 3.374484211 16.854 1.385 8.001123429

    大致看来，vgg16和resnet20的效果说得过去，fashion和svhn的效果太差了，还是需要把四个模型仔细地对比一下。
    依次为vgg16、resnet20、fashion、svhn的对比结果


    		 1平均值 2平均值 3平均值 4平均值 1平均值 2平均值 3平均值 4平均值 1平均值 2平均值 3平均值 4平均值 1平均值 2平均值 3平均值 4平均值
    5 1.737526316 30.32834737 6.683368421 0.740589474 2.692663158 24.2646 2.303221053 0.757515789 3.900715789 22.40829474 4.756894737 0.893010526 2.007010526 35.07458947 3.563347368 4.675210526
    6 4.189747368 30.76668421 6.163442105 0.724378947 2.969178947 21.89826316 2.297663158 1.510484211 4.662652632 23.62644211 4.955663158 2.367473684 1.805168421 29.40378947 2.677947368 4.476252632
    7 5.789252632 28.48361053 5.5608 0.751863158 1.447294737 17.58342105 3.569115789 0.777505263 3.352873684 21.20415789 5.742642105 0.530347368 2.762873684 19.83587368 3.427431579 4.159315789
    8 5.859863158 27.88743158 3.736084211 0.567326316 1.529684211 14.7254 3.323431579 0.365147368 2.547694737 12.21869474 4.233947368 1.6382 3.408673684 25.10787368 3.770557895 4.277936842
    9 5.009968421 24.31814737 5.027915789 0.793052632 1.508242105 13.11305263 3.485694737 1.068884211 3.765326316 12.20282105 4.421252632 0.558578947 2.653452632 16.46205263 4.027578947 4.570968421
    10 6.356421053 14.14335789 5.337536842 2.718768421 3.923778947 8.922736842 4.335168421 0.544252632 3.046410526 6.226263158 4.771326316 1.945378947 2.474663158 17.97963158 4.059178947 5.201989474
    11 6.057315789 16.32817895 5.871821053 2.793010526 3.530357895 7.802610526 3.707547368 0.695915789 2.801115789 7.155768421 4.636568421 1.815684211 2.242715789 15.45973684 3.569936842 4.685252632
    12 6.218821053 16.31132632 5.345526316 2.270621053 3.224494737 6.014842105 3.095978947 1.464894737 2.799905263 7.671263158 5.730115789 1.968894737 2.511894737 14.0708 5.271547368 3.598515789
    13 5.7838 13.86188421 4.410221053 3.096010526 3.240789474 3.834978947 2.931031579 2.418515789 2.707357895 6.507915789 5.778757895 1.338231579 2.393873684 13.87044211 3.696652632 4.483852632
    14 5.292389474 14.29063158 3.850684211 1.009368421 2.952778947 3.067263158 3.205431579 1.803557895 2.047084211 4.342178947 5.485305263 2.258936842 2.349326316 14.23810526 4.001842105 2.026631579
    15 5.482852632 13.43101053 4.703505263 0.763252632 2.970463158 2.731947368 2.468936842 1.459736842 2.516852632 2.436115789 5.313821053 2.430084211 2.1884 16.89207368 3.825 2.027284211
    16 5.5298 14.47966316 3.106905263 0.674884211 2.731684211 2.772421053 2.535873684 0.873168421 2.184705263 1.912936842 5.221715789 3.051305263 2.189557895 12.15753684 4.178305263 4.870084211
    17 5.869157895 12.65016842 3.913926316 1.279326316 2.544673684 3.829368421 2.005621053 0.559515789 2.174873684 1.121231579 5.809284211 3.262431579 2.055431579 13.62584211 5.003884211 2.963989474
    18 5.306147368 12.90841053 2.935578947 0.582505263 2.403978947 3.679021053 2.507684211 0.604073684 1.953494737 1.303168421 5.946357895 3.342736842 2.013168421 10.79661053 3.578073684 3.461084211
    19 5.602610526 12.41743158 3.186021053 0.901 2.408873684 4.102336842 2.095821053 0.528884211 1.969442105 3.340263158 5.201010526 3.758852632 1.993431579 11.34854737 5.005063158 3.419684211
    20 5.936473684 14.34192632 2.028705263 0.471821053 2.416663158 4.083873684 2.543263158 0.737126316 2.077442105 1.675852632 5.185115789 2.937747368 2.061147368 12.7972 3.588589474 3.025568421
    21 5.360273684 14.66232632 2.850347368 0.603431579 2.4206 5.205652632 2.805042105 0.396810526 2.044031579 2.749452632 5.219336842 2.510263158 2.044557895 12.03156842 4.811242105 3.653326316
    22 5.3314 14.146 2.703242105 0.596578947 2.26 4.212852632 1.690168421 0.837073684 1.954357895 2.049694737 5.626168421 2.6558 2.095284211 15.50416842 5.717652632 2.755221053
    23 4.845642105 13.80082105 1.960821053 0.9652 2.213589474 3.986557895 2.344442105 0.562694737 2.025368421 1.900778947 3.932431579 3.7862 1.999821053 13.10349474 4.421273684 3.849494737
    24 5.153494737 14.86201053 3.696968421 0.831863158 2.261463158 3.159042105 2.643884211 1.595736842 1.835463158 2.800884211 6.037147368 1.498473684 2.034221053 11.84731579 5.079705263 2.718294737
    25 4.956610526 12.80029474 4.008326316 0.570557895 2.126863158 2.682621053 3.363315789 0.425494737 2.063736842 2.900326316 5.407231579 1.739168421 2.030673684 9.841905263 5.514442105 3.075147368
    26 4.854505263 10.64034737 2.944526316 0.580863158 2.178757895 2.185378947 2.916768421 0.762863158 2.002547368 2.671242105 3.962136842 2.152442105 1.961115789 10.23884211 5.624557895 3.306052632
    27 4.986789474 11.67094737 3.303505263 1.263778947 2.171210526 2.596231579 2.197757895 0.384505263 2.029715789 1.358736842 5.292484211 1.208178947 2.072852632 3.919389474 4.948557895 2.441442105
    28 4.509947368 10.00345263 3.682252632 1.687473684 2.109936842 2.583126316 3.335831579 0.577884211 1.924336842 2.723326316 3.834568421 1.173242105 2.030863158 5.629884211 6.035705263 3.943336842
    29 4.805347368 10.30368421 2.488926316 0.731578947 2.031136842 3.180052632 2.326042105 0.544347368 1.949926316 2.568136842 3.926284211 1.120842105 1.930936842 4.561094737 4.101105263 3.501894737
    30 4.330894737 10.46677895 2.917389474 1.244126316 2.003210526 2.7014 2.507947368 0.758147368 1.797021053 2.843105263 3.184936842 1.487610526 1.932631579 4.061936842 4.677663158 3.210642105
    31 4.334284211 11.42287368 1.841410526 0.683431579 1.9958 3.946305263 2.607831579 1.180294737 1.823368421 1.664663158 3.903978947 2.1322 1.936989474 5.071757895 5.402094737 2.836821053
    32 4.482610526 10.56034737 3.115736842 1.258768421 1.982252632 2.874031579 2.903778947 1.027021053 1.810947368 2.7178 3.608852632 1.534968421 2.048442105 4.397568421 5.328084211 4.844115789
    33 4.242147368 11.37886316 2.812242105 2.321926316 1.995863158 3.625315789 3.521589474 1.229589474 2.035947368 2.508073684 2.343957895 1.864263158 2.251178947 5.243831579 5.699631579 4.470610526
    34 3.8434 9.436021053 3.478126316 0.466789474 1.979031579 2.664252632 3.496968421 0.624210526 1.702621053 2.620157895 4.853410526 1.870757895 2.177315789 4.955326316 5.788294737 4.634336842
    35 3.953010526 10.70486316 2.753368421 1.349589474 2.025252632 3.622305263 2.687231579 1.217894737 1.906021053 2.855968421 3.561421053 1.347347368 2.130031579 5.379726316 4.2988 4.082557895
    36 4.149442105 8.565357895 2.3654 0.646515789 2.096821053 3.182694737 3.375673684 1.136873684 1.820431579 1.709684211 3.276421053 0.451578947 2.254 4.577757895 5.492157895 3.005568421
    37 4.288242105 8.971652632 3.2604 0.858694737 2.152736842 3.435221053 3.3106 1.683042105 1.998189474 1.648305263 3.214294737 1.193442105 2.149557895 3.552042105 4.755178947 3.444821053
    38 3.990568421 8.872736842 3.681736842 1.339694737 2.132357895 3.594084211 2.297221053 1.400789474 2.033126316 2.037252632 3.811431579 2.502210526 1.939736842 3.522084211 4.996989474 3.472568421
    39 4.052821053 10.99558947 1.812221053 0.964231579 2.363536842 2.848515789 2.733526316 1.397968421 1.920821053 3.171442105 3.204410526 2.011073684 2.671515789 2.086747368 4.450810526 4.579621053
    40 4.188284211 6.197347368 1.893926316 0.944263158 2.479557895 2.996705263 2.927021053 1.126715789 1.790652632 2.518442105 2.882884211 1.905094737 1.904273684 3.586915789 4.858894737 3.313747368
    41 4.153031579 9.258094737 2.350105263 0.623073684 2.313631579 3.526789474 3.252263158 1.463410526 2.059210526 3.298021053 2.781031579 2.689052632 2.764063158 4.078210526 5.578821053 3.1354
    42 3.478526316 9.580189474 3.7246 1.624305263 2.737926316 3.274084211 2.762168421 1.105810526 1.771915789 2.276863158 3.257084211 0.533684211 2.535 5.766273684 5.4672 2.938421053
    43 3.417378947 6.382305263 2.879105263 0.802463158 2.700168421 3.640052632 2.557778947 2.370957895 1.991557895 3.228768421 3.832978947 1.930631579 2.744263158 2.930557895 4.844052632 3.270715789
    44 3.626168421 6.006884211 2.164957895 3.703568421 2.512294737 3.800557895 2.663063158 1.684178947 2.363926316 4.312294737 3.886157895 0.754894737 2.579368421 3.538684211 4.648073684 4.1112
    45 3.849421053 5.206894737 2.752031579 1.600357895 2.810831579 3.557515789 2.913136842 0.573621053 1.985789474 3.623010526 3.308578947 0.541863158 2.359 3.446557895 5.330157895 3.062652632
    46 3.3 6.124010526 2.761768421 2.295252632 2.778842105 3.714421053 2.764178947 1.133536842 1.863284211 3.567610526 2.523778947 1.486252632 2.963673684 3.975936842 5.115315789 4.376052632
    47 3.447926316 5.451852632 2.528210526 1.455610526 2.582115789 4.761242105 2.656284211 1.198715789 2.196736842 4.246189474 3.267063158 0.786589474 2.553368421 3.802010526 5.477557895 2.774452632
    48 4.031484211 5.592157895 2.104231579 2.035347368 3.030757895 3.442978947 2.766 1.399589474 2.295536842 2.230547368 3.225347368 2.129315789 2.753442105 3.447621053 4.042326316 3.695126316
    49 3.626294737 5.974105263 2.559989474 1.465947368 2.86 3.406084211 2.547326316 0.901989474 1.838747368 2.243568421 3.667178947 1.043326316 2.9844 3.102189474 4.163168421 3.655315789
    50 2.980610526 5.868094737 2.8328 2.352852632 3.002357895 3.393147368 2.618294737 1.138389474 2.077589474 3.353631579 4.033421053 0.326136842 3.049389474 3.046884211 3.951936842 5.148631579
    51 3.432389474 6.001557895 1.984252632 3.345494737 2.809621053 3.961178947 2.425136842 1.272652632 2.133147368 2.726926316 3.202884211 3.188978947 3.284494737 2.744452632 5.176768421 2.020694737
    52 3.413557895 5.709863158 2.883715789 1.405452632 2.957978947 4.144347368 2.507494737 1.322115789 2.557105263 3.390631579 3.571378947 1.620389474 2.582842105 4.122073684 4.921073684 5.168610526
    53 3.617884211 4.617642105 2.577357895 1.585231579 3.450273684 3.877315789 2.486610526 1.879957895 2.417663158 2.831705263 3.5886 0.573863158 3.412978947 3.399852632 5.048778947 2.855557895
    54 3.545452632 6.564705263 2.477136842 1.495536842 3.242557895 4.4762 2.839663158 1.391421053 2.508842105 2.983842105 2.946126316 2.333978947 2.261663158 5.385073684 4.350473684 2.856515789
    55 3.385105263 6.658263158 2.487052632 1.204136842 3.382305263 4.410421053 2.439757895 2.381957895 2.1818 2.957915789 2.779284211 1.349536842 2.9274 3.210936842 4.612347368 2.984178947
    56 3.280589474 5.887736842 2.875884211 3.504084211 3.166947368 4.782326316 2.042021053 1.202894737 2.506252632 2.485852632 2.529042105 1.034515789 3.273084211 4.170547368 4.473621053 3.953378947
    57 3.549178947 6.290115789 2.836189474 2.170631579 3.276694737 4.570336842 3.056978947 2.040873684 2.752157895 2.616631579 2.957873684 1.706284211 3.003526316 5.293957895 4.759894737 4.388852632
    58 3.295052632 7.274042105 3.731157895 3.489410526 3.094978947 4.145336842 3.187789474 1.651063158 2.874778947 4.257147368 4.260326316 1.037905263 3.887042105 5.183010526 4.592905263 2.848136842
    59 3.040431579 5.953305263 3.232484211 3.113421053 3.2148 4.057989474 3.417789474 1.684652632 2.948115789 2.860021053 3.200273684 2.164968421 3.1278 2.577736842 4.773126316 3.044768421
    60 3.271252632 5.658021053 3.139705263 1.603336842 3.414252632 4.221136842 2.973042105 1.678 2.921042105 3.630736842 3.0182 1.058242105 3.666305263 4.734621053 4.386094737 3.374484211

    可以看到在vgg16和resnet20上case4的优势比较大，因此最优的效果还可以接受，但是对于fashion和svhn，反而case1比较占优势，onehot_refine_200_2000降维后效果相较于不降维有了一定幅度的提升，但是还是没有达到理想的效果。因为使用的SVD降维没有考虑奇异值选取问题，只要降维统一设的参数都是降维至100，之前尝至500的时候效果也是不理想，接下来再做一组对比实验，看这四种情况在降至其他维度时效果会不会有改善，希望能从中找到确定最终维度的方法。

    奇异值平方和 前100奇异值平方和 百分比
    mnist_lenet1 699439.9999999998 599112.1679507367 85.66
    mnist_lenet4 801220.7584010088 742486.7844289693 92.67
    mnist_lenet5 865064.9999999998 647324.7025369342 74.83
    cifar10_vgg16 1200351.0 764782.2132995642 61.71
    cifar10_resnet20 1104252.0 729830.2404693662 66.09
    svhn_lenet5 1812669.0 1253828.6798741636 69.17
    fashion_lenet5 579356.9999999998 412068.1597127195 71.13

    就这个结果看，初步猜想可能是这四种情况下降维幅度较大，接下来针对这四个模型分别降维至平方和的80%、90%、95%观察效果有无改善。


    		 1平均值 2平均值 3平均值 1平均值 2平均值 3平均值 1平均值 2平均值 3平均值 1平均值 2平均值 3平均值
    5 0.581410526 0.765789474 0.798168421 0.798168421 0.782715789 0.787473684 0.865 0.817336842 0.816694737 4.213347368 3.758410526 3.9042
    6 0.538810526 0.461473684 0.445210526 1.577094737 1.602505263 1.606842105 2.374484211 2.345189474 2.329378947 4.229368421 4.434505263 4.513536842
    7 0.649778947 0.615168421 0.620421053 0.768242105 0.807052632 0.808336842 0.524136842 0.573368421 0.565905263 3.671389474 2.075389474 2.124905263
    8 0.597884211 0.678578947 0.668147368 0.358094737 0.395284211 0.396968421 1.495526316 1.414305263 1.3516 2.723536842 2.906263158 2.887989474
    9 1.100631579 1.108694737 1.208368421 1.090284211 1.091705263 1.107547368 0.772578947 1.095273684 1.093326316 3.188789474 3.653789474 3.428368421
    10 2.963536842 3.077105263 3.197115789 0.550536842 0.424715789 0.438305263 0.775042105 0.968484211 1.849094737 4.3928 4.513315789 4.277831579
    11 2.276631579 2.811768421 2.835915789 0.542905263 0.691852632 0.686326316 0.434442105 1.758547368 0.453831579 3.241189474 4.269431579 3.396568421
    12 2.455484211 2.540094737 2.475357895 1.195115789 1.617221053 1.687463158 1.872884211 1.395473684 1.457484211 2.858221053 2.933894737 3.436378947
    13 0.371968421 3.154684211 3.195936842 1.269305263 2.295178947 2.401326316 1.187115789 1.383705263 1.396221053 3.698757895 4.532705263 4.384063158
    14 0.958368421 1.412631579 1.285905263 2.044905263 1.009473684 0.958221053 2.492494737 1.776694737 1.760031579 2.664473684 4.157989474 4.718357895
    15 1.579715789 1.7544 1.494642105 1.388126316 2.636957895 2.697831579 2.971094737 2.744326316 2.758473684 5.176263158 4.4612 2.134978947
    16 1.969705263 2.372442105 1.928347368 1.259126316 1.434747368 0.521221053 2.563189474 3.615210526 3.693378947 1.944842105 2.499905263 4.276010526
    17 0.886589474 0.501715789 0.519526316 1.922284211 1.911894737 0.487073684 2.736221053 2.262242105 2.261126316 2.439652632 1.429821053 2.5352
    18 0.880989474 1.170652632 1.320210526 1.068368421 0.767484211 1.288410526 3.023157895 1.993105263 2.569568421 2.294547368 4.085136842 4.476894737
    19 1.646347368 1.082421053 2.077610526 1.229494737 0.601252632 0.616505263 3.330536842 1.563421053 2.073578947 2.083126316 2.597063158 1.745315789
    20 0.819842105 0.468968421 0.729115789 1.183631579 0.829694737 0.792642105 3.086768421 3.391305263 2.705557895 1.940294737 1.515768421 2.111452632
    21 0.777494737 0.621884211 0.866168421 0.316063158 0.617526316 0.590021053 3.116210526 1.130915789 2.248147368 2.408157895 5.491852632 3.5086
    22 1.547968421 1.406315789 1.363368421 0.309810526 0.521368421 0.4888 2.605178947 2.992094737 2.977926316 3.1366 2.537715789 2.679294737
    23 0.323515789 1.7448 0.576831579 0.782178947 0.851673684 0.370157895 0.832821053 1.208747368 1.463484211 3.2494 2.437210526 2.221842105
    24 1.398389474 0.725663158 0.631705263 0.852052632 1.009421053 1.198631579 2.020536842 1.549105263 2.400873684 2.308589474 2.778168421 2.662231579
    25 0.285221053 0.740115789 1.085084211 0.749326316 1.337210526 0.7008 3.385915789 2.143021053 2.125063158 3.343515789 3.095252632 3.099242105
    26 0.712063158 0.435526316 1.072884211 0.447157895 1.003768421 0.612589474 2.567157895 2.078252632 2.927273684 2.407147368 2.235063158 2.667536842
    27 0.714684211 0.858263158 1.439336842 0.479915789 1.180221053 0.971673684 1.472631579 3.523210526 1.691673684 2.472705263 1.978894737 3.334073684
    28 0.449863158 0.705115789 1.244084211 1.539557895 1.554663158 2.027726316 1.211347368 1.552378947 0.992484211 2.469221053 3.220589474 3.110526316
    29 0.930873684 0.869842105 0.906715789 0.978115789 0.924252632 1.164431579 2.199778947 1.803663158 2.687631579 3.121031579 4.930778947 4.014421053
    30 0.883684211 0.900052632 0.874147368 0.576378947 1.723863158 1.723894737 1.368842105 1.7424 1.769368421 3.416021053 4.263126316 3.293852632
    31 1.058389474 1.632421053 0.819368421 1.140210526 1.307747368 1.230052632 2.068305263 2.270663158 1.498210526 2.166463158 5.091115789 0.877884211
    32 0.485378947 1.135115789 0.639063158 0.572284211 0.466631579 0.523884211 1.404010526 3.320473684 2.561084211 5.146757895 4.966168421 4.222863158
    33 1.053705263 1.489252632 1.587810526 1.934463158 1.279968421 0.689273684 1.895389474 1.601957895 2.7256 2.202168421 5.163852632 2.945347368
    34 0.6382 0.878842105 1.686073684 1.7054 0.753957895 1.622431579 1.152694737 1.565947368 1.723242105 4.167663158 1.901978947 2.546484211
    35 0.863273684 0.895210526 0.471084211 0.905389474 1.668157895 0.652273684 0.607242105 2.124715789 1.430957895 5.074010526 3.820715789 3.977578947
    36 0.938610526 0.985105263 1.439452632 0.902210526 0.814326316 0.501463158 2.015757895 1.760126316 1.584336842 3.431694737 2.313336842 5.785252632
    37 0.964663158 0.446684211 1.760231579 1.520673684 1.4676 1.147915789 0.731768421 2.866210526 1.060168421 1.766115789 4.419789474 1.878073684
    38 1.495326316 1.730273684 0.744410526 1.009884211 0.574810526 1.119642105 1.341842105 2.319147368 2.289052632 3.388905263 4.335252632 2.787284211
    39 0.544368421 0.857526316 0.601221053 1.141989474 1.205326316 1.026473684 0.961557895 1.361536842 1.7502 4.457705263 4.337936842 4.168947368
    40 0.903242105 1.528621053 0.729452632 1.701263158 0.968631579 1.343368421 0.671821053 1.1462 2.411684211 4.284968421 1.921294737 4.245578947
    41 1.140347368 1.272705263 0.911221053 1.409642105 1.573757895 1.8434 1.423136842 0.7222 0.970305263 6.0804 3.532989474 3.234178947
    42 1.479178947 1.591694737 1.047821053 0.828831579 0.554094737 1.259105263 1.686231579 1.139421053 1.480273684 2.719547368 1.770336842 4.226884211
    43 1.367547368 1.056736842 1.096968421 0.842705263 1.173431579 1.162136842 0.809863158 0.597936842 0.6948 5.089578947 4.069084211 2.959810526
    44 2.629431579 1.026789474 0.730652632 0.562863158 1.963526316 1.227705263 1.114389474 0.672084211 0.843042105 4.000010526 1.609526316 3.419705263
    45 0.523315789 0.757505263 0.879915789 1.748789474 1.252884211 0.969 1.468673684 0.888926316 1.284136842 4.845673684 5.940326316 2.905484211
    46 1.209947368 1.110231579 1.583263158 1.575252632 0.977684211 1.730747368 2.137210526 2.425578947 0.519768421 6.808621053 4.186694737 1.323042105
    47 2.654673684 1.262821053 0.955368421 1.171684211 1.3392 0.828989474 1.343073684 0.961684211 1.953915789 6.097957895 2.207715789 4.315305263
    48 1.576810526 1.524768421 2.517326316 1.144631579 1.010747368 2.1258 0.575231579 0.592073684 0.637315789 2.703915789 3.806347368 4.070357895
    49 1.757231579 0.896189474 2.199410526 1.276663158 1.644378947 1.147284211 0.479873684 2.827831579 1.7286 4.794421053 3.399863158 2.744284211
    50 0.915726316 0.808242105 1.509926316 1.410536842 1.781589474 2.529831579 0.550178947 1.971084211 0.525210526 3.303431579 5.468168421 4.068831579
    51 1.090789474 0.861589474 1.3676 0.967347368 1.497305263 1.365042105 0.688757895 0.944757895 1.226915789 2.861063158 5.795947368 5.300547368
    52 1.332136842 1.951568421 2.812105263 1.405652632 1.178031579 1.093715789 1.334305263 0.876431579 0.831 4.971210526 2.457810526 3.100105263
    53 1.041621053 1.016473684 1.154736842 1.415031579 1.498178947 1.509326316 0.6426 1.198789474 1.447926316 3.904547368 5.661294737 2.155273684
    54 1.655031579 2.237789474 0.899821053 1.8372 0.792263158 1.417557895 0.805726316 0.844842105 1.172042105 4.356252632 3.486536842 2.249568421
    55 1.634178947 0.628684211 1.262126316 1.294357895 1.130547368 1.9228 0.716789474 1.391315789 1.085252632 2.466547368 2.896505263 4.441136842
    56 2.066052632 1.839515789 1.577505263 2.058378947 1.337115789 1.963947368 2.071715789 2.439968421 0.849905263 4.067221053 5.374126316 3.669157895
    57 3.153905263 0.652431579 0.821063158 1.251810526 1.234021053 1.588357895 0.896684211 1.678378947 0.344294737 4.495526316 5.831831579 3.873957895
    58 3.010778947 1.428578947 1.692147368 2.269705263 1.656631579 0.9258 1.756221053 0.889031579 0.443294737 3.809778947 3.233326316 3.615526316
    59 1.084010526 3.056421053 1.859136842 1.584273684 1.210915789 1.777578947 0.551989474 1.308926316 1.165652632 4.483252632 5.103389474 5.187821053
    60 1.882694737 1.694684211 2.308347368 1.365263158 2.714042105 0.925589474 1.181357895 0.569568421 1.002421053 3.628547368 4.788831579 5.609505263

    依这个结果来看，似乎降维的幅度对效果的影响不是很大，降至80% 90% 95%的情况里很难说有哪一种是有很明显的优势，在mnist_lenet145上也是一样。


    		 1平均值 2平均值 3平均值
    		1平均值 2平均值 3平均值
    		1平均值 2平均值 3平均值
    5 1.004494737 1.034136842 1.033947368
    		0.553105263 0.531021053 0.531715789
    		0.764915789 0.764915789 0.768031579
    6 0.718273684 0.706694737 0.741747368
    		0.746231579 0.7366 0.738452632
    		0.744894737 0.749326316 0.749326316
    7 1.216631579 1.198821053 0.692526316
    		0.539947368 0.330347368 0.323126316
    		0.377010526 0.385505263 0.418494737
    8 0.912431579 0.450568421 0.810368421
    		0.443052632 0.194231579 0.194231579
    		0.382073684 0.614915789 0.271294737
    9 0.402894737 1.158221053 0.535705263
    		0.626652632 0.636410526 0.634610526
    		0.524978947 0.527242105 0.544042105
    10 0.711315789 0.801852632 0.856494737
    		0.651852632 0.654768421 0.698168421
    		0.572410526 0.5318 0.5318
    11 0.505578947 0.850431579 0.448705263
    		0.342968421 0.356568421 0.186831579
    		0.357905263 0.469705263 0.475905263
    12 0.506105263 1.001715789 0.451115789
    		0.257705263 0.292978947 0.290231579
    		0.356621053 0.179768421 0.468105263
    13 0.463873684 0.532326316 0.719978947
    		0.151378947 0.210568421 0.254989474
    		0.455652632 0.506821053 0.213926316
    14 0.548294737 0.444978947 0.5178
    		0.418305263 0.207378947 0.547705263
    		0.275378947 0.174494737 0.1446
    15 0.4016 0.318305263 0.4584
    		0.463894737 0.694378947 0.598905263
    		0.470715789 0.359431579 0.426873684
    16 0.347831579 0.793136842 0.361705263
    		0.255663158 0.654168421 0.274610526
    		0.181684211 0.411978947 0.345294737
    17 0.750484211 0.891557895 0.662873684
    		1.017357895 0.461610526 0.188242105
    		0.432178947 0.341452632 0.345989474
    18 0.5592 0.572663158 0.434284211
    		0.677526316 0.368936842 0.555305263
    		0.284410526 0.244821053 0.455789474
    19 0.357010526 0.717463158 0.830105263
    		0.559 0.225568421 0.424326316
    		0.373778947 0.373210526 0.440915789
    20 1.204894737 0.293294737 0.999894737
    		0.8736 0.201305263 0.344726316
    		0.115610526 0.4756 0.437136842
    21 0.2876 0.2532 0.344442105
    		0.611273684 0.764063158 0.754178947
    		0.431063158 0.513147368 0.181094737
    22 0.411778947 0.591021053 0.529115789
    		0.648894737 0.810726316 0.410673684
    		0.274947368 0.501757895 0.657442105
    23 0.281315789 0.937842105 0.261915789
    		0.792789474 0.830031579 0.647263158
    		0.546831579 0.487105263 0.415031579
    24 0.550631579 0.912652632 0.858894737
    		1.053389474 0.572621053 0.124884211
    		0.476789474 0.314968421 0.474589474
    25 0.598052632 0.323842105 0.327284211
    		0.620852632 0.352926316 0.527547368
    		0.377273684 0.637978947 0.654652632
    26 0.394505263 0.3384 0.899305263
    		1.310136842 1.041147368 0.949505263
    		0.551642105 0.781294737 0.776726316
    27 0.941652632 0.586989474 0.319715789
    		1.453926316 0.7964 1.2198
    		0.420526316 0.756094737 0.460315789
    28 0.367852632 0.329178947 0.540947368
    		0.846094737 0.332568421 1.817642105
    		0.378263158 0.369494737 0.668852632
    29 1.050010526 0.430494737 1.166221053
    		0.576357895 0.180463158 1.127147368
    		0.801368421 0.692621053 0.621168421
    30 0.712063158 0.549178947 0.889831579
    		1.479336842 1.025536842 0.930473684
    		0.707 0.7826 0.600610526
    31 0.743705263 0.734747368 0.567389474
    		0.659631579 1.058936842 0.431726316
    		0.625621053 0.604536842 0.776052632
    32 0.695894737 0.930578947 0.568084211
    		0.259326316 1.308147368 0.733926316
    		0.906705263 0.674557895 0.706378947
    33 0.550473684 0.774063158 0.753610526
    		0.370315789 0.362821053 0.735136842
    		0.680757895 0.888347368 0.614789474
    34 0.7444 0.751642105 0.588357895
    		0.467084211 0.612389474 0.930536842
    		0.373410526 0.498 0.309894737
    35 0.484515789 1.161126316 0.3076
    		1.460736842 0.892094737 0.295673684
    		0.633084211 0.849536842 0.432557895
    36 0.708515789 0.536147368 0.332157895
    		0.338305263 0.785936842 0.623747368
    		0.3342 0.572410526 0.315031579
    37 0.553515789 0.622221053 0.5342
    		1.156242105 0.366252632 0.694568421
    		0.226831579 0.387115789 0.247863158
    38 0.311378947 0.902684211 0.679842105
    		0.948189474 0.813063158 0.827305263
    		0.3768 0.386263158 0.392747368
    39 0.651673684 0.629305263 1.106989474
    		0.603726316 0.259578947 0.701
    		0.556136842 0.478242105 0.309736842
    40 1.431526316 0.356315789 1.080505263
    		1.162631579 0.448231579 0.650210526
    		0.354936842 0.289652632 0.324273684
    41 0.402052632 0.761157895 0.954389474
    		0.8004 0.580884211 0.580884211
    		0.158157895 0.106252632 0.442789474
    42 0.729484211 1.282242105 0.559063158
    		1.306084211 1.192884211 0.352178947
    		0.430673684 0.278010526 0.376136842
    43 0.479378947 1.256736842 0.510263158
    		0.535052632 0.823221053 0.842873684
    		0.477031579 0.295189474 0.205515789
    44 0.332421053 0.648915789 1.059663158
    		2.026884211 0.663157895 0.598726316
    		0.475842105 0.552242105 0.394178947
    45 0.409442105 0.912357895 0.903894737
    		0.386642105 1.073031579 0.750484211
    		0.119336842 0.282684211 0.391536842
    46 0.650452632 1.525757895 1.247610526
    		0.325326316 1.013589474 1.517968421
    		0.261547368 0.398894737 0.669821053
    47 0.862210526 0.671431579 1.284326316
    		0.6668 0.602210526 0.465663158
    		0.277463158 0.433652632 0.219663158
    48 0.393642105 0.714031579 0.505336842
    		0.679263158 0.335536842 0.608210526
    		0.209263158 1.065578947 0.260589474
    49 1.038778947 0.531294737 0.466484211
    		0.487494737 0.372063158 0.708242105
    		0.4472 0.577873684 0.596968421
    50 0.482052632 0.582357895 1.342821053
    		1.002642105 0.719515789 1.543821053
    		0.414652632 0.221652632 0.282031579
    51 0.843221053 0.804484211 1.523905263
    		0.516557895 0.524884211 0.676831579
    		0.419126316 0.324568421 0.527789474
    52 1.141336842 0.98 1.209421053
    		0.509126316 0.421915789 0.544
    		0.669157895 0.278357895 0.384505263
    53 0.921968421 0.660852632 1.220747368
    		0.294957895 0.796673684 0.360021053
    		0.294042105 0.421147368 0.429736842
    54 1.467884211 1.315031579 1.335884211
    		0.701010526 0.872926316 0.580757895
    		0.540389474 0.477821053 0.388452632
    55 0.485663158 0.995031579 0.976673684
    		0.445315789 0.758673684 1.112168421
    		0.962726316 0.4834 0.584831579
    56 0.896842105 0.707147368 0.666231579
    		1.503957895 0.347084211 1.433
    		0.472357895 0.672463158 0.459473684
    57 0.911947368 1.010926316 0.504947368
    		0.889063158 0.914884211 0.474494737
    		0.532873684 0.452 0.617315789
    58 1.008336842 0.9966 0.945873684
    		0.588842105 0.626578947 0.791715789
    		0.429494737 0.933263158 0.521063158
    59 1.351936842 1.642631579 0.741631579
    		0.759347368 0.476484211 0.435242105
    		0.546168421 0.506873684 0.351568421
    60 1.238736842 0.805652632 1.396315789
    		0.447442105 0.505126316 0.433936842
    		0.669557895 0.668705263 1.06