📄 face.nn
字号:
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-0.3049422.967176-1.3026890.1862730.6331916.5467892.883735-2.471193-0.057237-0.122077-1.254956 net = train2(net,{face;nface},1000,'traingdx');TRAINGDX-calcgrad, Epoch 0/1000, MSE 0.0380841/0, Gradient 0.0161943/1e-006TRAINGDX-calcgrad, Epoch 25/1000, MSE 0.0379897/0, Gradient 0.0160989/1e-006TRAINGDX-calcgrad, Epoch 50/1000, MSE 0.0377002/0, Gradient 0.0157962/1e-006TRAINGDX-calcgrad, Epoch 75/1000, MSE 0.0367918/0, Gradient 0.0148357/1e-006TRAINGDX-calcgrad, Epoch 100/1000, MSE 0.0342653/0, Gradient 0.0131359/1e-006TRAINGDX-calcgrad, Epoch 125/1000, MSE 0.0288224/0, Gradient 0.00899192/1e-006TRAINGDX-calcgrad, Epoch 150/1000, MSE 0.0195839/0, Gradient 0.00661213/1e-006TRAINGDX-calcgrad, Epoch 175/1000, MSE 0.0114634/0, Gradient 0.00248487/1e-006TRAINGDX-calcgrad, Epoch 200/1000, MSE 0.00730671/0, Gradient 0.00141872/1e-006TRAINGDX-calcgrad, Epoch 225/1000, MSE 0.00619382/0, Gradient 0.0139478/1e-006TRAINGDX-calcgrad, Epoch 250/1000, MSE 0.00552043/0, Gradient 0.00225788/1e-006TRAINGDX-calcgrad, Epoch 275/1000, MSE 0.00506328/0, Gradient 0.00117316/1e-006TRAINGDX-calcgrad, Epoch 300/1000, MSE 0.00394131/0, Gradient 0.000806827/1e-006TRAINGDX-calcgrad, Epoch 325/1000, MSE 0.00304252/0, Gradient 0.00243748/1e-006TRAINGDX-calcgrad, Epoch 350/1000, MSE 0.00297078/0, Gradient 0.00102562/1e-006TRAINGDX-calcgrad, Epoch 375/1000, MSE 0.0028294/0, Gradient 0.00061697/1e-006TRAINGDX-calcgrad, Epoch 400/1000, MSE 0.00243267/0, Gradient 0.000526016/1e-006TRAINGDX-calcgrad, Epoch 425/1000, MSE 0.00187066/0, Gradient 0.00242446/1e-006TRAINGDX-calcgrad, Epoch 450/1000, MSE 0.00183145/0, Gradient 0.000864909/1e-006TRAINGDX-calcgrad, Epoch 475/1000, MSE 0.0017861/0, Gradient 0.000520862/1e-006TRAINGDX-calcgrad, Epoch 500/1000, MSE 0.00167154/0, Gradient 0.000361654/1e-006TRAINGDX-calcgrad, Epoch 525/1000, MSE 0.00136531/0, Gradient 0.000335621/1e-006TRAINGDX-calcgrad, Epoch 550/1000, MSE 0.00132639/0, Gradient 0.00278421/1e-006TRAINGDX-calcgrad, Epoch 575/1000, MSE 0.00128443/0, Gradient 0.000823477/1e-006TRAINGDX-calcgrad, Epoch 600/1000, MSE 0.00123788/0, Gradient 0.000294566/1e-006TRAINGDX-calcgrad, Epoch 625/1000, MSE 0.00110702/0, Gradient 0.000240032/1e-006TRAINGDX-calcgrad, Epoch 650/1000, MSE 0.000992816/0, Gradient 0.00162968/1e-006TRAINGDX-calcgrad, Epoch 675/1000, MSE 0.000973248/0, Gradient 0.000397771/1e-006TRAINGDX-calcgrad, Epoch 700/1000, MSE 0.000951908/0, Gradient 0.000277682/1e-006TRAINGDX-calcgrad, Epoch 725/1000, MSE 0.000891638/0, Gradient 0.000194585/1e-006TRAINGDX-calcgrad, Epoch 750/1000, MSE 0.000745773/0, Gradient 0.000971981/1e-006TRAINGDX-calcgrad, Epoch 775/1000, MSE 0.000738705/0, Gradient 0.000281221/1e-006TRAINGDX-calcgrad, Epoch 800/1000, MSE 0.000731111/0, Gradient 0.000223325/1e-006TRAINGDX-calcgrad, Epoch 825/1000, MSE 0.000708551/0, Gradient 0.0001534/1e-006TRAINGDX-calcgrad, Epoch 850/1000, MSE 0.000642279/0, Gradient 0.000133356/1e-006TRAINGDX-calcgrad, Epoch 875/1000, MSE 0.000624203/0, Gradient 0.00193949/1e-006TRAINGDX-calcgrad, Epoch 900/1000, MSE 0.000604328/0, Gradient 0.000337345/1e-006TRAINGDX-calcgrad, Epoch 925/1000, MSE 0.000593177/0, Gradient 0.000146603/1e-006TRAINGDX-calcgrad, Epoch 950/1000, MSE 0.000560221/0, Gradient 0.000115208/1e-006TRAINGDX-calcgrad, Epoch 975/1000, MSE 0.000535758/0, Gradient 0.00183329/1e-006TRAINGDX-calcgrad, Epoch 1000/1000, MSE 0.000518507/0, Gradient 0.000550508/1e-006TRAINGDX, Maximum epoch reached, performance goal was not met. train set: 893 17154 se: 99.55 sp: 100.00 pp: 100.00 np: 99.98 ac: 99.98 er: 0.000519 validation set: 447 8578 se: 96.42 sp: 99.90 pp: 97.95 np: 99.81 ac: 99.72 er: 0.001824 test set: 447 8578 se: 97.32 sp: 99.90 pp: 97.97 np: 99.86 ac: 99.77 er: 0.001746⌨️ 快捷键说明
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