MLP

MLP 可在 預測及決策樹狀結構中使用。

MLP 程序適合稱為多層感知器的特定類神經網路。 多層感知器使用前饋架構，並且可以具有多個隱藏層。 這是最常使用神經網路結構之一。

MLP dependent variable [(MLEVEL = {S})] [dependent variable...]
                                  {O}
                                  {N}

    [BY factor list] [WITH covariate list]

[/EXCEPT VARIABLES = varlist]

[/RESCALE [COVARIATE = {STANDARDIZED**}]
                       {NORMALIZED    }
                       {ADJNORMALIZED }
                       {NONE          }

          [DEPENDENT = {STANDARDIZED                         }]]
                       {NORMALIZED    [(CORRECTION = {0.02**})]}
                                                     {number}
                       {ADJNORMALIZED [(CORRECTION = {0.02**})]}
                                                     {number}
                       {NONE                                 }

[/PARTITION {TRAINING = {70**   } TESTING = {30**   } HOLDOUT = {0**    }}]
                        {integer}           {integer}           {integer}
            {VARIABLE = varname                                       }

[/ARCHITECTURE [AUTOMATIC = {YES**} [(MINUNITS = {1**    }, MAXUNITS = {50**   })]]
                                                 {integer}             {integer}
                            {NO   }

               [HIDDENLAYERS = {1** [(NUMUNITS = {AUTO** })]       }]
                                                 {integer}
                               {2 [(NUMUNITS = {AUTO**          })]}
                                               {integer, integer}
               [HIDDENFUNCTION = {TANH** }]   [OUTPUTFUNCTION = {IDENTITY}]]
                                 {SIGMOID}                      {SIGMOID }
                                                                {SOFTMAX }
                                                                {TANH    }

[/CRITERIA [TRAINING = {BATCH**  }]   [MINIBATCHSIZE = {AUTO** }]
                       {ONLINE   }                     {integer}
                       {MINIBATCH}

           [MEMSIZE = {1000** }]      [OPTIMIZATION = {GRADIENTDESCENT}]
                      {integer}                       {SCALEDCONJUGATE}

           [LEARNINGINITIAL = {0.4** }]   [LEARNINGLOWER = {0.001**}]
                              {number}                     {number }

           [MOMENTUM = {0.9** }]   [LEARNINGEPOCHS = {10**   }]
                       {number}                      {integer}

           [LAMBDAINITIAL = {0.0000005**}]   [SIGMAINITIAL = {0.00005**}]
                            {number     }                    {number   }

           [INTERVALCENTER = {0**   }]    [INTERVALOFFSET = {0.5** }]]
                             {number}                       {number}

[/STOPPINGRULES [ERRORSTEPS = {1**    } [(DATA = {AUTO** })]]
                              {integer}          {BOTH   }

                [TRAININGTIMER = {ON**} [(MAXTIME = {15**  })]]
                                 {OFF }             {number}

                [MAXEPOCHS = {AUTO** }
                             {integer}]

                [ERRORCHANGE = {0.0001**}]    [ERRORRATIO = {0.001**}]]
                               {number  }                   {number }

[/MISSING USERMISSING = {EXCLUDE**}]
                        {INCLUDE  }

[/PRINT [CPS**]  [NETWORKINFO**]  [SUMMARY**]  [CLASSIFICATION**]
        [SOLUTION]  [IMPORTANCE]  [NONE]]

[/PLOT [NETWORK**]  [PREDICTED]  [RESIDUAL]  [ROC]
       [GAIN]  [LIFT]  [NONE]]

[/SAVE [PREDVAL[(varname [varname...])]]

       [PSEUDOPROB[(rootname[:{25     }] [rootname...])]]]
                            {integer}

[/OUTFILE [MODEL = 'file' ['file'...]]]

**如果省略了次指令或關鍵字，則為預設值。

此指令會讀取作用中資料集，並導致執行任何擱置指令。 如需相關資訊，請參閱主題 指令順序 。

MLP 指令的語法可以從 多層感知器 對話框產生。

發行歷程

版本 16.0

• 已建立指令。

範例

MLP dep_var BY A B C WITH X Y Z.