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<p class="MsoNormal"><span lang="EN-US" style="font-size:10.5pt;font-family:"Calibri","sans-serif";color:#1F497D">Hi all</span><span style="font-size:10.5pt;color:#1F497D">：</span><span lang="EN-US" style="font-size:10.5pt;font-family:"Calibri","sans-serif";color:#1F497D"><o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-size:10.5pt;font-family:"Calibri","sans-serif";color:#1F497D"> I uses a ‘Car Evaluation’ dataset from
<a href="http://archive.ics.uci.edu/ml/machine-learning-databases/car/car.data">http://archive.ics.uci.edu/ml/machine-learning-databases/car/car.data</a> to test the effect of MLP. (I transfer some class in the data to digit value, e.g. ‘low’ to 1 ‘med’ to
2, ‘high ’to 3, the final dataset’s input is 6 dimension, output label is 4 dimension)<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-size:10.5pt;font-family:"Calibri","sans-serif";color:#1F497D"> However, the accuracy rate is not satisfied comparing to the result in Matlab which use BP algorithm too, I wonder if I should tune the
parameter of MLP for better?<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-size:10.5pt;font-family:"Calibri","sans-serif";color:#1F497D"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-size:10.5pt;font-family:"Calibri","sans-serif";color:#1F497D">Attachment:<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-size:10.5pt;font-family:"Calibri","sans-serif";color:#1F497D"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-size:10.5pt;font-family:"Calibri","sans-serif";color:#1F497D">main code in matlab: accuracy 100% after train<o:p></o:p></span></p>
<p class="MsoNormal" style="text-indent:35.0pt;text-autospace:none"><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:black">net=newff([-1 1;-1 1;-1 1;-1 1;-1 1;-1 1;],[10 4],{</span><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:#A020F0">'tansig'</span><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:black">,</span><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:#A020F0">'logsig'</span><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:black">},</span><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:#A020F0">'trainlm'</span><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:black">);<o:p></o:p></span></p>
<p class="MsoNormal" style="text-indent:42.0pt;text-autospace:none"><span lang="EN-US" style="font-family:"Courier New""><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-size:10.5pt;font-family:"Calibri","sans-serif";color:#1F497D">main code in MLP Code: accuracy 70% after fit<o:p></o:p></span></p>
<p class="MsoNormal" style="text-indent:21.0pt"><span lang="EN-US" style="font-size:10.5pt;font-family:"Calibri","sans-serif";color:#1F497D">clf = MLPClassifier(solver='sgd', activation='logistic', max_iter=2000, learning_rate='adaptive',warm_start = True)<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-size:10.5pt;font-family:"Calibri","sans-serif";color:#1F497D"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-size:10.5pt;font-family:"Calibri","sans-serif";color:#1F497D"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-size:10.5pt;font-family:"Calibri","sans-serif";color:#1F497D"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-size:10.5pt;font-family:"Calibri","sans-serif";color:#1F497D"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-size:10.5pt;font-family:"Calibri","sans-serif";color:#1F497D"><o:p> </o:p></span></p>
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