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    <title>MATLAB Central Newsreader - how to specify initial weight &amp; bias</title>
    <description>Feed for thread: how to specify initial weight &amp; bias</description>
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    <item>
      <pubDate>Wed, 04 Nov 2009 08:40:08 -0500</pubDate>
      <title>how to specify initial weight &amp; bias</title>
      <link>http://www.mathworks.com/matlabcentral/newsreader/view_thread/264934#691960</link>
      <author>aurikel Radzali</author>
      <description>I am a student who work on prediction of protein concentration using neural network. When i'm using mlp/feedforward neural network, the result vary every time I run the simulation. Someone said that it happen because in the training, the initial value for the weight and bias are set randomly (default) by the Matlab. I can specify it if i want. Does anyone know how to specify the weight &amp; bias? &lt;br&gt;
&lt;br&gt;
&lt;br&gt;
AR=[11];   % [hidden node]&lt;br&gt;
PR=[minmax(P)];&lt;br&gt;
net=newff(P,T,AR,{'tansig'  'purelin'}, 'trainlm');&lt;br&gt;
net.iw{1,1}&lt;br&gt;
net.b{1}</description>
    </item>
    <item>
      <pubDate>Thu, 05 Nov 2009 01:22:15 -0500</pubDate>
      <title>Re: how to specify initial weight &amp; bias</title>
      <link>http://www.mathworks.com/matlabcentral/newsreader/view_thread/264934#692251</link>
      <author>Greg Heath</author>
      <description>On Nov 4, 3:40&#160;am, &quot;aurikel Radzali&quot; &amp;lt;suriar...@gmail.com&amp;gt; wrote:&lt;br&gt;
&amp;gt; I am a student who work on prediction of protein concentration using neural network. When i'm using mlp/feedforward neural network, the result vary every time I run the simulation. Someone said that it happen because in the training, the initial value for the weight and bias are set randomly (default) by the Matlab. I can specify it if i want. Does anyone know how to specify the weight &amp; bias?&lt;br&gt;
&lt;br&gt;
Yes. However stick to the MATLAB default. To reproduce runs&lt;br&gt;
reset the random generator to the same state.&lt;br&gt;
&lt;br&gt;
state0 = 2009&lt;br&gt;
rand('state',state0)&lt;br&gt;
&amp;gt; AR=[11]; &#160; % [hidden node]&lt;br&gt;
&amp;gt; PR=[minmax(P)];&lt;br&gt;
&amp;gt; net=newff(P,T,AR,{'tansig'  'purelin'}, 'trainlm');&lt;br&gt;
&lt;br&gt;
net = newff(P,T,AR); % Using defaults&lt;br&gt;
&lt;br&gt;
&amp;gt; net.iw{1,1}&lt;br&gt;
&amp;gt; net.b{1}&lt;br&gt;
&lt;br&gt;
net.lw{2,1}&lt;br&gt;
&lt;br&gt;
Hope this helps.&lt;br&gt;
&lt;br&gt;
Greg</description>
    </item>
    <item>
      <pubDate>Thu, 05 Nov 2009 03:00:22 -0500</pubDate>
      <title>Re: how to specify initial weight &amp; bias</title>
      <link>http://www.mathworks.com/matlabcentral/newsreader/view_thread/264934#692265</link>
      <author>aurikel Radzali</author>
      <description>Greg Heath &amp;lt;heath@alumni.brown.edu&amp;gt; wrote in message &amp;lt;6a37872f-758d-4926-a45d-6b3ff5b7fc50@h2g2000vbd.googlegroups.com&amp;gt;...&lt;br&gt;
&amp;gt; On Nov 4, 3:40?am, &quot;aurikel Radzali&quot; &amp;lt;suriar...@gmail.com&amp;gt; wrote:&lt;br&gt;
&amp;gt; &amp;gt; I am a student who work on prediction of protein concentration using neural network. When i'm using mlp/feedforward neural network, the result vary every time I run the simulation. Someone said that it happen because in the training, the initial value for the weight and bias are set randomly (default) by the Matlab. I can specify it if i want. Does anyone know how to specify the weight &amp; bias?&lt;br&gt;
&amp;gt; &lt;br&gt;
&amp;gt; Yes. However stick to the MATLAB default. To reproduce runs&lt;br&gt;
&amp;gt; reset the random generator to the same state.&lt;br&gt;
&amp;gt; &lt;br&gt;
&amp;gt; state0 = 2009&lt;br&gt;
&amp;gt; rand('state',state0)&lt;br&gt;
&amp;gt; &amp;gt; AR=[11]; ? % [hidden node]&lt;br&gt;
&amp;gt; &amp;gt; PR=[minmax(P)];&lt;br&gt;
&amp;gt; &amp;gt; net=newff(P,T,AR,{'tansig'  'purelin'}, 'trainlm');&lt;br&gt;
&amp;gt; &lt;br&gt;
&amp;gt; net = newff(P,T,AR); % Using defaults&lt;br&gt;
&amp;gt; &lt;br&gt;
&amp;gt; &amp;gt; net.iw{1,1}&lt;br&gt;
&lt;br&gt;
&lt;br&gt;
Thanks, it really help..&lt;br&gt;
&amp;gt; &amp;gt; net.b{1}&lt;br&gt;
&amp;gt; &lt;br&gt;
&amp;gt; net.lw{2,1}&lt;br&gt;
&amp;gt; &lt;br&gt;
&amp;gt; Hope this helps.&lt;br&gt;
&amp;gt; &lt;br&gt;
&amp;gt; Greg</description>
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