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| author | wolffd |
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| date | Tue, 10 Feb 2015 15:05:51 +0000 |
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| 1 <html> | |
| 2 <head> | |
| 3 <title> | |
| 4 Netlab Reference Manual demev1 | |
| 5 </title> | |
| 6 </head> | |
| 7 <body> | |
| 8 <H1> demev1 | |
| 9 </H1> | |
| 10 <h2> | |
| 11 Purpose | |
| 12 </h2> | |
| 13 Demonstrate Bayesian regression for the MLP. | |
| 14 | |
| 15 <p><h2> | |
| 16 Synopsis | |
| 17 </h2> | |
| 18 <PRE> | |
| 19 demev1</PRE> | |
| 20 | |
| 21 | |
| 22 <p><h2> | |
| 23 Description | |
| 24 </h2> | |
| 25 The problem consists an input variable <CODE>x</CODE> which sampled from a | |
| 26 Gaussian distribution, and a target variable <CODE>t</CODE> generated by | |
| 27 computing <CODE>sin(2*pi*x)</CODE> and adding Gaussian noise. A 2-layer | |
| 28 network with linear outputs is trained by minimizing a sum-of-squares | |
| 29 error function with isotropic Gaussian regularizer, using the scaled | |
| 30 conjugate gradient optimizer. The hyperparameters <CODE>alpha</CODE> and | |
| 31 <CODE>beta</CODE> are re-estimated using the function <CODE>evidence</CODE>. A graph | |
| 32 is plotted of the original function, the training data, the trained | |
| 33 network function, and the error bars. | |
| 34 | |
| 35 <p><h2> | |
| 36 See Also | |
| 37 </h2> | |
| 38 <CODE><a href="evidence.htm">evidence</a></CODE>, <CODE><a href="mlp.htm">mlp</a></CODE>, <CODE><a href="scg.htm">scg</a></CODE>, <CODE><a href="demard.htm">demard</a></CODE>, <CODE><a href="demmlp1.htm">demmlp1</a></CODE><hr> | |
| 39 <b>Pages:</b> | |
| 40 <a href="index.htm">Index</a> | |
| 41 <hr> | |
| 42 <p>Copyright (c) Ian T Nabney (1996-9) | |
| 43 | |
| 44 | |
| 45 </body> | |
| 46 </html> |