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From Apache Wiki <wikidi...@apache.org>
Subject [Hama Wiki] Update of "MultiLayerPerceptron" by YexiJiang
Date Sun, 20 Oct 2013 17:17:08 GMT
Dear Wiki user,

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The "MultiLayerPerceptron" page has been changed by YexiJiang:

  The two phases will repeat alternatively until the termination condition is met (reach a
specified number of iterations).
  == How to use Multilayer Perceptron in Hama? ==
  MLP can be used for both regression and classification. For both tasks, we need first initialize
the MLP model by specifying the parameters. 
+ === Train the model ===
  For training, the following things need to be specified:
   * The '''''model topology''''': including the number of neurons (besides the bias neuron)
in each layer; whether current layer is the final layer; the type of squashing function.
   * The '''''learning rate''''': Specify how aggressive the model learning the training instances.
A large value can accelerate the learning process but decrease the chance of model convergence.
Recommend in range (0, 0.5].
@@ -93, +92 @@

  || convergence.check.interval || If this parameters is set, then the model will be checked
every time when the iteration is a multiple of this parameter. If the convergence condition
is satisfied, the training will terminate immediately. ||
  || tasks || The number of concurrent tasks. ||
+ === Use the trained model ===
+ Once the model is trained and stored, it can be reused later.
+ {{{
+   String modelPath = ...;  // the location of the existing model
+   DoubleVector features = ...; // the features of an instance
+   SmallLayeredNeuralNetwork ann = new SmallLayeredNeuralNetwork(modelPath);
+   DoubleVector labels = ann.getOutput(instance);  // the label evaluated by the model
+ }}}
  === Two class learning problem ===
  To be added...

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