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The "MultiLayerPerceptron" page has been changed by YexiJiang: https://wiki.apache.org/hama/MultiLayerPerceptron?action=diff&rev1=28&rev2=29 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...
