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What is Multilayer Perceptron?
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MLP can be used for both regression and classification. For both tasks, we need first initialize the MLP model by specifying the parameters. The parameters are listed as follows:
Parameter | Description | |
model path | The path to specify the location to store the model. | |
learningRate | Control the aggressive of learning. A big learning rate can accelerate the training speed, | |
regularization | Control the complexity of the model. A large regularization value can make the weights between | |
momentum | Control the speed of training. A big momentum can accelerate the training speed, but it may | ]]></ac:plain-text-body></ac:structured-macro> |
squashing function | Activate function used by MLP. Candidate squashing function: sigmoid, tanh. | |
cost function | Evaluate the error made during training. Candidate cost function: squared error, cross entropy (logistic). | |
layer size array | An array specify the number of neurons (exclude bias neurons) in each layer (include input and output layer). | |
The following is the sample code regarding model initialization.
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String modelPath = "/tmp/xorModel-training-by-xor.data"; double learningRate = 0.6; double regularization = 0.02; // no regularization double momentum = 0.3; // no momentum String squashingFunctionName = "Tanh"; String costFunctionName = "SquaredError"; int[] layerSizeArray = new int[] { 2, 5, 1 }; SmallMultiLayerPerceptron mlp = new SmallMultiLayerPerceptron(learningRate, regularization, momentum, squashingFunctionName, costFunctionName, layerSizeArray); |
Two class learning problem
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