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AVL.MLP_Init Method
Creates multilayer perceptron model.
| Namespace: | AvlNet |
|---|---|
| Assembly: | AVL.NET.dll |
Syntax
public static void MLP_Init( int[] inHiddenLayers, AvlNet.ActivationFunction inActivationFunction, AvlNet.MlpPreprocessing inPreprocessing, int? inRandomSeed, int inInputCount, int inOutputCount, out AvlNet.MlpModel outMlpModel )
Parameters
| Name | Type | Range | Default | Description | |
|---|---|---|---|---|---|
 | inHiddenLayers | int | Internal structure of MLP network. Default value: atl::NIL, or null. | ||
| inActivationFunction | AvlNet.ActivationFunction | Type of activation function used to calculate neural response. | ||
| inPreprocessing | AvlNet.MlpPreprocessing | Method of processing input data before learning. | ||
| inRandomSeed | int? | <0, INF> | Number used as starting random seed. Default value: atl::NIL, or null. | |
| inInputCount | int | <1, INF> | 1 | MLP network input count. Default value: 1. |
| inOutputCount | int | <1, INF> | 1 | MLP network output count. Default value: 1. |
 | outMlpModel | AvlNet.MlpModel | Initialized MlpModel. |
Description
Filter initializes and sets structure of the MlpModel.
Image: Internal structure of MlpModel. Function f denotes the inActivationFunction.
Parameter inHiddenLayers represents number of neurons in consecutive hidden layers.
The parameter inActivationFunction is a function used to calculate internal neuron activation.
The weights of the multilayer perceptron are initialized by a random numbers. Their values depend on inRandomSeed value.
Parameters inInputCount and inOutputCount defines network inputs and outputs count.
