C# (CSharp) AIMA.Core.Learning.Neural NNConfig.getParameterAsint示例

getParameterAsint() public method

public getParameterAsint ( String key ) : int
key	String
return	int

示例#1

文件： FeedForwardNeuralNetwork.cs 项目： claudiu04/AIMA.Net

        /*
         * constructor to be used for non testing code.
         */
        public FeedForwardNeuralNetwork(NNConfig config)
        {
            int numberOfInputNeurons = config
                                       .getParameterAsint(NUMBER_OF_INPUTS);
            int numberOfHiddenNeurons = config
                                        .getParameterAsint(NUMBER_OF_HIDDEN_NEURONS);
            int numberOfOutputNeurons = config
                                        .getParameterAsint(NUMBER_OF_OUTPUTS);

            double lowerLimitForWeights = config
                                          .getParameterAsDouble(LOWER_LIMIT_WEIGHTS);
            double upperLimitForWeights = config
                                          .getParameterAsDouble(UPPER_LIMIT_WEIGHTS);

            hiddenLayer = new Layer(numberOfHiddenNeurons, numberOfInputNeurons,
                                    lowerLimitForWeights, upperLimitForWeights,
                                    new LogSigActivationFunction());

            outputLayer = new Layer(numberOfOutputNeurons, numberOfHiddenNeurons,
                                    lowerLimitForWeights, upperLimitForWeights,
                                    new PureLinearActivationFunction());
        }

示例#2

文件： FeedForwardNeuralNetwork.cs 项目： PaulMineau/AIMA.Net

	/*
	 * constructor to be used for non testing code.
	 */
	public FeedForwardNeuralNetwork(NNConfig config) {

		int numberOfInputNeurons = config
				.getParameterAsint(NUMBER_OF_INPUTS);
		int numberOfHiddenNeurons = config
				.getParameterAsint(NUMBER_OF_HIDDEN_NEURONS);
		int numberOfOutputNeurons = config
				.getParameterAsint(NUMBER_OF_OUTPUTS);

		double lowerLimitForWeights = config
				.getParameterAsDouble(LOWER_LIMIT_WEIGHTS);
		double upperLimitForWeights = config
				.getParameterAsDouble(UPPER_LIMIT_WEIGHTS);

		hiddenLayer = new Layer(numberOfHiddenNeurons, numberOfInputNeurons,
				lowerLimitForWeights, upperLimitForWeights,
				new LogSigActivationFunction());

		outputLayer = new Layer(numberOfOutputNeurons, numberOfHiddenNeurons,
				lowerLimitForWeights, upperLimitForWeights,
				new PureLinearActivationFunction());

	}