Generate() 공개 메소드

Generate the feedforward neural network.
public Generate ( ) : IMLMethod
리턴 IMLMethod
예제 #1
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 public static BasicNetwork CreateNetwork()
 {
     var pattern = new FeedForwardPattern {InputNeurons = 3};
     pattern.AddHiddenLayer(50);
     pattern.OutputNeurons = 1;
     pattern.ActivationFunction = new ActivationTANH();
     var network = (BasicNetwork) pattern.Generate();
     network.Reset();
     return network;
 }
예제 #2
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 private IMLMethod CreateFeedforwardNetwork()
 {
     // construct a feedforward type network
     FeedForwardPattern pattern = new FeedForwardPattern();
     pattern.ActivationFunction = new ActivationSigmoid();
     pattern.InputNeurons = 1;
     pattern.AddHiddenLayer(6);
     pattern.OutputNeurons = 1;
     return pattern.Generate();
 }
예제 #3
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 public static BasicNetwork createNetwork()
 {
     var pattern = new FeedForwardPattern { InputNeurons = (Board.SIZE * Board.SIZE) };
     pattern.AddHiddenLayer (NEURONS_HIDDEN_1);
     pattern.OutputNeurons = 1;
     pattern.ActivationFunction = new ActivationTANH ();
     var network = (BasicNetwork)pattern.Generate ();
     network.Reset ();
     return network;
 }
예제 #4
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 /// <summary>
 ///     Creates the feedforward network.
 /// </summary>
 /// <param name="inputsize">The inputsize.</param>
 /// <param name="outputsize">The outputsize.</param>
 /// <param name="hiddenlayers">The hiddenlayers.</param>
 /// <param name="hidden2Layers">The hidden2layers.</param>
 /// <returns></returns>
 public static BasicNetwork CreateFeedforwardNetwork(int inputsize, int outputsize, int hiddenlayers,
                                                     int hidden2Layers)
 {
     // construct an Elman type network
     var pattern = new FeedForwardPattern {ActivationFunction = new ActivationTANH(), InputNeurons = inputsize};
     pattern.AddHiddenLayer(hiddenlayers);
     pattern.AddHiddenLayer(hidden2Layers);
     pattern.OutputNeurons = outputsize;
     IMLMethod network = pattern.Generate();
     return (BasicNetwork) network;
 }
예제 #5
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 private static IMLMethod CreateFeedforwardNetwork(int inputs, int outputs, int hidden)
 {
     // construct a feedforward type network
     var pattern = new FeedForwardPattern();
     pattern.ActivationFunction = new ActivationSigmoid();
     pattern.InputNeurons = inputs;
     pattern.AddHiddenLayer(hidden);
     pattern.OutputNeurons = outputs;
     return pattern.Generate();
 }
예제 #6
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        /// <summary>
        /// Create a simple feedforward neural network.
        /// </summary>
        /// <param name="input">The number of input neurons.</param>
        /// <param name="hidden1">The number of hidden layer 1 neurons.</param>
        /// <param name="hidden2">The number of hidden layer 2 neurons.</param>
        /// <param name="output">The number of output neurons.</param>
        /// <param name="tanh">True to use hyperbolic tangent activation function, false to
        /// use the sigmoid activation function.</param>
        /// <returns>The neural network.</returns>
        public static BasicNetwork SimpleFeedForward(int input,
                                                     int hidden1, int hidden2, int output,
                                                     bool tanh)
        {
            var pattern = new FeedForwardPattern {InputNeurons = input, OutputNeurons = output};
            if (tanh)
            {
                pattern.ActivationFunction = new ActivationTANH();
            }
            else
            {
                pattern.ActivationFunction = new ActivationSigmoid();
            }

            if (hidden1 > 0)
            {
                pattern.AddHiddenLayer(hidden1);
            }
            if (hidden2 > 0)
            {
                pattern.AddHiddenLayer(hidden2);
            }

            var network = (BasicNetwork) pattern.Generate();
            network.Reset();
            return network;
        }