C# Network.AutoLinkFeedforward方法代码示例

        public void BuildNetwork()
        {
            network = new Network(node);

            if(!groupedHiddenLayer)
            {
                network.AddLayer(32); //Hidden layer with 32 neurons
                network.AddLayer(16); //Output layer with 16 neuron

                network.BindInputLayer(input); //Bind Input Data

                network.PushUnboundTraining(outFF);
                network.AutoLinkFeedforward(); //Create synapses between the layers for typical feedforward networks.
            }
            else
            {
                network.AddLayer(64); //Hidden layer with 64 neurons
                network.AddLayer(16); //Output layer with 16 neuron

                network.BindInputLayer(input); //Bind Input Data

                network.PushUnboundTraining(outFF);

                network.FirstLayer.CrossLinkForward();
                Layer hidden = network.FirstLayer.TargetLayer;
                Layer output = network.LastLayer;
                for(int i=0;i<output.Count;i++)
                {
                    hidden[i*4].ConnectToNeuron(output[i]);
                    hidden[i*4+1].ConnectToNeuron(output[i]);
                    hidden[i*4+2].ConnectToNeuron(output[i]);
                    hidden[i*4+3].ConnectToNeuron(output[i]);
                }
            }
        }

        public void RunDemo()
        {
            Console.WriteLine("### BASIC UNBOUND DEMO ###");

            //Initialize the network manager.
            //This constructor also creates the first
            //network layer (Inputlayer).
            Network network = new Network();

            //You need to initialize (the size of) the
            //input layer in an unbound scenario
            network.InitUnboundInputLayer(3);
            //Add the hidden layer with 4 neurons.
            network.AddLayer(4);
            //Add the output layer with 2 neurons.
            network.AddLayer(2);

            //Connect the neurons together using synapses.
            //This is the easiest way to do it; I'll discuss
            //other ways in more detail in another demo.
            network.AutoLinkFeedforward();

            //Push new input data
            network.PushUnboundInput(new bool[] {false,true,false});
            //... and output training data ...
            network.PushUnboundTraining(new bool[] {false,true});

            //Propagate the network using the bound input data.
            //Internally, this is a two round process, to
            //correctly handle feedbacks
            network.CalculateFeedforward();
            //Collect the network output and print it.
            App.PrintArray(network.CollectOutput());

            //Train the current pattern using Backpropagation (one step)!
            network.TrainCurrentPattern(false,true);
            //Print the output; the difference to (-1,1) should be
            //smaller this time!
            App.PrintArray(network.CollectOutput());

            //Same one more time:
            network.TrainCurrentPattern(false,true);
            App.PrintArray(network.CollectOutput());

            //Train another pattern:
            Console.WriteLine("# new pattern:");
            //this time we're using doubles directly, instead of booleans.
            //5/1 are the default values for input/training values.
            network.PushUnboundInput(new double[] {5d,-5d,-5d});
            network.PushUnboundTraining(new double[] {1,1});
            //calculate ...
            network.CalculateFeedforward();
            App.PrintArray(network.CollectOutput());
            //... and train it one time
            network.TrainCurrentPattern(false,true);
            App.PrintArray(network.CollectOutput());

            //what about the old pattern now?
            Console.WriteLine("# the old pattern again:");
            network.PushUnboundInput(new double[] {-5d,5d,-5d});
            network.PushUnboundTraining(new double[] {-1,1});
            network.CalculateFeedforward();
            App.PrintArray(network.CollectOutput());

            Console.WriteLine("=== COMPLETE ===");
            Console.WriteLine();
        }

        public void RunDemo()
        {
            Console.WriteLine("### BASIC BOUND DEMO ###");

            //Prepare you're input and training data
            //to bind to the network
            double[] input = new double[] {-5d,5d,-5d};
            double[] training = new double[] {-1,1};

            //Initialize the network manager.
            //This constructor also creates the first
            //network layer (Inputlayer).
            Network network = new Network();

            //Bind your input array (to the already
            //existing input layer)
            network.BindInputLayer(input);
            //Add the hidden layer with 4 neurons.
            network.AddLayer(4);
            //Add the output layer with 2 neurons.
            network.AddLayer(2);
            //bind your training array to the output layer.
            //Always do this AFTER creating the layers.
            network.BindTraining(training);

            //Connect the neurons together using synapses.
            //This is the easiest way to do it; I'll discuss
            //other ways in more detail in another demo.
            network.AutoLinkFeedforward();

            //Propagate the network using the bound input data.
            //Internally, this is a two round process, to
            //correctly handle feedbacks
            network.CalculateFeedforward();
            //Collect the network output and print it.
            App.PrintArray(network.CollectOutput());

            //Train the current pattern using Backpropagation (one step)!
            network.TrainCurrentPattern(false,true);
            //Print the output; the difference to (-1,1) should be
            //smaller this time!
            App.PrintArray(network.CollectOutput());

            //Same one more time:
            network.TrainCurrentPattern(false,true);
            App.PrintArray(network.CollectOutput());

            //Train another pattern:
            Console.WriteLine("# new pattern:");
            input[0] = 5d;
            input[1] = -5d;
            training[0] = 1;
            //calculate ...
            network.CalculateFeedforward();
            App.PrintArray(network.CollectOutput());
            //... and train it one time
            network.TrainCurrentPattern(false,true);
            App.PrintArray(network.CollectOutput());

            //what about the old pattern now?
            Console.WriteLine("# the old pattern again:");
            input[0] = -5d;
            input[1] = 5d;
            training[0] = -1;
            network.CalculateFeedforward();
            App.PrintArray(network.CollectOutput());

            Console.WriteLine("=== COMPLETE ===");
            Console.WriteLine();
        }

        static void Main(string[] args)
        {
            Controler ffc = new Controler(1);

            Network net = new Network(ffc.Node);
            net.InitUnboundInputLayer(1).BasicConfiguration.ActivationType.Value = EActivationType.Linear;
            net.AddLayer(1, EActivationType.Linear);
            net.AutoLinkFeedforward();

            Neuron neuron = net.LastLayer[0];
            Synapse synapse = neuron.SourceSynapses[0];

            ffc.ImportNetwork(net, false);

            BasicConfig config = ffc.NeuralNetwork.BasicConfiguration;
            config.BiasNeuronEnable.Value = true;
            config.BiasNeuronOutput.Value = 1.0;
            config.FlatspotEliminationEnable.Value = false;
            config.WeightDecayEnable.Value = false;
            config.SymmetryPreventionEnable.Value = false;
            config.ManhattanTrainingEnable.Value = false;
            config.LearningRate.Value = 0.005;

            StochasticCoordinateGenerator scg = new StochasticCoordinateGenerator(0,10,100);
            //RegularCoordinateGenerator rcg = new RegularCoordinateGenerator(-25, 25, 50);
            DynamicSampleProvider dsp = new DynamicSampleProvider(my_func, scg); //rcg);

            ffc.Provider = dsp; // new CachedSampleProvider(dsp);

            Console.WriteLine("TARGET FUNCTION:             3*x+5");
            Console.WriteLine("TARGET Synapse Weight      = 3.0");
            Console.WriteLine("TARGET Bias Weight         = 5.0");
            Console.WriteLine("TARGET Mean Squared Error <= 0.000000001");
            Console.WriteLine();

            Console.WriteLine("Synapse Weight: " + synapse.Weight + " - Bias Weight: " + neuron.BiasNeuronWeight);
            Console.WriteLine("Initial MSE: " + ffc.EstimateMeanSquaredError());
            Console.WriteLine();

            ffc.TrainAllSamplesOnce();
            Console.WriteLine("Synapse Weight: " + synapse.Weight + " - Bias Weight: " + neuron.BiasNeuronWeight);
            Console.WriteLine("Trained MSE: " + ffc.EstimateMeanSquaredError());
            Console.WriteLine();

            ffc.TrainAllSamplesOnce();
            Console.WriteLine("Synapse Weight: " + synapse.Weight + " - Bias Weight: " + neuron.BiasNeuronWeight);
            Console.WriteLine("Trained MSE: " + ffc.EstimateMeanSquaredError());
            Console.WriteLine();

            ffc.TrainAllSamplesOnce();
            Console.WriteLine("Synapse Weight: " + synapse.Weight + " - Bias Weight: " + neuron.BiasNeuronWeight);
            Console.WriteLine("Trained MSE: " + ffc.EstimateMeanSquaredError());
            Console.WriteLine();

            Console.WriteLine("Auto Training, maximum 1000 Epochs");
            Console.WriteLine();
            if(ffc.TrainAllSamplesUntil(0.000000001, 1000))
            {
                Console.WriteLine("Synapse Weight: " + synapse.Weight + " - Bias Weight: " + neuron.BiasNeuronWeight);
                Console.ForegroundColor = ConsoleColor.Green;
                Console.WriteLine("SUCCEEDS auto training with MSE: " + ffc.EstimateMeanSquaredError());
                Console.ResetColor();
            }
            else
            {
                Console.WriteLine("Synapse Weight: " + synapse.Weight + " - Bias Weight: " + neuron.BiasNeuronWeight);
                Console.ForegroundColor = ConsoleColor.Red;
                Console.WriteLine("FAILS auto training with MSE: " + ffc.EstimateMeanSquaredError());
                Console.ResetColor();
            }

            Console.ReadKey();
        }