C# Network.AddLayer方法代码示例

 public NetTrainer(IModelService modelService)
 {
     Network net = new Network();
     net.AddLayer(new BasicLayer(new ActivationTANH(), true, DefaultFingerprintSize));
     net.AddLayer(new BasicLayer(new ActivationTANH(), true, DefaultHiddenNeuronsCount));
     net.AddLayer(new BasicLayer(new ActivationTANH(), false, OutPutNeurons));
     net.Structure.FinalizeStructure();
     net.Reset();
     this.modelService = modelService;
     pauseSem = new Semaphore(0, 1, "PauseSemaphore");
 }

        public void BuildNetwork()
        {
            _network = new Network(_node);

            _network.AddLayer(4); //Hidden layer with 2 neurons
            _network.AddLayer(1); //Output layer with 1 neuron

            _network.BindInputLayer(_input); //Bind Input Data
            _network.BindTraining(_desired); //Bind desired output data

            _network.AutoLinkFeedforward(); //Create synapses between the layers for typical feedforward networks.
        }

        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();
        }

        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();
        }

        private void RebuildCurrentNetworkStructure()
        {
            currentNetwork = new Network();
            currentNeuronMap = new Hashtable();
            currentSynapseMap = new Hashtable();

            //Build Layers and Neurons
            NeuralDataSet.LayersRow[] layerRows = SelectLayersFromNetwork(currentNetworkRow);
            if(layerRows.Length == 0)
                return;
            NeuralDataSet.NeuronsRow[] neuronRows = SelectNeuronsFromLayer(layerRows[0]);
            currentNetwork.InitUnboundInputLayer(neuronRows.Length);
            Layer[] layers = new Layer[layerRows.Length];
            layers[0] = currentNetwork.FirstLayer;
            AppendNeuronsToNeuronMap(currentNeuronMap,neuronRows,layers[0]);
            for(int i=1;i<layerRows.Length;i++)
            {
                neuronRows = SelectNeuronsFromLayer(layerRows[i]);
                layers[i] = currentNetwork.AddLayer(neuronRows.Length);
                AppendNeuronsToNeuronMap(currentNeuronMap,neuronRows,layers[i]);
            }

            //Build Synapses
            NeuralDataSet.SynapsesRow[] synapseRows = SelectSynapsesFromNetwork(currentNetworkRow);
            for(int i=0;i<synapseRows.Length;i++)
                currentSynapseMap.Add(synapseRows[i].syID,
                    ((Neuron)currentNeuronMap[synapseRows[i].syFK_neuronSource]).ConnectToNeuron((Neuron)currentNeuronMap[synapseRows[i].syFK_neuronTarget]));
        }

        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();
        }

        public void RunDemo()
        {
            Console.WriteLine("### NETWORK STRUCTURE 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);

            //Instead of calling AutoLinkFeedforward()
            //on this place, in this demo we'll connect
            //the network together by our own!

            Layer input = network.FirstLayer;
            Layer hidden = input.TargetLayer;
            Layer output = network.LastLayer;

            //First we want to connect all neurons
            //of the hidden layer to all neurons
            //of the input layer (that's exactly
            //what the AutoLinkFeedforward would
            //do - but between all layers).
            input.CrossLinkForward();

            //Then we want to achieve a lateral
            //feedback in the hidden layer
            //(AutoLinkFeedforward does NOT do this):
            hidden.CrossLinkLayer();

            //Next we want to connect the first
            //and the second Neuron of the hidden
            //Layer to the first output neuron,
            //and the third and fourth to the 2nd
            //output neuron. Some of the synapses
            //shall start with special weights:
            hidden[0].ConnectToNeuron(output[0]);
            hidden[1].ConnectToNeuron(output[0],0.5);
            hidden[2].ConnectToNeuron(output[1]);
            hidden[3].ConnectToNeuron(output[1],-1.5);

            //That's it. Now we can work with it,
            //just we did on the Basic Unbound Demo:
            network.PushUnboundInput(new bool[] {false,true,false});
            network.PushUnboundTraining(new bool[] {false,true});
            network.CalculateFeedforward();
            App.PrintArray(network.CollectOutput());
            network.TrainCurrentPattern(false,true);
            App.PrintArray(network.CollectOutput());
            network.TrainCurrentPattern(false,true);
            App.PrintArray(network.CollectOutput());

            //This demo may help you e.g. building your own
            //INetworkStructureFactory implementations
            //for the grid pattern matching building block.
            //(You may also want to check out the default
            //implementation!)

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

 /// <summary>
 ///   Standard constructor of NetTrainer, should be used in most cases.
 /// </summary>
 /// <param name = "dalManager">Database gateway</param>
 public NetTrainer(DaoGateway dalManager)
 {
     Network net = new Network();
     net.AddLayer(new BasicLayer(new ActivationTANH(), true, DEFAULT_FINGERPRINT_SIZE));
     net.AddLayer(new BasicLayer(new ActivationTANH(), true, DEFAULT_HIDDEN_NEURONS_COUNT));
     net.AddLayer(new BasicLayer(new ActivationTANH(), false, OUT_PUT_NEURONS));
     net.Structure.FinalizeStructure();
     net.Reset();
     Init(net, dalManager);
 }