コード例 #1
0
        private static void Train(BasicNetwork network, BasicMLDataSet trainingSet)
        {
            var propagation = new StochasticGradientDescent(network, trainingSet);

            var epoch = 1;

            for (var i = 0; i < 200; i++)
            {
                propagation.Iteration(1000);
                Console.WriteLine($"Epoch: {epoch} Error: {propagation.Error}");
                epoch++;
            }

            propagation.FinishTraining();
        }
コード例 #2
0
        void ItWorks()
        {
            var network2 = new BasicNetwork();

            var data    = LoadData();
            var label   = LoadLabel();
            var network = new BasicNetwork();

            network.AddLayer(new BasicLayer(null, true, 4));
            network.AddLayer(new BasicLayer(new ActivationReLU(), true, 8));
            network.AddLayer(new BasicLayer(new ActivationSigmoid(), false, 1));
            network.Structure.FinalizeStructure();
            network.Reset();

            var trainingSet = new BasicMLDataSet(data, label);

            var propagation = new StochasticGradientDescent(network, trainingSet);

            var epoch = 1;

            for (var i = 0; i < 5000; i++)
            {
                propagation.Iteration();
                Console.WriteLine($"Epoch: {epoch} Error: {propagation.Error}");
                epoch++;
            }

            propagation.FinishTraining();


            var weights = network.Flat.Weights;
            var biases  = network.Flat.BiasActivation;

            foreach (var pair in trainingSet)
            {
                var output = network.Compute(pair.Input);
                Debug.Log(pair.Input[0] + "," + pair.Input[1]
                          + ", actual=" + output[0] + ",ideal=" + pair.Ideal[0]);
            }
        }