Ejemplo n.º 1
0
        static void Main(string[] args)
        {
            Operations K = new Operations();

            //Load array to the tensor
            NDArray x = new NDArray(3, 3);

            x.Load(2, 4, 6, 1, 3, 5, 2, 3, 5);
            x.Print("Load X Values");

            NDArray y = new NDArray(3, 1);

            y.Load(1, 0, 1);
            y.Print("Load Y Values");

            //Create two layers, one with 6 neurons and another with 1
            FullyConnected fc1 = new FullyConnected(3, 6, "relu");
            FullyConnected fc2 = new FullyConnected(6, 1, "sigmoid");

            //Connect input by passing data from one layer to another
            fc1.Forward(x);
            fc2.Forward(fc1.Output);
            var preds = fc2.Output;

            preds.Print("Predictions");

            //Calculate the mean square error cost between the predicted and expected values
            BaseCost cost       = new BinaryCrossEntropy();
            var      costValues = cost.Forward(preds, y);

            costValues.Print("BCE Cost");

            //Calculate the mean absolute metric value for the predicted vs expected values
            BaseMetric metric       = new BinaryAccuacy();
            var        metricValues = metric.Calculate(preds, y);

            metricValues.Print("Acc Metric");

            var grad = cost.Backward(preds, y);

            fc2.Backward(grad);
            fc1.Backward(fc2.InputGrad);
            fc1.PrintParams();

            Console.ReadLine();
        }
Ejemplo n.º 2
0
        static void Main(string[] args)
        {
            Operations K = new Operations();

            //Load array to the tensor
            NDArray x = new NDArray(3, 3);

            x.Load(2, 4, 6, 1, 3, 5, 2, 3, 5);
            x.Print("Load X Values");

            NDArray y = new NDArray(3, 1);

            y.Load(1, 0, 1);
            y.Print("Load Y Values");

            //Create two layers, one with 6 neurons and another with 1
            FullyConnected fc1 = new FullyConnected(3, 6, "relu");
            FullyConnected fc2 = new FullyConnected(6, 1, "sigmoid");

            //Connect input by passing data from one layer to another
            fc1.Forward(x);
            fc2.Forward(fc1.Output);
            var preds = fc2.Output;

            preds.Print("Predictions");

            //Calculate the mean square error cost between the predicted and expected values
            BaseCost cost       = new BinaryCrossEntropy();
            var      costValues = cost.Forward(preds, y);

            costValues.Print("BCE Cost");

            //Calculate the mean absolute metric value for the predicted vs expected values
            BaseMetric metric       = new BinaryAccuacy();
            var        metricValues = metric.Calculate(preds, y);

            metricValues.Print("Acc Metric");

            var grad = cost.Backward(preds, y);

            fc2.Backward(grad);
            fc1.Backward(fc2.InputGrad);

            Console.WriteLine("Param value for FC1 before ADAM optimization");
            fc1.PrintParams(printGrads: false);

            //Initialise ADAM optimizer with default learning rate of 0.01
            BaseOptimizer optimizer = BaseOptimizer.Get("adam");

            //Change the value of learning rate to see the jump is weight changes.
            //optimizer.LearningRate = 0.1;

            //Apply optimizer for the first layer for first iteration
            optimizer.Update(1, fc1);
            Console.WriteLine("Param value for FC1 after ADAM optimization");
            fc1.PrintParams(printGrads: false);

            Console.WriteLine("Param value for FC2 before ADAM optimization");
            fc2.PrintParams(printGrads: false);

            //Apply optimizer for the first layer for first iteration
            optimizer.Update(1, fc2);
            Console.WriteLine("Param value for FC2 after ADAM optimization");
            fc2.PrintParams(printGrads: false);

            Console.ReadLine();
        }