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();
}
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(20, 15, 15);
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, "relu");
//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 MeanSquaredError();
var costValues = cost.Forward(preds, y);
costValues.Print("MSE Cost");
//Calculate the mean absolute metric value for the predicted vs expected values
BaseMetric metric = new MeanAbsoluteError();
var metricValues = metric.Calculate(preds, y);
metricValues.Print("MAE Metric");
Console.ReadLine();
}
static void Main(string[] args)
{
//Load array to the tensor
NDArray x = new NDArray(1, 3);
x.Load(1, 2, 3);
x.Print("Load array");
//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);
x = fc1.Output;
x.Print("FC1 Output");
fc2.Forward(x);
x = fc2.Output;
x.Print("FC2 Output");
Console.ReadLine();
}
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();
}
static void Main(string[] args)
{
/*
* //
* //1. Test tensor and operations
* //
* Operations K = new Operations();
*
* //Load array to the tensor
* NDArray a = new NDArray(3, 6);
* a.Load(1, 2, 3, 4, 5, 6, 7, 8, 9, 9, 8, 7, 6, 5, 4, 3, 2, 1);
* a.Print("Load array");
*
* //Transpose of the matrix
* NDArray t = a.Transpose();
* t.Print("Transpose");
*
* //Create a tensor with all value 5
* NDArray b = new NDArray(6, 3);
* b.Fill(5);
* b.Print("Constant 5");
*
* //Create a tensor with all value 3
* NDArray c = new NDArray(6, 3);
* c.Fill(3);
* c.Print("Constant 3");
*
* // Subtract two tensor
* b = b - c;
*
* //Perform dot product
* NDArray r = K.Dot(a, b);
* r.Print("Dot product");
*/
//
//2. Test layers and activations
//
/*
* //Load array to the tensor
* NDArray x = new NDArray(1, 3);
* x.Load(1, 2, 3);
* x.Print("Load array");
*
* //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);
* x = fc1.Output;
* x.Print("FC1 Output");
*
* fc2.Forward(x);
* x = fc2.Output;
* x.Print("FC2 Output");
*
*/
//
//3. Test cost functions and metrics
//
//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(20, 15, 15);
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, "relu");
//Connect input by passing data from one layer to the other
fc1.Forward(x);
fc2.Forward(fc1.Output);
var preds = fc2.Output;
preds.Print("Predictions");
//Calculate mean square error between predicted and expected values
BaseCost cost = new BinaryCrossEntropy();
var costValues = cost.Forward(preds, y);
costValues.Print("BCE Cost");
//Calculate the mean absolute value for the predicted vs expected values
BaseMetric metric = new BinaryAccuracy();
var metricValues = metric.Calculate(preds, y);
metricValues.Print("Acc Metric");
//Backpropagation starts here
//Calculate gradient cost function
var grad = cost.Backward(preds, y);
//Then the fc2 layer by passing cost function grad into the layer backward function
fc2.Backward(grad);
//The grad of the fc2 is stored in the InputGrad property, pass it to fc1
fc1.Backward(fc2.InputGrad);
//Print parameters for both layers along with the Grad
fc1.PrintParams();
fc2.PrintParams();
}