public static void Run()
{
// Create
var trainX = new NDArray(new float[] { 0, 0, 0, 1, 1, 0, 1, 1 }).Reshape(4, 2);
var trainY = new NDArray(new float[] { 0, 1, 1, 0 });
var batch_size = 2;
var train_data = new NDArrayIter(trainX, trainY, batch_size);
var val_data = new NDArrayIter(trainX, trainY, batch_size);
var net = new Sequential();
net.Add(new Dense(64, ActivationType.Relu));
net.Add(new Dense(1));
var gpus = TestUtils.ListGpus();
var ctxList = gpus.Count > 0 ? gpus.Select(x => Context.Gpu(x)).ToArray() : new[] { Context.Cpu() };
net.Initialize(new Uniform(), ctxList.ToArray());
var trainer = new Trainer(net.CollectParams(), new Adam());
var epoch = 1000;
var metric = new BinaryAccuracy();
var binary_crossentropy = new LogisticLoss();
float lossVal = 0;
for (var iter = 0; iter < epoch; iter++)
{
train_data.Reset();
lossVal = 0;
while (!train_data.End())
{
var batch = train_data.Next();
var data = Utils.SplitAndLoad(batch.Data[0], ctxList);
var label = Utils.SplitAndLoad(batch.Label[0], ctxList);
NDArrayList outputs = null;
using (var ag = Autograd.Record())
{
outputs = Enumerable.Zip(data, label, (x, y) =>
{
var z = net.Call(x);
NDArray loss = binary_crossentropy.Call(z, y);
loss.Backward();
lossVal += loss.Mean();
return(z);
}).ToList();
}
metric.Update(label, outputs.ToArray());
trainer.Step(batch.Data[0].Shape[0]);
}
var(name, acc) = metric.Get();
metric.Reset();
Console.WriteLine($"Loss: {lossVal}");
Console.WriteLine($"Training acc at epoch {iter}: {name}={acc * 100}%");
}
}
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();
}