/// <summary>
/// Configures the model for training.
/// </summary>
/// <param name="optimizer">The optimizer function name used for training the model.</param>
/// <param name="loss">The function name with which the training loss will be minimized.</param>
/// <param name="metric"> The metric name to be evaluated by the model during training and testing.</param>
/// <param name="regulizer">The regulizer instance to apply penalty on layers parameters.</param>
public void Compile(BaseOptimizer optimizer, string loss, string metric = "", Regulizers regulizer = null)
{
CompileModel();
learners.Add(optimizer.Get(modelOut, regulizer));
lossName = loss;
lossFunc = Losses.Get(loss, labelVariable, modelOut);
if (!string.IsNullOrWhiteSpace(metric))
{
metricName = metric;
metricFunc = Metrics.Get(metric, labelVariable, modelOut);
}
else
{
metricName = loss;
metricFunc = lossFunc;
}
}
public void Compile(OptimizerType optimizer, LossType loss, MetricType metric)
{
OptimizerFn = BaseOptimizer.Get(optimizer);
LossFn = BaseLoss.Get(loss);
MetricFn = BaseMetric.Get(metric);
}
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();
}
