// Runs a single epoch of training.
private static void TrainEpoch(Sequential model, ICriterion criterion, SgdOptimizer optim, DataSet trainingSet, int numInputs, bool useTargetClasses)
{
using (new SimpleTimer("Training epoch completed in {0}ms"))
{
for (int batchStart = 0; batchStart <= trainingSet.inputs.Shape[0] - BatchSize; batchStart += BatchSize)
{
Console.Write(".");
var grad = new GradFunc(parameters =>
{
using (var mbInputs = trainingSet.inputs.Narrow(0, batchStart, BatchSize))
using (var mbTargets = trainingSet.targets.Narrow(0, batchStart, BatchSize))
using (var mbTargetClasses = trainingSet.targetValues.Narrow(0, batchStart, BatchSize))
{
foreach (var gradNDArray in model.GetGradParameters())
{
Ops.Fill(gradNDArray, 0);
}
var modelOutput = model.Forward(mbInputs, ModelMode.Train);
var criterionOutput = criterion.UpdateOutput(modelOutput, useTargetClasses ? mbTargetClasses : mbTargets);
var criterionGradIn = criterion.UpdateGradInput(modelOutput, useTargetClasses ? mbTargetClasses : mbTargets);
model.Backward(mbInputs, criterionGradIn, ModelMode.Train);
return(new OutputAndGrads()
{
output = modelOutput, grads = model.GetGradParameters().ToArray()
});
}
});
optim.Update(grad, model.GetParameters().ToArray());
}
}
Console.WriteLine();
}