protected override void BeginProcessing()
{
if (Context == null)
{
Context = Context.CurrentContext;
}
var stopWatch = new Stopwatch();
stopWatch.Start();
for (var epoch = 1; epoch <= MaxEpoch; ++epoch)
{
TrainingData.Reset();
var totalLoss = 0.0f;
var dataSize = 0;
while (!TrainingData.End())
{
var batch = TrainingData.Next();
var data = batch.Data[0].AsInContext(Context);
var label = batch.Label[0].AsInContext(Context);
using (Autograd.Record())
{
var output = Model.Call(data);
var loss = (NDArray)LossFunction.Call(output, label);
loss.Backward();
totalLoss += loss.Sum();
dataSize += data.Shape[0];
Trainer.Step(batch.Data[0].Shape[0]);
}
}
if (epoch % DisplayStep == 0 || epoch == MaxEpoch)
{
totalLoss /= dataSize;
ValidationData.Reset();
var totalValidLoss = 0.0f;
var validDataSize = 0;
if (MetricFunction != null)
{
MetricFunction.Reset();
}
while (!ValidationData.End())
{
var batch = ValidationData.Next();
var data = batch.Data[0].AsInContext(Context);
var label = batch.Label[0].AsInContext(Context);
var output = Model.Call(data);
var validLoss = (NDArray)LossFunction.Call(output, label);
totalValidLoss += validLoss.Sum();
validDataSize += data.Shape[0];
if (MetricFunction != null)
{
MetricFunction.Update(label, output);
}
}
totalValidLoss /= validDataSize;
string metricName = null;
float metric = float.NaN;
if (MetricFunction != null)
{
(metricName, metric) = MetricFunction.Get();
}
var status = new TrainingStatus(epoch,
(float)Math.Round(totalLoss, DisplayDigits),
(float)Math.Round(totalValidLoss, DisplayDigits),
metricName,
(float)Math.Round(metric, DisplayDigits),
stopWatch.Elapsed);
WriteObject(status);
}
}
}