private void Test()
{
UnityEngine.Profiling.Profiler.BeginSample("Test");
int correctTestLabels = 0;
for (int i = 0; i < DataManager.Test.NumImgs; i++)
{
int lbl = (int)DataManager.Test.Labels[i];
var handle = DataManager.CopyInput(_net.Inputs, DataManager.Test, i);
handle = NeuralJobs.ForwardPass(_net, handle);
handle.Complete();
int predictedLbl = NeuralMath.ArgMax(_net.Last.Outputs);
if (predictedLbl == lbl)
{
correctTestLabels++;
}
}
float accuracy = correctTestLabels / (float)DataManager.Test.NumImgs;
Debug.Log("Test Accuracy: " + System.Math.Round(accuracy * 100f, 4) + "%");
UnityEngine.Profiling.Profiler.EndSample();
}
private void TrainMinibatch()
{
UnityEngine.Profiling.Profiler.BeginSample("TrainMiniBatch");
float avgTrainCost = 0f;
DataManager.GetBatch(_batch, DataManager.Train, ref _rng);
var handle = NeuralJobs.ZeroGradients(_gradientsAvg);
for (int i = 0; i < _batch.Length; i++)
{
handle = DataManager.CopyInput(_net.Inputs, DataManager.Train, _batch[i], handle);
handle = NeuralJobs.ForwardPass(_net, handle);
int lbl = (int)DataManager.Train.Labels[_batch[i]];
handle.Complete();
NeuralMath.ClassToOneHot(lbl, _targetOutputs); // Todo: job
handle = NeuralJobs.BackwardsPass(_net, _gradients, _targetOutputs, handle);
handle = NeuralJobs.AddGradients(_gradients, _gradientsAvg, handle);
handle.Complete();
// Todo: backwards pass logic now does this, don't redo, just check
NeuralMath.Subtract(_targetOutputs, _net.Last.Outputs, _dCdO);
float cost = NeuralMath.Cost(_dCdO);
avgTrainCost += cost;
}
// Update weights and biases according to averaged gradient and learning rate
_rate = 3.0f / (float)BatchSize;
handle = NeuralJobs.UpdateParameters(_net, _gradientsAvg, _rate, handle);
handle.Complete(); // Todo: Is this one needed?
_batchCount++;
avgTrainCost /= (float)BatchSize;
_trainingLoss = (float)System.Math.Round(avgTrainCost, 6);
UnityEngine.Profiling.Profiler.EndSample();
}
