private void TrainMinibatch()
{
UnityEngine.Profiling.Profiler.BeginSample("TrainMiniBatch");
const int numClasses = 10;
const int batchSize = 10;
var target = new float[numClasses];
var dCdO = new float[numClasses];
float avgTrainCost = 0f;
int correctTrainLabels = 0;
ZeroGradients(_gradientBucket);
var trainBatch = DataManager.GetBatch(batchSize, DataManager.Train, ref _rng);
for (int i = 0; i < trainBatch.Indices.Length; i++)
{
int lbl = DataManager.Train.Labels[trainBatch.Indices[i]];
// Copy image to input layer (Todo: this is a waste of time/memory)
UnityEngine.Profiling.Profiler.BeginSample("CopyInputs");
for (int p = 0; p < DataManager.Train.ImgDims; p++)
{
_net.Input[p] = DataManager.Train.Images[trainBatch.Indices[i], p];
}
UnityEngine.Profiling.Profiler.EndSample();
NetUtils.Forward(_net);
int predictedLbl = NetUtils.GetMaxOutput(_net);
NetUtils.LabelToOneHot(lbl, target);
if (predictedLbl == lbl)
{
correctTrainLabels++;
}
//Debug.Log(outputClass + ", " + batch.Labels[i]);
// Calculate error between output layer and target
NetUtils.Subtract(target, _net.Output, dCdO);
float cost = NetUtils.Cost(dCdO);
avgTrainCost += cost;
// Propagate error back
// Calculate per-parameter gradient, store it
NetUtils.Backward(_net, target);
AddGradients(_net, _gradientBucket);
}
avgTrainCost /= (float)batchSize;
// Update weights and biases according to averaged gradient and learning rate
_rate = 3.0f / (float)batchSize;
NetUtils.UpdateParameters(_net, _gradientBucket, _rate);
_batch++;
_trainingLoss = (float)Math.Round(avgTrainCost, 6);
// Debug.Log(
// "Batch: " + _batchesTrained +
// ", TrainLoss: " + Math.Round(avgTrainCost, 6) +
// ", Rate: " + Math.Round(rate, 6));
// Mnist.ToTexture(batch, batch.Labels.Length-1, _tex);
// _label = batch.Labels[batch.Labels.Length-1];
UnityEngine.Profiling.Profiler.EndSample();
}
