public void Learning(Dictionary <string, double[]> keyValuePairs, int outClass, int epochs, int miniBatchSize, string filePath, MNIST mnist /* Mnist */)
{
TextWriter textWriter = new StreamWriter(filePath + "learning_report.csv");
for (int e = 0; e < epochs; e++)
{
Console.WriteLine("Processing epoch {0} of {1}", e + 1, epochs + 1);
var recognition = 0;
var recRate = 0.0;
// shuffle training input
//var list = keyValuePairs.Keys.ToList();
//var rnd = new Random();
//var rndList = list.OrderBy(x => rnd.Next()).ToList();
//Dictionary<string, double[]> rndKeyValuePairs = new Dictionary<string, double[]>();
//foreach (var key in rndList)
//{
// rndKeyValuePairs.Add(key, keyValuePairs[key]);
//}
// ------------------------ MNIST Dataset ------------------------
mnist.ShuffleTrainingImgs();
// ------------------------ MNIST Dataset ------------------------
// training data
for (int i = 0; i < /*rndKeyValuePairs.Count*/ mnist.TrainingImgs.Count; i++)
{
double[] output = new double[outClass];
// forward pass
for (int j = 0; j < this.Layers.Count; j++)
{
if (this.Layers[j].GetType().Equals(typeof(InputLayer)))
{
this.Layers[j].FeedForward(/*new Bitmap(rndKeyValuePairs.ElementAt(i).Key)*/ null, null, /*null*/ mnist.TrainingImgs[i].Pixels); // MNIST
continue;
}
if (this.Layers[j].GetType().Equals(typeof(ConvolutionLayer)))
{
if (e == 0 && i == 0)
{
this.Layers[j].RandInitFilter();
}
this.Layers[j].FeedForward(null, null, this.Layers[j - 1].ImgMatrix);
continue;
}
if (this.Layers[j].GetType().Equals(typeof(PoolingLayer)))
{
this.Layers[j].FeedForward(null, null, this.Layers[j - 1].ImgMatrix);
if (this.Layers[j + 1].GetType().Equals(typeof(FullyConnectedLayer)))
{
this.Layers[j].Flattening();
}
continue;
}
if (this.Layers[j].GetType().Equals(typeof(FullyConnectedLayer)))
{
if (this.Layers[j + 1].GetType().Equals(typeof(OutputLayer)))
{
this.Layers[j].InitLayer(this.Layers[j - 1].FlatArray.Length, outClass);
}
else
{
this.Layers[j].InitLayer(this.Layers[j - 1].FlatArray.Length, this.Layers[j - 1].FlatArray.Length);
}
if (e == 0 && i == 0)
{
this.Layers[j].RandInitLayerMat();
}
this.Layers[j].FeedForward(null, this.Layers[j - 1].FlatArray, null);
continue;
}
if (this.Layers[j].GetType().Equals(typeof(OutputLayer)))
{
this.Layers[j].FeedForward(null, this.Layers[j - 1].FlatArray, null);
output = this.Layers[j].GetOutputArray();
}
}
// backward pass
for (int j = this.Layers.Count - 1; j >= 0; j--)
{
if (this.Layers[j].GetType().Equals(typeof(ConvolutionLayer)))
{
this.Layers[j].BackwardPass(null, this.Layers[j + 1].DeltaMatrix);
continue;
}
if (this.Layers[j].GetType().Equals(typeof(PoolingLayer)))
{
if (this.Layers[j + 1].GetType().Equals(typeof(FullyConnectedLayer)))
{
this.Layers[j].BackwardPass(this.Layers[j + 1].DeltaArray, null);
}
else
{
this.Layers[j].BackwardPass(null, this.Layers[j + 1].DeltaMatrix);
}
continue;
}
if (this.Layers[j].GetType().Equals(typeof(FullyConnectedLayer)))
{
this.Layers[j].BackwardPass(this.Layers[j + 1].DeltaArray, null);
continue;
}
if (this.Layers[j].GetType().Equals(typeof(OutputLayer)))
{
this.Layers[j].BackwardPass(/*rndKeyValuePairs.ElementAt(i).Value*/ mnist.TrainingImgs[i].Label, null); // MNIST
}
}
// update for every mini Batch
if (i % miniBatchSize == miniBatchSize - 1)
{
// update forward pass
for (int j = 0; j < this.Layers.Count; j++)
{
if (this.Layers[j].GetType().Equals(typeof(ConvolutionLayer)) ||
this.Layers[j].GetType().Equals(typeof(FullyConnectedLayer)))
{
this.Layers[j].UpdateWeights(miniBatchSize);
}
}
//Console.WriteLine("\t" + "Weights updated after {0} inputs", i + 1);
}
// recognition rate computation
recognition += RecognitionRate(output, /*rndKeyValuePairs.ElementAt(i).Value*/ mnist.TrainingImgs[i].Label); // MNIST
}
recRate = (double)recognition / (double)/*rndKeyValuePairs.Count*/ mnist.TrainingImgs.Count; // MNIST
Console.WriteLine("Recognition rate in epoch {0}: {1}", e + 1, recRate);
// save recognition rate to csv after each epoch
textWriter.WriteLine(recRate);
textWriter.Flush();
// store weights
for (int j = 0; j < this.Layers.Count; j++)
{
if (this.Layers[j].GetType().Equals(typeof(ConvolutionLayer)) ||
this.Layers[j].GetType().Equals(typeof(FullyConnectedLayer)))
{
// store weights
this.Layers[j].StoreWeights();
}
}
if (recRate == 1)
{
Console.WriteLine("Learning stopped in epoch {0} of {1}", e + 1, epochs);
break;
}
}
}
