public ConvolutionalPoolingLayer(ConvolutionalPoolingLayer layer) : base(layer)
{
var activationMapWidth = layer.imageWidth - layer.filterWidth + 1;
var activationMapHeight = layer.imageHeight - layer.filterHeight + 1;
var length = layer.filters * layer.channels * layer.filterWidth * layer.filterHeight;
this.weights = new double[length];
this.biases = new double[layer.filters * activationMapWidth * activationMapHeight];
this.imageWidth = layer.imageWidth;
this.imageHeight = layer.imageHeight;
this.channels = layer.channels;
this.filters = layer.filters;
this.filterWidth = layer.filterWidth;
this.filterHeight = layer.filterHeight;
this.poolWidth = layer.poolWidth;
this.poolHeight = layer.poolHeight;
this.convolvedInputs = new double[layer.filters, activationMapHeight, activationMapWidth];
this.activationMaps = new double[layer.filters, activationMapHeight, activationMapWidth];
this.activationFunction = layer.activationFunction;
for (int i = 0; i < length; i++)
{
this.weights[i] = layer.weights[i];
}
for (int i = 0, j = 0; i < this.filters; i++)
{
for (int k = 0; k < activationMapHeight; k++)
{
for (int l = 0; l < activationMapWidth; l++)
{
this.biases[j] = layer.biases[j];
this.convolvedInputs[i, k, l] = layer.convolvedInputs[i, k, l];
this.activationMaps[i, k, l] = layer.activationMaps[i, k, l];
j++;
}
}
}
}
static void Main(string[] args)
{
Console.WriteLine("MNIST Test");
int seed;
using (var rng = new RNGCryptoServiceProvider())
{
var buffer = new byte[sizeof(int)];
rng.GetBytes(buffer);
seed = BitConverter.ToInt32(buffer, 0);
}
RandomProvider.SetSeed(seed);
var assembly = Assembly.GetExecutingAssembly();
var random = RandomProvider.GetRandom();
var trainingList = new List <Tuple <double[], double[]> >();
var testList = new List <Tuple <double[], double[]> >();
var accuracyList = new List <double>();
var lossList = new List <double>();
var logDictionary = new Dictionary <string, IEnumerable <double> >();
var logPath = "Log.csv";
var channels = 1;
var imageWidth = 28;
var imageHeight = 28;
var filters = 30;
var filterWidth = 5;
var filterHeight = 5;
var poolWidth = 2;
var poolHeight = 2;
using (Stream
imagesStream = assembly.GetManifestResourceStream("MNISTTest.train-images.idx3-ubyte"),
labelsStream = assembly.GetManifestResourceStream("MNISTTest.train-labels.idx1-ubyte"))
{
foreach (var image in MnistImage.Load(imagesStream, labelsStream).Take(1000))
{
var t = new double[10];
for (int i = 0; i < 10; i++)
{
if (i == image.Label)
{
t[i] = 1.0;
}
else
{
t[i] = 0.0;
}
}
trainingList.Add(Tuple.Create <double[], double[]>(image.Normalize(), t));
}
}
using (Stream
imagesStream = assembly.GetManifestResourceStream("MNISTTest.t10k-images.idx3-ubyte"),
labelsStream = assembly.GetManifestResourceStream("MNISTTest.t10k-labels.idx1-ubyte"))
{
foreach (var image in MnistImage.Load(imagesStream, labelsStream).Take(1000))
{
var t = new double[10];
for (int i = 0; i < 10; i++)
{
if (i == image.Label)
{
t[i] = 1.0;
}
else
{
t[i] = 0.0;
}
}
testList.Add(Tuple.Create <double[], double[]>(image.Normalize(), t));
}
}
var inputLayer = new ConvolutionalPoolingLayer(channels, imageWidth, imageHeight, filters, filterWidth, filterHeight, poolWidth, poolHeight, new ReLU(), (index, fanIn, fanOut) => Initializers.HeNormal(fanIn));
var hiddenLayer = new FullyConnectedLayer(inputLayer, 100, new ReLU(), (index, fanIn, fanOut) => Initializers.HeNormal(fanIn));
var outputLayer = new SoftmaxLayer(hiddenLayer, 10, (index, fanIn, fanOut) => Initializers.GlorotNormal(fanIn, fanOut));
var network = new Network(inputLayer, outputLayer, new Adam(), new SoftmaxCrossEntropy());
int epochs = 50;
int iterations = 1;
network.Stepped += (sender, e) =>
{
double tptn = 0;
trainingList.ForEach(x =>
{
var vector = network.Predicate(x.Item1);
var i = ArgMax(vector);
var j = ArgMax(x.Item2);
if (i == j && Math.Round(vector[i]) == x.Item2[j])
{
tptn += 1.0;
}
});
var accuracy = tptn / trainingList.Count;
accuracyList.Add(accuracy);
lossList.Add(network.Loss);
Console.WriteLine("Epoch {0}/{1}", iterations, epochs);
Console.WriteLine("Accuracy: {0}, Loss: {1}", accuracy, network.Loss);
iterations++;
};
Console.WriteLine("Training...");
var stopwatch = Stopwatch.StartNew();
network.Train(trainingList, epochs, 100);
stopwatch.Stop();
Console.WriteLine("Done ({0}).", stopwatch.Elapsed.ToString());
double testTptn = 0;
testList.ForEach(x =>
{
var vector = network.Predicate(x.Item1);
var i = ArgMax(vector);
var j = ArgMax(x.Item2);
if (i == j && Math.Round(vector[i]) == x.Item2[j])
{
testTptn += 1.0;
}
});
Console.WriteLine("Accuracy: {0}", testTptn / testList.Count);
logDictionary.Add("Accuracy", accuracyList);
logDictionary.Add("Loss", lossList);
ToCsv(logPath, logDictionary);
Console.Write("Saved log to {0}...", logPath);
}
