public NeuralNetwork Read()
{
NeuralNetwork net;
using (BinaryReader reader = new BinaryReader(new FileStream(fileName, FileMode.Open)))
{
reader.BaseStream.Seek(0x18600, SeekOrigin.Begin);
double learningRate = reader.ReadDouble();
net = new NeuralNetwork(learningRate);
int layersCount = reader.ReadInt32();
NNLayer layer = null;
for (int i = 0; i < layersCount; i++)
{
string layerName = ReadString(reader);
layer = new NNLayer(layer);
net.Layers.Add(layer);
int neuronsCount = reader.ReadInt32();
int weightsCount = reader.ReadInt32();
for (int j = 0; j < neuronsCount; j++)
{
string neuronName = ReadString(reader);
NNNeuron neuron = new NNNeuron();
layer.Neurons.Add(neuron);
int connectionsCount = reader.ReadInt32();
for (int k = 0; k < connectionsCount; k++)
{
uint neuronIndex = reader.ReadUInt32();
uint weightIndex = reader.ReadUInt32();
NNConnection connection = new NNConnection(neuronIndex, weightIndex);
neuron.Connections.Add(connection);
}
}
for (int j = 0; j < weightsCount; j++)
{
string weightName = ReadString(reader);
double value = reader.ReadDouble();
NNWeight weight = new NNWeight {
Value = value
};
layer.Weights.Add(weight);
}
}
}
return(net);
}
/////////////////////////
private bool CreateNNNetWork(NeuralNetwork network)
{
NNLayer pLayer;
int ii, jj, kk;
int icNeurons = 0;
int icWeights = 0;
double initWeight;
String sLabel;
var m_rdm = new Random();
// layer zero, the input layer.
// Create neurons: exactly the same number of neurons as the input
// vector of 29x29=841 pixels, and no weights/connections
pLayer = new NNLayer("Layer00", null);
network.m_Layers.Add(pLayer);
for (ii = 0; ii < 841; ii++)
{
sLabel = String.Format("Layer00_Neuro{0}_Num{1}", ii, icNeurons);
pLayer.m_Neurons.Add(new NNNeuron(sLabel));
icNeurons++;
}
//double UNIFORM_PLUS_MINUS_ONE= (double)(2.0 * m_rdm.Next())/Constants.RAND_MAX - 1.0 ;
// layer one:
// This layer is a convolutional layer that has 6 feature maps. Each feature
// map is 13x13, and each unit in the feature maps is a 5x5 convolutional kernel
// of the input layer.
// So, there are 13x13x6 = 1014 neurons, (5x5+1)x6 = 156 weights
pLayer = new NNLayer("Layer01", pLayer);
network.m_Layers.Add(pLayer);
for (ii = 0; ii < 1014; ii++)
{
sLabel = String.Format("Layer01_Neuron{0}_Num{1}", ii, icNeurons);
pLayer.m_Neurons.Add(new NNNeuron(sLabel));
icNeurons++;
}
for (ii = 0; ii < 156; ii++)
{
sLabel = String.Format("Layer01_Weigh{0}_Num{1}", ii, icWeights);
initWeight = 0.05 * (2.0 * m_rdm.NextDouble() - 1.0);
pLayer.m_Weights.Add(new NNWeight(sLabel, initWeight));
}
// interconnections with previous layer: this is difficult
// The previous layer is a top-down bitmap image that has been padded to size 29x29
// Each neuron in this layer is connected to a 5x5 kernel in its feature map, which
// is also a top-down bitmap of size 13x13. We move the kernel by TWO pixels, i.e., we
// skip every other pixel in the input image
int[] kernelTemplate = new int[25] {
0, 1, 2, 3, 4,
29, 30, 31, 32, 33,
58, 59, 60, 61, 62,
87, 88, 89, 90, 91,
116, 117, 118, 119, 120
};
int iNumWeight;
int fm;
for (fm = 0; fm < 6; fm++)
{
for (ii = 0; ii < 13; ii++)
{
for (jj = 0; jj < 13; jj++)
{
iNumWeight = fm * 26; // 26 is the number of weights per feature map
NNNeuron n = pLayer.m_Neurons[jj + ii * 13 + fm * 169];
n.AddConnection((uint)MyDefinations.ULONG_MAX, (uint)iNumWeight++); // bias weight
for (kk = 0; kk < 25; kk++)
{
// note: max val of index == 840, corresponding to 841 neurons in prev layer
n.AddConnection((uint)(2 * jj + 58 * ii + kernelTemplate[kk]), (uint)iNumWeight++);
}
}
}
}
// layer two:
// This layer is a convolutional layer that has 50 feature maps. Each feature
// map is 5x5, and each unit in the feature maps is a 5x5 convolutional kernel
// of corresponding areas of all 6 of the previous layers, each of which is a 13x13 feature map
// So, there are 5x5x50 = 1250 neurons, (5x5+1)x6x50 = 7800 weights
pLayer = new NNLayer("Layer02", pLayer);
network.m_Layers.Add(pLayer);
for (ii = 0; ii < 1250; ii++)
{
sLabel = String.Format("Layer02_Neuron{0}_Num{1}", ii, icNeurons);
pLayer.m_Neurons.Add(new NNNeuron(sLabel));
icNeurons++;
}
for (ii = 0; ii < 7800; ii++)
{
sLabel = String.Format("Layer02_Weight{0}_Num{1}", ii, icWeights);
initWeight = 0.05 * (2.0 * m_rdm.NextDouble() - 1.0);
pLayer.m_Weights.Add(new NNWeight(sLabel, initWeight));
}
// Interconnections with previous layer: this is difficult
// Each feature map in the previous layer is a top-down bitmap image whose size
// is 13x13, and there are 6 such feature maps. Each neuron in one 5x5 feature map of this
// layer is connected to a 5x5 kernel positioned correspondingly in all 6 parent
// feature maps, and there are individual weights for the six different 5x5 kernels. As
// before, we move the kernel by TWO pixels, i.e., we
// skip every other pixel in the input image. The result is 50 different 5x5 top-down bitmap
// feature maps
int[] kernelTemplate2 = new int[25] {
0, 1, 2, 3, 4,
13, 14, 15, 16, 17,
26, 27, 28, 29, 30,
39, 40, 41, 42, 43,
52, 53, 54, 55, 56
};
for (fm = 0; fm < 50; fm++)
{
for (ii = 0; ii < 5; ii++)
{
for (jj = 0; jj < 5; jj++)
{
iNumWeight = fm * 156; // 26 is the number of weights per feature map
NNNeuron n = pLayer.m_Neurons[jj + ii * 5 + fm * 25];
n.AddConnection((uint)MyDefinations.ULONG_MAX, (uint)iNumWeight++); // bias weight
for (kk = 0; kk < 25; kk++)
{
// note: max val of index == 1013, corresponding to 1014 neurons in prev layer
n.AddConnection((uint)(2 * jj + 26 * ii + kernelTemplate2[kk]), (uint)iNumWeight++);
n.AddConnection((uint)(169 + 2 * jj + 26 * ii + kernelTemplate2[kk]), (uint)iNumWeight++);
n.AddConnection((uint)(338 + 2 * jj + 26 * ii + kernelTemplate2[kk]), (uint)iNumWeight++);
n.AddConnection((uint)(507 + 2 * jj + 26 * ii + kernelTemplate2[kk]), (uint)iNumWeight++);
n.AddConnection((uint)(676 + 2 * jj + 26 * ii + kernelTemplate2[kk]), (uint)iNumWeight++);
n.AddConnection((uint)(845 + 2 * jj + 26 * ii + kernelTemplate2[kk]), (uint)iNumWeight++);
}
}
}
}
// layer three:
// This layer is a fully-connected layer with 100 units. Since it is fully-connected,
// each of the 100 neurons in the layer is connected to all 1250 neurons in
// the previous layer.
// So, there are 100 neurons and 100*(1250+1)=125100 weights
pLayer = new NNLayer("Layer03", pLayer);
network.m_Layers.Add(pLayer);
for (ii = 0; ii < 100; ii++)
{
sLabel = String.Format("Layer03_Neuron{0}_Num{1}", ii, icNeurons);
pLayer.m_Neurons.Add(new NNNeuron(sLabel));
icNeurons++;
}
for (ii = 0; ii < 125100; ii++)
{
sLabel = String.Format("Layer03_Weight{0}_Num{1}", ii, icWeights);
initWeight = 0.05 * (2.0 * m_rdm.NextDouble() - 1.0);
pLayer.m_Weights.Add(new NNWeight(sLabel, initWeight));
}
// Interconnections with previous layer: fully-connected
iNumWeight = 0; // weights are not shared in this layer
for (fm = 0; fm < 100; fm++)
{
NNNeuron n = pLayer.m_Neurons[fm];
n.AddConnection((uint)MyDefinations.ULONG_MAX, (uint)iNumWeight++); // bias weight
for (ii = 0; ii < 1250; ii++)
{
n.AddConnection((uint)ii, (uint)iNumWeight++);
}
}
// layer four, the final (output) layer:
// This layer is a fully-connected layer with 10 units. Since it is fully-connected,
// each of the 10 neurons in the layer is connected to all 100 neurons in
// the previous layer.
// So, there are 10 neurons and 10*(100+1)=1010 weights
pLayer = new NNLayer("Layer04", pLayer);
network.m_Layers.Add(pLayer);
for (ii = 0; ii < 10; ii++)
{
sLabel = String.Format("Layer04_Neuron{0}_Num{1}", ii, icNeurons);
pLayer.m_Neurons.Add(new NNNeuron(sLabel));
icNeurons++;
}
for (ii = 0; ii < 1010; ii++)
{
sLabel = String.Format("Layer04_Weight{0}_Num{1}", ii, icWeights);
initWeight = 0.05 * (2.0 * m_rdm.NextDouble() - 1.0);
pLayer.m_Weights.Add(new NNWeight(sLabel, initWeight));
}
// Interconnections with previous layer: fully-connected
iNumWeight = 0; // weights are not shared in this layer
for (fm = 0; fm < 10; fm++)
{
var n = pLayer.m_Neurons[fm];
n.AddConnection((uint)MyDefinations.ULONG_MAX, (uint)iNumWeight++); // bias weight
for (ii = 0; ii < 100; ii++)
{
n.AddConnection((uint)ii, (uint)iNumWeight++);
}
}
return(true);
}
