public RecogAkshaysPictureNetwork()
{
Globals.LearningRateForNeurons = (decimal)0.00000000000000000043368086899420177360298112034798;
NeuralLayer rawInputLayer = new NeuralLayer();
for (int i = 0; i < 1024 * 1024; i++)
{
RawInputLayerNeuron riln = new RawInputLayerNeuron((decimal)0, (decimal)2097152);
rawInputLayer.AddNeuronToLayer(riln);
}
Layers.Add(rawInputLayer);
NeuralLayer hiddenLayer = new NeuralLayer();
for (int i = 0; i < 1024 * 1024; i++)
{
HiddenLayerNeuron hln = new HiddenLayerNeuron((decimal)2199023255552);
hiddenLayer.AddNeuronToLayer(hln);
}
Layers.Add(hiddenLayer);
foreach (HiddenLayerNeuron hln in hiddenLayer.LayerNeurons)
{
foreach (RawInputLayerNeuron riln in rawInputLayer.LayerNeurons)
{
NeuralConnection nc = new NeuralConnection(riln, hln, (decimal)2097152);
hln.Inputs.Add(nc);
riln.Outputs.Add(nc);
}
}
NeuralLayer outputLayer = new NeuralLayer();
RawOutputLayerNeuron roln = new RawOutputLayerNeuron((decimal)2305843009213693952);
foreach (HiddenLayerNeuron hln in hiddenLayer.LayerNeurons)
{
NeuralConnection nc = new NeuralConnection(hln, roln, (decimal)2199023255552);
hln.Outputs.Add(nc);
roln.Inputs.Add(nc);
}
}
public void AddNeuronToLayer(RawInputLayerNeuron riln)
{
LayerNeurons.Add((object)riln);
}