示例#1
0
        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);
            }
        }
示例#2
0
 public void AddNeuronToLayer(RawInputLayerNeuron riln)
 {
     LayerNeurons.Add((object)riln);
 }