Exemple #1
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        public NeuralNetwork(int nbInputs, int nbOuputs, List <int> innerLayerDescr)
        {
            _inputs  = new NeuralLayer(nbInputs);
            _outputs = new NeuralLayer(nbOuputs);

            for (int idxLayer = 0; idxLayer < innerLayerDescr.Count; idxLayer++)
            {
                _innerLayers.Add(new NeuralLayer(innerLayerDescr[idxLayer]));
            }

            NeuralLayer prevLayer = _outputs;

            for (int idxLayer = innerLayerDescr.Count - 1; idxLayer >= 0; idxLayer--)
            {
                NeuralLayer postLayer = idxLayer > 0 ? _innerLayers[idxLayer - 1] : _inputs;
                for (int idxNeuron = 0; idxNeuron < innerLayerDescr[idxLayer]; idxNeuron++)
                {
                    _innerLayers[idxLayer].Neurons.Add(new Neuron(postLayer, prevLayer));
                }
                _innerLayers[idxLayer].SetBias(new NeuronBias(prevLayer));
                prevLayer = _innerLayers[idxLayer];
            }

            for (int idxInput = 0; idxInput < nbInputs; idxInput++)
            {
                _inputs.Neurons.Add(new NeuronInput(prevLayer, ""));
            }
            _inputs.SetBias(new NeuronBias(prevLayer));

            for (int idxOutput = 0; idxOutput < nbOuputs; idxOutput++)
            {
                _outputs.Neurons.Add(new NeuronOutput(_innerLayers.Last(), ""));
            }
        }
Exemple #2
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        void AnnVisualizerForm_Paint(object sender, PaintEventArgs e)
        {
            Pen   pen          = new Pen(Color.FromArgb(255, 0, 0, 0));
            int   idxNeuron    = 0;
            Label curDest      = null;
            int   idxLayer     = 0;
            int   nextLayerIdx = _ann._outputs.Neurons.Count;

            foreach (var nn in _ann._outputs.Neurons)
            {
                var   nextOrigin = findWeightLabelOrig(idxNeuron++);
                int   idxWeight  = 0;
                Value max        = nn.Weights.Max();
                foreach (var nextNn in _ann._innerLayers[0].Neurons)
                {
                    curDest = findWeightLabelDest(nextLayerIdx++);
                    double strength = Math.Abs(nn.Weights[idxWeight++].X / max.X);
                    pen.Color = Color.FromArgb(255, (byte)(strength * 255.0), 0, (byte)((1.0 - strength) * 255.0));
                    e.Graphics.DrawLine(pen, nextOrigin.Location.X, nextOrigin.Location.Y, curDest.Location.X, curDest.Location.Y + 10);
                }
            }
            foreach (var layer in _ann._innerLayers)
            {
                int idxCurLayerNn = idxNeuron + layer.Neurons.Count;
                foreach (var nn in layer.Neurons)
                {
                    var         nextOrigin = findWeightLabelOrig(idxNeuron++);
                    NeuralLayer nextLayer  = null;
                    if (_ann._innerLayers.Count > idxLayer + 1)
                    {
                        nextLayer = _ann._innerLayers[idxLayer + 1];
                    }
                    else
                    {
                        nextLayer = _ann._inputs;
                    }
                    nextLayerIdx = idxCurLayerNn;
                    if (nn.Weights.Count > 0)
                    {
                        Value max       = nn.Weights.Max();
                        int   idxWeight = 0;
                        foreach (var nextNn in nextLayer.Neurons)
                        {
                            curDest = findWeightLabelDest(nextLayerIdx++);
                            double strength = Math.Abs(nn.Weights[idxWeight++].X / max.X);
                            pen.Color = Color.FromArgb(255, (byte)(strength * 255.0), 0, (byte)((1.0 - strength) * 255.0));
                            e.Graphics.DrawLine(pen, nextOrigin.Location.X, nextOrigin.Location.Y, curDest.Location.X, curDest.Location.Y + 10);
                        }
                    }
                }
                idxLayer++;
            }
        }
Exemple #3
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        public AnnVisualizerForm(NeuralNetwork ann)
        {
            _ann = ann;
            InitializeComponent();
            ResizeEnd += AnnVisualizerForm_Resize;
            Paint += AnnVisualizerForm_Paint;

            var tlayer = new NeuralLayer(1);
            Neuron input = new NeuronInput(tlayer, "X");
            tlayer.Neurons.Add(input);
            _transferFunc = new Neuron(tlayer, new NeuralLayer(0), "transfer");
            _transferFunc.Weights[0].X = 1.0;

            Recompute();
        }
Exemple #4
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        public AnnVisualizerForm(NeuralNetwork ann)
        {
            _ann = ann;
            InitializeComponent();
            ResizeEnd += AnnVisualizerForm_Resize;
            Paint     += AnnVisualizerForm_Paint;

            var    tlayer = new NeuralLayer(1);
            Neuron input  = new NeuronInput(tlayer, "X");

            tlayer.Neurons.Add(input);
            _transferFunc = new Neuron(tlayer, new NeuralLayer(0), "transfer");
            _transferFunc.Weights[0].X = 1.0;

            Recompute();
        }
Exemple #5
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        public Neuron(NeuralLayer children, NeuralLayer parentLayer, string label = "hidden")
        {
            _parents  = parentLayer;
            _children = children;
            _label    = label;
            int size = children.Size;

            while (size-- > 0)
            {
                Weights.Add(new Value());
            }
            if (children.Size > 0) // Bias neuron
            {
                Weights.Add(new Value());
            }
            size = parentLayer.Size;
            while (size-- > 0)
            {
                Deltas.Add(new Value());
            }
        }
Exemple #6
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 public NeuronBias(NeuralLayer nextLayer)
     : base(nextLayer, "bias")
 {
     Value.X = 1.0;
 }
Exemple #7
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 public NeuronInput(NeuralLayer firstInnerLayer, string label)
     : base(new NeuralLayer(0), firstInnerLayer, label)
 {
 }
Exemple #8
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 public NeuronOutput(NeuralLayer outputLayer, string label)
     : base(outputLayer, new NeuralLayer(0), label)
 {
 }
Exemple #9
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        public NeuralNetwork(int nbInputs, int nbOuputs, List<int> innerLayerDescr)
        {
            _inputs = new NeuralLayer(nbInputs);
            _outputs = new NeuralLayer(nbOuputs);

            for (int idxLayer = 0; idxLayer < innerLayerDescr.Count; idxLayer++)
                _innerLayers.Add(new NeuralLayer(innerLayerDescr[idxLayer]));

            NeuralLayer prevLayer = _outputs;
            for (int idxLayer = innerLayerDescr.Count - 1; idxLayer >= 0; idxLayer--)
            {
                NeuralLayer postLayer = idxLayer > 0 ? _innerLayers[idxLayer - 1] : _inputs;
                for (int idxNeuron = 0; idxNeuron < innerLayerDescr[idxLayer]; idxNeuron++)
                    _innerLayers[idxLayer].Neurons.Add(new Neuron(postLayer, prevLayer));
                _innerLayers[idxLayer].SetBias(new NeuronBias(prevLayer));
                prevLayer = _innerLayers[idxLayer];
            }

            for (int idxInput = 0; idxInput < nbInputs; idxInput++)
                _inputs.Neurons.Add(new NeuronInput(prevLayer, ""));
            _inputs.SetBias(new NeuronBias(prevLayer));

            for (int idxOutput = 0; idxOutput < nbOuputs; idxOutput++)
                _outputs.Neurons.Add(new NeuronOutput(_innerLayers.Last(), ""));
        }
Exemple #10
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 public NeuronOutput(NeuralLayer outputLayer, string label)
     : base(outputLayer, new NeuralLayer(0), label)
 {
 }
Exemple #11
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 public NeuronInput(NeuralLayer firstInnerLayer, string label)
     : base(new NeuralLayer(0), firstInnerLayer, label)
 {
 }
Exemple #12
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 public NeuronBias(NeuralLayer nextLayer)
     : base(nextLayer, "bias")
 {
     Value.X = 1.0;
 }
Exemple #13
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 public Neuron(NeuralLayer children, NeuralLayer parentLayer, string label = "hidden")
 {
     _parents = parentLayer;
     _children = children;
     _label = label;
     int size = children.Size;
     while (size--> 0)
         Weights.Add(new Value());
     if (children.Size > 0) // Bias neuron
         Weights.Add(new Value());
     size = parentLayer.Size;
     while (size--> 0)
         Deltas.Add(new Value());
 }