/// <summary>
/// Build the synapse and layer structure. This method should be called after
/// you are done adding layers to a network, or change the network's logic
/// property.
/// </summary>
///
public void FinalizeStructure()
{
if (_layers.Count < 2)
{
throw new NeuralNetworkError(
"There must be at least two layers before the structure is finalized.");
}
var flatLayers = new FlatLayer[_layers.Count];
for (int i = 0; i < _layers.Count; i++)
{
var layer = (BasicLayer)_layers[i];
if (layer.Activation == null)
{
layer.Activation = new ActivationLinear();
}
flatLayers[i] = layer;
}
_flat = new FlatNetwork(flatLayers);
FinalizeLimit();
_layers.Clear();
EnforceLimit();
}
/// <inheritdoc />
public string Part2()
{
int imageSize = 25 * 6;
//List<Layer> layers = new List<Layer>(ints.Count / imageSize);
List <FlatLayer> layers = new List <FlatLayer>(ints.Count / imageSize);
for (int i = 0; i < ints.Count / imageSize; i++)
{
//layers.Add(new Layer(25, ints.Skip(i * imageSize).Take(imageSize).ToList()));
layers.Add(new FlatLayer(ints.Skip(i * imageSize).Take(imageSize).ToList()));
}
FlatLayer output = new FlatLayer(new List <int>());
for (int i = 0; i < imageSize; i++)
{
for (int j = 0; j < layers.Count; j++)
{
if (layers[j].Pixels[i] < 2)
{
output.Pixels.Add(layers[j].Pixels[i]);
break;
}
}
// transparent all the way down
if (output.Pixels.Count < i)
{
output.Pixels.Add(2);
}
}
for (int i = 0; i < 6; i++)
{
for (int j = 0; j < 25; j++)
{
switch (output.Pixels[i * 25 + j])
{
case 0:
Console.BackgroundColor = ConsoleColor.Black;
Console.ForegroundColor = ConsoleColor.White;
Console.Write(output.Pixels[i * 25 + j]);
Console.ResetColor();
break;
case 1:
Console.BackgroundColor = ConsoleColor.White;
Console.ForegroundColor = ConsoleColor.Black;
Console.Write(output.Pixels[i * 25 + j]);
Console.ResetColor();
break;
}
}
Console.Write("\n");
}
return($"");
}
public static FlatNetwork ToFlatNetwork(this MatrixNetwork @this)
{
var net = new FlatNetwork();
foreach (var layer in @this.Layers)
{
var flatLayer = new FlatLayer();
for (var i = 0; i < layer.Weights.RowCount; i++)
{
var el = new { w = layer.Weights.Row(i), b = layer.Biases[i] };
var flatNeuron = new FlatNeuron();
flatNeuron.Weights.AddRange(el.w);
flatNeuron.Bias = el.b;
flatLayer.Neurons.Add(flatNeuron);
}
net.Layers.Add(flatLayer);
}
return(net);
}
/// <summary>
/// Create the flat neural network.
/// </summary>
public void Flatten()
{
bool isRBF = false;
IDictionary <ILayer, FlatLayer> regular2flat = new Dictionary <ILayer, FlatLayer>();
IDictionary <FlatLayer, ILayer> flat2regular = new Dictionary <FlatLayer, ILayer>();
IList <ObjectPair <ILayer, ILayer> > contexts = new List <ObjectPair <ILayer, ILayer> >();
this.flat = null;
ValidateForFlat val = new ValidateForFlat();
if (val.IsValid(this.network) == null)
{
if (this.layers.Count == 3 &&
this.layers[1] is RadialBasisFunctionLayer)
{
RadialBasisFunctionLayer rbf = (RadialBasisFunctionLayer)this.layers[1];
this.flat = new FlatNetworkRBF(this.network.InputCount,
rbf.NeuronCount, this.network.OutputCount,
rbf.RadialBasisFunction);
FlattenWeights();
this.flatUpdate = FlatUpdateNeeded.None;
return;
}
int flatLayerCount = CountNonContext();
FlatLayer[] flatLayers = new FlatLayer[flatLayerCount];
int index = flatLayers.Length - 1;
foreach (ILayer layer in this.layers)
{
if (layer is ContextLayer)
{
ISynapse inboundSynapse = network.Structure
.FindPreviousSynapseByLayerType(layer,
typeof(BasicLayer));
ISynapse outboundSynapse = network
.Structure
.FindNextSynapseByLayerType(layer, typeof(BasicLayer));
if (inboundSynapse == null)
{
throw new NeuralNetworkError(
"Context layer must be connected to by one BasicLayer.");
}
if (outboundSynapse == null)
{
throw new NeuralNetworkError(
"Context layer must connect to by one BasicLayer.");
}
ILayer inbound = inboundSynapse.FromLayer;
ILayer outbound = outboundSynapse.ToLayer;
contexts
.Add(new ObjectPair <ILayer, ILayer>(inbound, outbound));
}
else
{
double bias = this.FindNextBias(layer);
IActivationFunction activationType;
double[] param = new double[1];
if (layer.ActivationFunction == null)
{
activationType = new ActivationLinear();
param = new double[1];
param[0] = 1;
}
else
{
activationType = layer.ActivationFunction;
param = layer.ActivationFunction.Params;
}
FlatLayer flatLayer = new FlatLayer(activationType, layer
.NeuronCount, bias, param);
regular2flat[layer] = flatLayer;
flat2regular[flatLayer] = layer;
flatLayers[index--] = flatLayer;
}
}
// now link up the context layers
foreach (ObjectPair <ILayer, ILayer> context in contexts)
{
// link the context layer on the FlatLayer
ILayer layer = context.B;
ISynapse synapse = this.network
.Structure
.FindPreviousSynapseByLayerType(layer, typeof(BasicLayer));
FlatLayer from = regular2flat[context.A];
FlatLayer to = regular2flat[synapse.FromLayer];
to.ContextFedBy = from;
}
this.flat = new FlatNetwork(flatLayers);
// update the context indexes on the non-flat network
for (int i = 0; i < flatLayerCount; i++)
{
FlatLayer fedBy = flatLayers[i].ContextFedBy;
if (fedBy != null)
{
ILayer fedBy2 = flat2regular[flatLayers[i + 1]];
ISynapse synapse = FindPreviousSynapseByLayerType(fedBy2, typeof(ContextLayer));
if (synapse == null)
{
throw new NeuralNetworkError("Can't find parent synapse to context layer.");
}
ContextLayer context = (ContextLayer)synapse.FromLayer;
// find fedby index
int fedByIndex = -1;
for (int j = 0; j < flatLayerCount; j++)
{
if (flatLayers[j] == fedBy)
{
fedByIndex = j;
break;
}
}
if (fedByIndex == -1)
{
throw new NeuralNetworkError("Can't find layer feeding context.");
}
context.FlatContextIndex = this.flat.ContextTargetOffset[fedByIndex];
}
}
// RBF networks will not train every layer
if (isRBF)
{
this.flat.EndTraining = flatLayers.Length - 1;
}
FlattenWeights();
if (this.IsConnectionLimited)
{
}
this.flatUpdate = FlatUpdateNeeded.None;
}
else
{
this.flatUpdate = FlatUpdateNeeded.Never;
}
}
public examML()
{
layer1 = new FlatLayer(2);
layer2 = new NodeLayer(2);
}
