public ILayer GetLayer()
{
var activation = GetActivationFunction();
ILayer toReturn;
switch (LayerType)
{
case NeuralLayerType.BasicLayer:
toReturn = new BasicLayer(activation, HasBias, NeuronsPerDimension.All.ToArray());
break;
case NeuralLayerType.Conv2D:
toReturn = new Conv2DLayer(activation, ConvNbFilters, ConvNbRows, ConvNbColumns);
break;
case NeuralLayerType.Dropout:
toReturn = new DropoutLayer(activation, HasBias, NeuronsPerDimension.One, DropOut);
break;
case NeuralLayerType.MaxPool:
toReturn = new MaxPoolLayer(NeuronsPerDimension.All.ToArray());
break;
default:
throw new ArgumentOutOfRangeException();
}
return(toReturn);
}
private static Network CreateNewNetwork()
{
var num_inputs = 27; // 9 eyes, each sees 3 numbers (wall, green, red thing proximity)
var num_actions = 5; // 5 possible angles agent can turn
var temporal_window = 1; // amount of temporal memory. 0 = agent lives in-the-moment :)
var network_size = num_inputs * temporal_window + num_actions * temporal_window + num_inputs;
Network net = new Network();
InputLayer il = new InputLayer();
il.OutputWidth = 1;
il.OutputHeight = 1;
il.OutputDepth = network_size;
net.Layers.Add(il);
ConvLayer conv = new ConvLayer(16, 5, 5, il.OutputDepth, il.OutputWidth, il.OutputHeight, 1, 2, 0, 1, 0.1);
net.Layers.Add(conv);
ReluLayer rlv = new ReluLayer(conv.OutputDepth, conv.OutputWidth, conv.OutputHeight);
net.Layers.Add(rlv);
MaxPoolLayer pl = new MaxPoolLayer(2, 2, rlv.OutputDepth, rlv.OutputWidth, rlv.OutputHeight, 2, 0, 0);
net.Layers.Add(pl);
FullyConnLayer fc = new FullyConnLayer(50, pl.OutputDepth, pl.OutputWidth, pl.OutputHeight, 0, 1, 0);
net.Layers.Add(fc);
ReluLayer rl = new ReluLayer(fc.OutputDepth, fc.OutputWidth, fc.OutputHeight);
net.Layers.Add(rl);
FullyConnLayer fc2 = new FullyConnLayer(50, rl.OutputDepth, rl.OutputWidth, rl.OutputHeight, 0, 1, 0);
net.Layers.Add(fc2);
ReluLayer rl2 = new ReluLayer(fc2.OutputDepth, fc2.OutputWidth, fc2.OutputHeight);
net.Layers.Add(rl2);
FullyConnLayer fc8 = new FullyConnLayer(5, rl2.OutputDepth, rl2.OutputWidth, rl2.OutputHeight, 0, 1, 0);
net.Layers.Add(fc8);
RegressionLayer sl = new RegressionLayer(fc8.OutputDepth, fc8.OutputWidth, fc8.OutputHeight);
net.LossLayer = sl;
return(net);
}
private static Network CreateNewNetwork()
{
Network net = new Network();
InputLayer il = new InputLayer();
il.OutputWidth = 32;
il.OutputHeight = 32;
il.OutputDepth = 3;
net.Layers.Add(il);
ConvLayer conv = new ConvLayer(16, 5, 5, 3, 32, 32, 1, 2, 0, 1, 0.1);
net.Layers.Add(conv);
ReluLayer rl = new ReluLayer(conv.OutputDepth, conv.OutputWidth, conv.OutputHeight);
net.Layers.Add(rl);
MaxPoolLayer pl = new MaxPoolLayer(2, 2, rl.OutputDepth, rl.OutputWidth, rl.OutputHeight, 2, 0, 0);
net.Layers.Add(pl);
ConvLayer conv2 = new ConvLayer(20, 5, 5, pl.OutputDepth, pl.OutputWidth, pl.OutputHeight, 1, 2, 0, 1, 0.1);
net.Layers.Add(conv2);
ReluLayer rl2 = new ReluLayer(conv2.OutputDepth, conv2.OutputWidth, conv2.OutputHeight);
net.Layers.Add(rl2);
MaxPoolLayer pl2 = new MaxPoolLayer(2, 2, rl2.OutputDepth, rl2.OutputWidth, rl2.OutputHeight, 2, 0, 0);
net.Layers.Add(pl2);
ConvLayer conv3 = new ConvLayer(20, 5, 5, pl2.OutputDepth, pl2.OutputWidth, pl2.OutputHeight, 1, 2, 0, 1, 0.1);
net.Layers.Add(conv3);
ReluLayer rl3 = new ReluLayer(conv3.OutputDepth, conv3.OutputWidth, conv3.OutputHeight);
net.Layers.Add(rl3);
MaxPoolLayer pl3 = new MaxPoolLayer(2, 2, rl3.OutputDepth, rl3.OutputWidth, rl3.OutputHeight, 2, 0, 0);
net.Layers.Add(pl3);
FullyConnLayer fc = new FullyConnLayer(10, pl3.OutputDepth, pl3.OutputWidth, pl3.OutputHeight, 0, 1, 0);
net.Layers.Add(fc);
SoftmaxLayer sl = new SoftmaxLayer(fc.OutputDepth, fc.OutputWidth, fc.OutputHeight);
net.LossLayer = sl;
return(net);
}
/// <summary>
/// Instantiates Layer from LayerData and sets initial values.
/// </summary>
/// <param name="l"></param>
/// <param name="input"></param>
/// <param name="isOutput"></param>
/// <returns></returns>
GameObject InstantiateLayer(LayerData l, GameObject input, bool isOutput)
{
switch (l.type)
{
case LayerType.IMAGE:
{
GameObject go = GetImageLayerPrefab();
GameObject inst = Instantiate(go);
ImageLayer imLayer = inst.GetComponent <ImageLayer>();
imLayer.reducedResolution = new Vector2Int(l.activationShape[1], l.activationShape[2]);
imLayer.fullResolution = new Vector2Int(l.activationShape[1], l.activationShape[2]);
imLayer.pixelSpacing = 0.025f;
imLayer.depth = l.activationShape[3];
GameObject canvas = GameObject.FindGameObjectWithTag("Canvas");
Material pixelMaterial = canvas.GetComponent <GuiManager>().pixelMaterial;
imLayer.rgb = false;
MeshRenderer meshRenderer = inst.GetComponent <MeshRenderer>();
meshRenderer.sharedMaterials[0] = pixelMaterial;
imLayer.SetActivationTensorShape(l.activationShape);
for (int i = 0; i < l.activationTensors.Count; i++)
{
imLayer.SetActivationTensorForEpoch(l.activationTensors[i], i);
}
return(inst);
}
case LayerType.CONV:
{
GameObject go = GetConvLayerPrefab();
GameObject inst = Instantiate(go);
ConvLayer convLayer = inst.GetComponent <ConvLayer>();
convLayer.convShape = new Vector2Int(l.weightShape[0], l.weightShape[1]);
convLayer.reducedDepth = l.weightShape[3];
convLayer.fullDepth = l.weightShape[3];
convLayer.input = input;
convLayer.filterSpread = 1.0f;
convLayer.lineCircleGrid = 2.0f;
convLayer.filterSpacing = 0.025f;
convLayer.SetWeightTensorShape(l.weightShape);
for (int i = 0; i < l.weightTensors.Count; i++)
{
convLayer.SetWeightTensorForEpoch(l.weightTensors[i], i);
}
convLayer.SetActivationTensorShape(l.activationShape);
for (int i = 0; i < l.activationTensors.Count; i++)
{
convLayer.SetActivationTensorForEpoch(l.activationTensors[i], i);
}
return(inst);
}
case LayerType.MAXPOOL:
{
GameObject go = GetMaxPoolLayerPrefab();
GameObject inst = Instantiate(go);
MaxPoolLayer mpLayer = inst.GetComponent <MaxPoolLayer>();
mpLayer.filterSpacing = 0.025f;
mpLayer.zOffset = 0.25f;
mpLayer.input = input;
mpLayer.SetActivationTensorShape(l.activationShape);
for (int i = 0; i < l.activationTensors.Count; i++)
{
mpLayer.SetActivationTensorForEpoch(l.activationTensors[i], i);
}
return(inst);
}
case LayerType.FC:
{
GameObject go = GetFCLayerPrefab();
GameObject inst = Instantiate(go);
FCLayer fcLayer = inst.GetComponent <FCLayer>();
fcLayer.input = input;
fcLayer.filterSpacing = 0.025f;
fcLayer.reducedDepth = l.weightShape[1];
fcLayer.fullDepth = l.weightShape[1];
//TODO: here loading is reducing non output fc layers automatically by 4
if (!l.name.Contains("out"))
{
fcLayer.reducedDepth = l.weightShape[1];
}
else
{
fcLayer.lineCircleGrid = 0;
}
if (l.name.Contains("0"))
{
fcLayer.collapseInput = 1f;
}
//fcLayer.edgeBundle = 1.0f;
fcLayer.SetTensorShape(l.weightShape);
for (int i = 0; i < l.weightTensors.Count; i++)
{
fcLayer.SetTensorForEpoch(l.weightTensors[i], i);
}
fcLayer.SetActivationTensorShape(l.activationShape);
for (int i = 0; i < l.activationTensors.Count; i++)
{
fcLayer.SetActivationTensorForEpoch(l.activationTensors[i], i);
}
return(inst);
}
default:
break;
}
return(null);
}
public static Network CreateVGG16Network(int imageWidth, int imageHeight, int LabelsCount)
{
Network net = new Network();
InputLayer il = new InputLayer();
il.OutputWidth = imageWidth;
il.OutputHeight = imageHeight;
il.OutputDepth = 3;
net.Layers.Add(il);
ConvLayer conv1_1 = new ConvLayer(64, 3, 3, 3, imageWidth, imageHeight, 1, 1, 0, 1, 0.1f);
net.Layers.Add(conv1_1);
ReluLayer rl1 = new ReluLayer(conv1_1.OutputDepth, conv1_1.OutputWidth, conv1_1.OutputHeight);
net.Layers.Add(rl1);
ConvLayer conv1_2 = new ConvLayer(64, 3, 3, rl1.OutputDepth, rl1.OutputWidth, rl1.OutputHeight, 1, 1, 0, 1, 0.1f);
net.Layers.Add(conv1_2);
ReluLayer rl2 = new ReluLayer(conv1_2.OutputDepth, conv1_2.OutputWidth, conv1_2.OutputHeight);
net.Layers.Add(rl2);
MaxPoolLayer pl1 = new MaxPoolLayer(2, 2, rl2.OutputDepth, rl2.OutputWidth, rl2.OutputHeight, 2, 0);
net.Layers.Add(pl1);
ConvLayer conv2_1 = new ConvLayer(128, 3, 3, pl1.OutputDepth, pl1.OutputWidth, pl1.OutputHeight, 1, 1, 0, 1, 0.1f);
net.Layers.Add(conv2_1);
ReluLayer rl3 = new ReluLayer(conv2_1.OutputDepth, conv2_1.OutputWidth, conv2_1.OutputHeight);
net.Layers.Add(rl3);
ConvLayer conv2_2 = new ConvLayer(128, 3, 3, rl3.OutputDepth, rl3.OutputWidth, rl3.OutputHeight, 1, 1, 0, 1, 0.1f);
net.Layers.Add(conv2_2);
ReluLayer rl4 = new ReluLayer(conv2_2.OutputDepth, conv2_2.OutputWidth, conv2_2.OutputHeight);
net.Layers.Add(rl4);
MaxPoolLayer pl2 = new MaxPoolLayer(2, 2, rl4.OutputDepth, rl4.OutputWidth, rl4.OutputHeight, 2, 0);
net.Layers.Add(pl2);
ConvLayer conv3_1 = new ConvLayer(256, 3, 3, pl2.OutputDepth, pl2.OutputWidth, pl2.OutputHeight, 1, 1, 0, 1, 0.1f);
net.Layers.Add(conv3_1);
ReluLayer rl5 = new ReluLayer(conv3_1.OutputDepth, conv3_1.OutputWidth, conv3_1.OutputHeight);
net.Layers.Add(rl5);
ConvLayer conv3_2 = new ConvLayer(256, 3, 3, rl5.OutputDepth, rl5.OutputWidth, rl5.OutputHeight, 1, 1, 0, 1, 0.1f);
net.Layers.Add(conv3_2);
ReluLayer rl6 = new ReluLayer(conv3_2.OutputDepth, conv3_2.OutputWidth, conv3_2.OutputHeight);
net.Layers.Add(rl6);
ConvLayer conv3_3 = new ConvLayer(256, 3, 3, rl6.OutputDepth, rl6.OutputWidth, rl6.OutputHeight, 1, 1, 0, 1, 0.1f);
net.Layers.Add(conv3_3);
ReluLayer rl7 = new ReluLayer(conv3_3.OutputDepth, conv3_3.OutputWidth, conv3_3.OutputHeight);
net.Layers.Add(rl7);
MaxPoolLayer pl3 = new MaxPoolLayer(2, 2, rl7.OutputDepth, rl7.OutputWidth, rl7.OutputHeight, 2, 0);
net.Layers.Add(pl3);
ConvLayer conv4_1 = new ConvLayer(512, 3, 3, pl3.OutputDepth, pl3.OutputWidth, pl3.OutputHeight, 1, 1, 0, 1, 0.1f);
net.Layers.Add(conv4_1);
ReluLayer rl8 = new ReluLayer(conv4_1.OutputDepth, conv4_1.OutputWidth, conv4_1.OutputHeight);
net.Layers.Add(rl8);
ConvLayer conv4_2 = new ConvLayer(512, 3, 3, rl8.OutputDepth, rl8.OutputWidth, rl8.OutputHeight, 1, 1, 0, 1, 0.1f);
net.Layers.Add(conv4_2);
ReluLayer rl9 = new ReluLayer(conv4_2.OutputDepth, conv4_2.OutputWidth, conv4_2.OutputHeight);
net.Layers.Add(rl9);
ConvLayer conv4_3 = new ConvLayer(512, 3, 3, rl9.OutputDepth, rl9.OutputWidth, rl9.OutputHeight, 1, 1, 0, 1, 0.1f);
net.Layers.Add(conv4_3);
ReluLayer rl10 = new ReluLayer(conv4_3.OutputDepth, conv4_3.OutputWidth, conv4_3.OutputHeight);
net.Layers.Add(rl10);
MaxPoolLayer pl4 = new MaxPoolLayer(2, 2, rl10.OutputDepth, rl10.OutputWidth, rl10.OutputHeight, 2, 0);
net.Layers.Add(pl4);
ConvLayer conv5_1 = new ConvLayer(512, 3, 3, pl4.OutputDepth, pl4.OutputWidth, pl4.OutputHeight, 1, 1, 0, 1, 0.1f);
net.Layers.Add(conv5_1);
ReluLayer rl11 = new ReluLayer(conv5_1.OutputDepth, conv5_1.OutputWidth, conv5_1.OutputHeight);
net.Layers.Add(rl11);
ConvLayer conv5_2 = new ConvLayer(512, 3, 3, rl11.OutputDepth, rl11.OutputWidth, rl11.OutputHeight, 1, 1, 0, 1, 0.1f);
net.Layers.Add(conv5_2);
ReluLayer rl12 = new ReluLayer(conv5_2.OutputDepth, conv5_2.OutputWidth, conv5_2.OutputHeight);
net.Layers.Add(rl12);
ConvLayer conv5_3 = new ConvLayer(512, 3, 3, rl12.OutputDepth, rl12.OutputWidth, rl12.OutputHeight, 1, 1, 0, 1, 0.1f);
net.Layers.Add(conv5_3);
ReluLayer rl13 = new ReluLayer(conv5_3.OutputDepth, conv5_3.OutputWidth, conv5_3.OutputHeight);
net.Layers.Add(rl13);
MaxPoolLayer pl5 = new MaxPoolLayer(2, 2, rl13.OutputDepth, rl13.OutputWidth, rl13.OutputHeight, 2, 0);
net.Layers.Add(pl5);
FullyConnLayer fc = new FullyConnLayer(4096, pl5.OutputDepth,
pl5.OutputWidth, pl5.OutputHeight, 0, 1, 0);
net.Layers.Add(fc);
ReluLayer rl14 = new ReluLayer(fc.OutputDepth, fc.OutputWidth, fc.OutputHeight);
net.Layers.Add(rl14);
DropoutLayer d = new DropoutLayer(rl14.OutputDepth, rl14.OutputWidth, rl14.OutputHeight, 0.5f);
net.Layers.Add(d);
FullyConnLayer fc2 = new FullyConnLayer(4096, d.OutputDepth, d.OutputWidth, d.OutputHeight, 0, 1, 0);
net.Layers.Add(fc2);
ReluLayer rl15 = new ReluLayer(fc2.OutputDepth, fc2.OutputWidth, fc2.OutputHeight);
net.Layers.Add(rl15);
DropoutLayer d2 = new DropoutLayer(rl15.OutputDepth, rl15.OutputWidth, rl15.OutputHeight, 0.5f);
net.Layers.Add(d2);
FullyConnLayer fc3 = new FullyConnLayer(LabelsCount, d2.OutputDepth, d2.OutputWidth, d2.OutputHeight, 0, 1, 0);
net.Layers.Add(fc3);
SoftmaxLayer sl = new SoftmaxLayer(fc3.OutputDepth, fc3.OutputWidth, fc3.OutputHeight);
net.LossLayer = sl;
return(net);
}
