public MeanPoolLayerBackprop(MeanPoolLayer unit)
{
_unit = unit;
_outgoing = new Matrix <float> [unit.Prev.ChannelCount];
for (int i = 0; i < _outgoing.Length; i++)
{
_outgoing[i] = Matrix <float> .Build.Dense(unit.Prev.SideLength, unit.Prev.SideLength);
}
_ones = Matrix <float> .Build.Dense(unit.PoolSize, unit.PoolSize, 1f);
}
public ConvolutionalNetwork(int matsize, int vecsize, int depth, int labels, params CNNArgs[] args)
{
_matsize = matsize;
_vecsize = vecsize;
_depth = depth;
_labels = labels;
_args = args;
InputLayer = new SpatialLayer(matsize, depth);
ConvolutionalLayers = new ConvolutionalLayer[args.Length];
SubSampleLayers = new MeanPoolLayer[args.Length];
ConvolutionalLayers[0] = new ConvolutionalLayer(args[0].FilterSize, args[0].FilterCount, args[0].Stride, InputLayer, Functions.Rectifier2D);
SubSampleLayers[0] = new MeanPoolLayer(args[0].PoolLayerSize, ConvolutionalLayers[0]);
for (int i = 1; i < args.Length; i++)
{
ConvolutionalLayers[i] = new ConvolutionalLayer(args[i].FilterSize, args[i].FilterCount, args[i].Stride, SubSampleLayers[i - 1], Functions.Rectifier2D);
SubSampleLayers[i] = new MeanPoolLayer(args[i].PoolLayerSize, ConvolutionalLayers[i]);
}
FlattenLayer = new FlattenLayer(SubSampleLayers[SubSampleLayers.Length - 1]);
VectorInput = new InputLayer(vecsize);
LinearHiddenLayer = new DenseLayer(vecsize, VectorInput, Functions.Sigmoid);
CombinationLayer = new TreeLayer(FlattenLayer.Size(), vecsize);
OutputLayer = new DenseLayer(labels, CombinationLayer, Functions.Identity);
}
