public DeconvolutionLayer(UpSamplingLayer prev_upsampling_l, ConvolutionLayer base_convolution_layer)
{
this.feature_maps_number = prev_upsampling_l.feature_maps_number;
this.feature_maps = new List <DeconvolutionFeatureMap>();
this.inputs = new List <float[, ]>();
this.avg_inputs = new List <float[, ]>();
this.proportion = prev_upsampling_l.feature_maps_number / base_convolution_layer.feature_maps_number;
this.kwidth = base_convolution_layer.kwidth;
this.kheight = base_convolution_layer.kheight;
//boundaries effect
this.map_width = kwidth + base_convolution_layer.map_width - 1;
this.map_height = kheight + base_convolution_layer.map_height - 1;
this.inp_w = prev_upsampling_l.outputwidth;
this.inp_h = prev_upsampling_l.outputwidth;
//create kernels
//previous layer must contain more feature maps or same number of feature maps
for (int k = 0; k < base_convolution_layer.feature_maps_number; k++)
{
avg_inputs.Add(new float[inp_w, inp_h]);
feature_maps.Add(new DeconvolutionFeatureMap(avg_inputs[k], base_convolution_layer.feature_maps[k]));
}
for (int k = 0; k < prev_upsampling_l.feature_maps_number; k++)
{
inputs.Add(prev_upsampling_l.feature_maps[k].output);
}
}
public static void save_output_to_file(string name, UpSamplingLayer layer)
{
for (int k = 0; k < layer.feature_maps_number; k++)
{
save_matrix_to_file(name + " feature map " + k.ToString() + " ", layer.feature_maps[k].output, layer.outputwidth, layer.outputheight);
}
}
//input is a picture's matrix
public Network(int input_w, int input_h, int FL_feature_maps_number,
int fl_kw, int fl_kh, int SL_feature_maps_number, int sl_kw, int sl_kh)
{
this.FL_feature_maps_number = FL_feature_maps_number;
this.SL_feature_maps_number = SL_feature_maps_number;
//create first convolutional layer(first layer)
FirstLayer = new ConvolutionLayer(FL_feature_maps_number, fl_kw, fl_kh, input_w, input_h);
//first subsampling layer(second layer)
SecondLayer = new SubsamplingLayer(FirstLayer);
//second convolutional layer(third layer).Consists of number of convolutional layers
ThirdLayer = new ConvolutionLayer(SL_feature_maps_number, sl_kw, sl_kh, SecondLayer.outputwidth, SecondLayer.outputheight);
//connect inputs with previous layer
//Parallel conctruction (like RGB) 2 cards of new layer for 1 card of previous layer
//like 6/3 = 2
//set topology for second layer
List <int> topology = new List <int>();
int counter = 0;
for (int i = 0; i < FL_feature_maps_number; i++)
{
for (int j = 0; j < SL_feature_maps_number / FL_feature_maps_number; j++)
{
topology.Add(counter);
counter++;
}
SecondLayer.set_link_with_conv_next_layer(ThirdLayer, i, topology);
topology.Clear();
}
//set topology for third layer
for (int i = 0; i < SL_feature_maps_number; i++)
{
int nextid = i * FL_feature_maps_number / SL_feature_maps_number;
ThirdLayer.feature_maps[i].add_input_full_connection(SecondLayer.feature_maps[nextid].output);
}
/*
* //all-to-all connection
* List<int> topology = new List<int>();
* for (int i = 0; i < FL_feature_maps_number; i++)
* {
* for (int j = 0; j < SL_feature_maps_number; j++)
* {
* topology.Add(j);
* }
* SecondLayer.set_link_with_conv_next_layer(ThirdLayer, i, topology);
* topology.Clear();
* }
*
* //set topology for third layer
* for (int j = 0; j < ThirdLayer.feature_maps_number; j++)
* {
* for (int i = 0; i < SecondLayer.feature_maps_number; i++)
* {
* ThirdLayer.feature_maps[j].add_input_full_connection(SecondLayer.feature_maps[i].output);
* }
* }
*/
FourthLayer = new SubsamplingLayer(ThirdLayer);
FifthLayer = new UpSamplingLayer(FourthLayer);
SixthLayer = new DeconvolutionLayer(FifthLayer, ThirdLayer);
SeventhLayer = new UpSamplingLayer(SixthLayer, SecondLayer.feature_maps_number);
EightsLayer = new DeconvolutionLayer(SeventhLayer, FirstLayer);
}
