public void send_front_signal(float[,] input)
{
change_input(input);
// connect_input(input);
send_signal_front();
MatrixOperations.save_output_to_file("First layer", FirstLayer);
MatrixOperations.save_output_to_file("Second layer", SecondLayer);
MatrixOperations.save_output_to_file("Third layer", ThirdLayer);
MatrixOperations.save_output_to_file("Fourth layer", FourthLayer);
MatrixOperations.save_output_to_file("Fifth layer", FifthLayer);
MatrixOperations.save_output_to_file("Sixth layer", SixthLayer);
MatrixOperations.save_output_to_file("Seventh layer", SeventhLayer);
MatrixOperations.save_output_to_file("Eight layer", EightsLayer);
}
public ConvolutionLayer(ConvolutionLayer prev_convolution, int k_w, int k_h)
{
//default value
this.next_l_type = "convolution";
this.feature_maps_number = prev_convolution.feature_maps_number;
this.bj = new float[this.feature_maps_number];
//bj=1 initially
for (int j = 0; j < this.feature_maps_number; j++)
{
bj[j] = 0.01f;
}
this.feature_maps = new List <ConvolutionFeatureMap>();
this.errors = new List <float[, ]>();
this.kwidth = k_w;
this.kheight = k_h;
//if filter<map =>"valid" bounadary mode, else - full-mode
this.map_width = prev_convolution.map_width;
this.map_height = prev_convolution.map_height;
if (this.map_width > kwidth)
{
this.map_width = -(kwidth - 1);
}
if (this.map_height > kheight)
{
this.map_height -= (kheight - 1);
}
//create kernels
for (int k = 0; k < feature_maps_number; k++)
{
ConvolutionFeatureMap fm = new ConvolutionFeatureMap(kwidth, kheight, map_width, map_height);
MatrixOperations.init_matrix_random(fm.weights, kwidth, kheight);
feature_maps.Add(fm);
errors.Add(new float[map_width, map_height]);
}
//link with previous layer
//all-to-all connection
for (int i = 0; i < this.feature_maps_number; i++)
{ //link prev layers output
this.feature_maps[i].add_input_full_connection(prev_convolution.feature_maps[i].output);
//link this error input path
}
}
public ConvolutionLayer(int f_maps_number, int k_w, int k_h, int map_w, int map_h)
{
//default value
this.next_l_type = "subsampling";
this.feature_maps_number = f_maps_number;
this.bj = new float[f_maps_number];
//bj=1 initially
for (int j = 0; j < f_maps_number; j++)
{
bj[j] = 0.01f;
}
this.feature_maps = new List <ConvolutionFeatureMap>();
this.errors = new List <float[, ]>();
this.kwidth = k_w;
this.kheight = k_h;
//if filter<map =>"valid" bounadary mode, else - full-mode
if (map_w > kwidth)
{
this.map_width = map_w - kwidth + 1;
}
if (map_h > kheight)
{
this.map_height = map_h - kheight + 1;
}
//create kernels
for (int k = 0; k < feature_maps_number; k++)
{
ConvolutionFeatureMap fm = new ConvolutionFeatureMap(kwidth, kheight, map_width, map_height);
MatrixOperations.init_matrix_random(fm.weights, kwidth, kheight);
feature_maps.Add(fm);
errors.Add(new float[map_width, map_height]);
}
}
