private void backward_convolute_on_depth(UnitLayer to, int stride, int depth, int length)
{
int to_depth = depth + length;
if (to_depth >= Depth)
{
to_depth = Depth;
}
for (int d = depth; d < to_depth; d++)
{
WeightLayer weights = WeightLayerPool.find(to.Id, Id, d);
int width_of_weights = weights.Width, height_of_weights = weights.Height;
for (int y = 0; y < Height; y++)
{
int top = y * stride;
int bottom = top + height_of_weights;
for (int x = 0; x < Width; x++)
{
int left = x * stride;
to.diff_convolute_product(left, top, left + width_of_weights, bottom, Units[x, y, d].gradient_at_inport, weights);
}
}
weights.diff_weights();
}
}
private void forward_convolute_on_depth(UnitLayer to, int stride, int depth, int length)
{
int to_depth = depth + length;
if (to_depth >= to.Depth)
{
to_depth = to.Depth;
}
for (int d = depth; d < to_depth; d++)
{
WeightLayer weights = WeightLayerPool.find(Id, to.Id, d);
int width_of_weights = weights.Width, height_of_weights = weights.Height;
int width_of_units = to.Width, height_of_units = to.Height;
for (int y = 0; y < height_of_units; y++)
{
int top = y * stride;
int bottom = top + height_of_weights;
for (int x = 0; x < width_of_units; x++)
{
int left = x * stride;
to.set_value_at_inport(x, y, d, weights.convolute_product(left, top, left + width_of_weights, bottom, Units));
}
}
}
}
static public void create_convolution(UnitLayer from, UnitLayer to, int width, int height)
{
for (int d = 0; d < to.Depth; d++)
{
string id = string.Format("C/{0}/{1}/{2}", from.Id, to.Id, d);
WeightLayer layer = new WeightLayer(id, width, height, from.Depth);
layer.fill_weights(from.Width * from.Height * from.Depth);
WeightLayers.Add(id, layer);
}
}
static public void create_fully_connection(UnitLayer from, UnitLayer to)
{
for (int d = 0; d < to.Depth; d++)
{
for (int y = 0; y < to.Height; y++)
{
for (int x = 0; x < to.Width; x++)
{
string id = string.Format("F/{0}/{1}/{2}/{3}/{4}", from.Id, to.Id, x, y, d);
WeightLayer layer = new WeightLayer(id, from.Width, from.Height, from.Depth);
layer.fill_weights(from.Width * from.Height * from.Depth);
WeightLayers.Add(id, layer);
}
}
}
}
static public void load(BinaryReader reader)
{
int count = reader.ReadInt32();
for (int i = 0; i < count; i++)
{
string id = reader.ReadString();
int depth = reader.ReadInt32();
int width = reader.ReadInt32();
int height = reader.ReadInt32();
WeightLayer layer = new WeightLayer(id, width, height, depth);
WeightLayers.Add(id, layer);
layer.load(reader);
}
}
private void backward_fully_connect_on_depth(UnitLayer to, int depth, int length)
{
int to_depth = depth + length;
if (to_depth >= Depth)
{
to_depth = Depth;
}
for (int d = depth; d < to_depth; d++)
{
for (int y = 0; y < Height; y++)
{
for (int x = 0; x < Width; x++)
{
WeightLayer weights = WeightLayerPool.find(to.Id, Id, x, y, d);
to.diff_fully_product(Units[x, y, d].gradient_at_inport, weights);
weights.diff_weights();
}
}
}
}
private void forward_fully_connect_on_depth(UnitLayer to, int depth, int length)
{
int width = to.Width, height = to.Height;
int to_depth = depth + length;
if (to_depth >= to.Depth)
{
to_depth = to.Depth;
}
for (int d = depth; d < to_depth; d++)
{
for (int y = 0; y < height; y++)
{
for (int x = 0; x < width; x++)
{
WeightLayer weights = WeightLayerPool.find(Id, to.Id, x, y, d);
to.set_value_at_inport(x, y, d, weights.fully_product(Units));
}
}
}
}
public void diff_convolute_product(int left, int top, int right, int bottom, double gradient, WeightLayer weights)
{
weights.diff_convolute_product(left, top, right, bottom, gradient, Units);
}
public void diff_fully_product(double gradient, WeightLayer weights)
{
weights.diff_fully_product(gradient, Units);
}
