public override void Backward(SuperArray outputgrad)
{
int pad = 0;
if (Padding == PaddingType.Same)
{
pad = 1;
}
else if (Padding == PaddingType.Full)
{
pad = 2;
}
var dout_flat = outputgrad.Transpose(2, 0, 1).Reshape(Filters, -1);
var dW = Ops.Dot(dout_flat, xCols.Transpose());
dW = dW.Reshape(Params["w"].Data.Shape);
var db = Ops.Sum(outputgrad, new uint[] { 0, 1, 2 }).Reshape(Filters, -1);
var W_flat = Params["w"].Data.Reshape(Filters, -1);
var dX_col = Ops.Dot(W_flat.Transpose(), dout_flat);
Input.Grad = ImUtil.Col2Im(dX_col, Input.Data.Shape.Dims, Tuple.Create(KernalSize, KernalSize), pad, Strides);
Params["w"].Grad = dW;
if (UseBias)
{
Params["b"].Grad = db;
}
}
public override void Backward(SuperArray outputgrad)
{
SuperArray dX_col = Ops.Constant(0, xCols.Shape);
var(n, c, h, w) = Input.Data.GetConv2DShape();
int pad = 0;
if (Padding == PaddingType.Same)
{
pad = 1;
}
else if (Padding == PaddingType.Full)
{
pad = 2;
}
var dout_flat = outputgrad.Transpose(2, 3, 0, 1);
var dX = ImUtil.Col2Im(dout_flat, Input.Data.Shape.Dims, PoolSize, pad, Strides);
Input.Grad = dX.Reshape(n, c, h, w);
}
