void forward_propagation(Eigen::MatrixBase<Derived1> const &input,
Eigen::MatrixBase<Derived2> const &weight,
Eigen::MatrixBase<Derived3> const &bias,
Eigen::MatrixBase<Derived4> &output,
bool no_overlap = true,
UnaryFunc func = UnaryFunc())
{
static_assert(std::is_same<Derived1::Scalar, Derived2::Scalar>::value &&
std::is_same<Derived2::Scalar, Derived3::Scalar>::value &&
std::is_same<Derived4::Scalar, Derived4::Scalar>::value,
"Data type of matrix input, weight, bias and output should be the same");
if(input.rows() != 0 && weight.rows() != 0 &&
bias.rows() != 0){
if(no_overlap){
output.noalias() = weight * input;
}else{
output = weight * input;
}
using Scalar = typename Derived3::Scalar;
using MatType = Eigen::Matrix<Scalar, Eigen::Dynamic, 1>;
using Mapper = Eigen::Map<const MatType, Eigen::Aligned>;
Mapper Map(&bias(0, 0), bias.size());
output.colwise() += Map;
func(output);
}
}
inline typename iterators::enable_if<
is_class<UnaryFunc> // We should probably find a cheaper test than is_class<>
, transform_iterator<UnaryFunc, Iterator>
>::type
make_transform_iterator(Iterator it)
{
return transform_iterator<UnaryFunc, Iterator>(it, UnaryFunc());
}
typename mpl::if_<
#else
typename iterators::enable_if<
#endif
is_class<UnaryFunc> // We should probably find a cheaper test than is_class<>
, transform_iterator<UnaryFunc, Iterator>
#if POTHOS_WORKAROUND(POTHOS_MSVC, <= 1300)
, int[3]
#endif
>::type
make_transform_iterator(Iterator it)
{
return transform_iterator<UnaryFunc, Iterator>(it, UnaryFunc());
}
void forward_propagation(cv::Mat const &input,
cv::Mat const &weight,
cv::Mat const &bias,
cv::Mat &output,
UnaryFunc func = UnaryFunc())
{
if(!input.empty() && !weight.empty() &&
!bias.empty()){
//output = weight * input;
cv::gemm(weight, input, 1.0, cv::Mat(),
0.0, output);
for(int i = 0; i != output.cols; ++i){
output.col(i) += bias;
}
func(output);
}
}
