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Made add_prev output a tensor with dimensions that are the max of each of the 

dimensions of its inputs rather than always outputting a tensor that has the
dimensions of its immediate predecessors.
This commit is contained in:
Davis King 2016-05-25 19:12:36 -04:00
parent b9332698fe
commit 911638638d
2 changed files with 14 additions and 3 deletions
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9
dlib/dnn/layers.h
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@ -1447,8 +1447,13 @@ namespace dlib
template <typename SUBNET>
void forward(const SUBNET& sub, resizable_tensor& output)
{
output.copy_size(sub.get_output());
tt::add(output, sub.get_output(), layer<tag>(sub).get_output());
auto&& t1 = sub.get_output();
auto&& t2 = layer<tag>(sub).get_output();
output.set_size(std::max(t1.num_samples(),t2.num_samples()),
std::max(t1.k(),t2.k()),
std::max(t1.nr(),t2.nr()),
std::max(t1.nc(),t2.nc()));
tt::add(output, t1, t2);
}
template <typename SUBNET>

8
dlib/dnn/layers_abstract.h
View File

@ -1600,7 +1600,13 @@ namespace dlib
what layer to add to the output of the previous layer. The result of this
addition is output by add_prev_. Finally, the addition happens pointwise
according to 4D tensor arithmetic. If the dimensions don't match then
missing elements are presumed to be equal to 0.
missing elements are presumed to be equal to 0. Moreover, each dimension
of the output tensor is equal to the maximum dimension of either of the
inputs. That is, if the tensors A and B are being added to produce C then:
- C.num_samples() == max(A.num_samples(), B.num_samples())
- C.k() == max(A.k(), B.k())
- C.nr() == max(A.nr(), B.nr())
- C.nc() == max(A.nc(), B.nc())
!*/
public: