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.

dlib/dnn/layers.h

@@ -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>

dlib/dnn/layers_abstract.h

@@ -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: