make update_parameters() a little more uniform

dlib/dnn/core.h

@@ -1003,7 +1003,7 @@ namespace dlib
         template <typename solver_type>
         void update_parameters(std::vector<solver_type>& solvers, double learning_rate)
         {
-            subnetwork->update_parameters(make_sstack(solvers), learning_rate);
+            update_parameters(make_sstack(solvers), learning_rate);
         }
 
         const tensor& get_parameter_gradient(
@@ -1369,6 +1369,12 @@ namespace dlib
             }
         }
 
+        template <typename solver_type>
+        void update_parameters(std::vector<solver_type>& solvers, double learning_rate)
+        {
+            update_parameters(make_sstack(solvers), learning_rate);
+        }
+
         const tensor& get_parameter_gradient(
         ) const { return params_grad; }
 
@@ -1609,6 +1615,12 @@ namespace dlib
             subnetwork.update_parameters(solvers, learning_rate);
         }
 
+        template <typename solver_type>
+        void update_parameters(std::vector<solver_type>& solvers, double learning_rate)
+        {
+            update_parameters(make_sstack(solvers), learning_rate);
+        }
+
         const tensor& get_parameter_gradient(
         ) const { return params_grad; }
 
@@ -1905,6 +1917,12 @@ namespace dlib
             subnetwork.update_parameters(solvers.pop(comp_layers_in_each_group*details.size()),learning_rate);
         }
 
+        template <typename solver_type>
+        void update_parameters(std::vector<solver_type>& solvers, double learning_rate)
+        {
+            update_parameters(make_sstack(solvers), learning_rate);
+        }
+
         const subnet_type& subnet() const { return subnetwork; }
         subnet_type& subnet() { return subnetwork; }
 
@@ -2135,6 +2153,12 @@ namespace dlib
             // nothing to do
         }
 
+        template <typename solver_type>
+        void update_parameters(std::vector<solver_type>& solvers, double learning_rate)
+        {
+            update_parameters(make_sstack(solvers), learning_rate);
+        }
+
         const subnet_type& subnet() const { return input_layer; }
         subnet_type& subnet() { return input_layer; }
 
@@ -2550,6 +2574,12 @@ namespace dlib
             subnetwork.update_parameters(solvers, learning_rate);
         }
 
+        template <typename solver_type>
+        void update_parameters(std::vector<solver_type>& solvers, double learning_rate)
+        {
+            update_parameters(make_sstack(solvers), learning_rate);
+        }
+
         const subnet_type& subnet() const { return subnetwork; }
         subnet_type& subnet() { return subnetwork; }
         const loss_details_type& loss_details() const { return loss; }
@@ -2940,6 +2970,12 @@ namespace dlib
             subnetwork.update_parameters(solvers, learning_rate);
         }
 
+        template <typename solver_type>
+        void update_parameters(std::vector<solver_type>& solvers, double learning_rate)
+        {
+            update_parameters(make_sstack(solvers), learning_rate);
+        }
+
         const tensor& get_parameter_gradient(
         ) const { return params_grad; }
 

dlib/dnn/core_abstract.h

@@ -639,6 +639,13 @@ namespace dlib
                 - The solvers use the given learning rate.
         !*/
 
+        template <typename solver_type>
+        void update_parameters(std::vector<solver_type>& solvers, double learning_rate)
+        { update_parameters(make_sstack(solvers), learning_rate); }
+        /*!
+            Convenience method for calling update_parameters()
+        !*/
+
         void clean(
         );
         /*!
@@ -1155,6 +1162,13 @@ namespace dlib
                 - The solvers use the given learning rate.
         !*/
 
+        template <typename solver_type>
+        void update_parameters(std::vector<solver_type>& solvers, double learning_rate)
+        { update_parameters(make_sstack(solvers), learning_rate); }
+        /*!
+            Convenience method for calling update_parameters()
+        !*/
+
     // -------------
 
         void clean (