Fixed spelling error in method name. Also optimized and cleaned up the

automatic step size reduction code a little.

dlib/dnn/trainer.h

@@ -328,12 +328,14 @@ namespace dlib
             rs.clear();
         }
 
-        void set_setep_size (
+        void set_step_size (
             double ss
         )
         {
             DLIB_CASSERT(ss > 0,"");
             wait_for_thread_to_pause();
+            if (step_size != ss)
+                previous_loss_values.clear();
             step_size = ss;
         }
 
@@ -391,24 +393,33 @@ namespace dlib
             resizable_tensor t;
         };
 
-        template <typename T>
-        void run_update(job_t& next_job, const T&)
+        void record_loss(double loss)
         {
-            double loss = net.update(next_job.t, next_job.labels.begin(), make_sstack(solvers),step_size);
+            // Say that we will check if the gradient is bad 200 times during each
+            // iter_between_step_size_adjust interval of network updates.   This kind of
+            // budgeting causes our gradient checking to use a fixed amount of
+            // computational resources, regardless of the size of
+            // iter_between_step_size_adjust.
+            gradient_check_budget += 200;
+
             rs.add(loss);
             previous_loss_values.push_back(loss);
             if (previous_loss_values.size() > iter_between_step_size_adjust)
                 previous_loss_values.pop_front();
         }
 
+        template <typename T>
+        void run_update(job_t& next_job, const T&)
+        {
+            double loss = net.update(next_job.t, next_job.labels.begin(), make_sstack(solvers),step_size);
+            record_loss(loss);
+        }
+
         void run_update(job_t& next_job, const no_label_type&)
         {
             no_label_type pick_wich_run_update;
             double loss = net.update(next_job.t, make_sstack(solvers), step_size);
-            rs.add(loss);
-            previous_loss_values.push_back(loss);
-            if (previous_loss_values.size() > iter_between_step_size_adjust)
-                previous_loss_values.pop_front();
+            record_loss(loss);
         }
 
         void thread() try
@@ -425,9 +436,14 @@ namespace dlib
                 run_update(next_job, pick_wich_run_update);
 
                 // If we have been running for a while then check if the loss is still
-                // dropping.  If it isn't then we will reduce the step size.
-                if (previous_loss_values.size() >= iter_between_step_size_adjust)
-                {
+                // dropping.  If it isn't then we will reduce the step size.  Note that we
+                // have a "budget" that prevents us from calling
+                // probability_gradient_greater_than() every iteration.  We do this because
+                // it can be expensive to compute when previous_loss_values is large.
+                if (previous_loss_values.size() >= iter_between_step_size_adjust && 
+                    gradient_check_budget > previous_loss_values.size())
+                {
+                    gradient_check_budget = 0;
                     if (probability_gradient_greater_than(previous_loss_values, 0) > 0.49)
                     {
                         step_size = step_size_shrink*step_size;
@@ -458,12 +474,13 @@ namespace dlib
             verbose = false;
             cuda_device_id = dlib::cuda::get_device();
             step_size = 1;
-            min_step_size = 1e-4;
+            min_step_size = 1e-3;
             iter_between_step_size_adjust = 2000;
             step_size_shrink = 0.1;
             epoch_iteration = 0;
             epoch_pos = 0;
             train_one_step_calls = 0;
+            gradient_check_budget = 0;
             start();
         }
 
@@ -575,7 +592,7 @@ namespace dlib
         std::vector<solver_type> solvers;
         std::atomic<double> step_size;
         double min_step_size;
-        std::atomic<long> iter_between_step_size_adjust;
+        std::atomic<unsigned long> iter_between_step_size_adjust;
         std::atomic<double> step_size_shrink;
         std::chrono::time_point<std::chrono::system_clock> last_sync_time;
         std::string sync_filename;
@@ -584,6 +601,7 @@ namespace dlib
         unsigned long epoch_pos;
         std::chrono::time_point<std::chrono::system_clock> last_time;
         unsigned long long train_one_step_calls;
+        unsigned long gradient_check_budget;
 
         // The job object is not logically part of the state of this object. It is here
         // only to avoid reallocating it over and over.

dlib/dnn/trainer_abstract.h

@@ -60,7 +60,7 @@ namespace dlib
                 - #get_max_num_epochs() == 10000
                 - #get_mini_batch_size() == 128
                 - #get_step_size() == 1
-                - #get_min_step_size() == 1e-4
+                - #get_min_step_size() == 1e-3
                 - #get_iterations_between_step_size_adjust() == 2000
                 - #get_step_size_shrink() == 0.1
         !*/
@@ -149,7 +149,7 @@ namespace dlib
                 - #get_max_num_epochs() == num
         !*/
 
-        void set_setep_size (
+        void set_step_size (
             double ss
         );
         /*!