Keep estimating steps_without_progress, even if steps_since_last_learning_rate_shrink < iter_without_progress_thresh

dlib/dnn/trainer.h

@@ -697,10 +697,10 @@ namespace dlib
 
                     // Check if we should shrink the learning rate based on how the test
                     // error has been doing lately.
-                    if (learning_rate_shrink != 1 && steps_since_last_learning_rate_shrink > iter_without_progress_thresh)
+                    if (learning_rate_shrink != 1)
                     {
                         test_steps_without_progress = count_steps_without_decrease(test_previous_loss_values);
-                        if (test_steps_without_progress >= test_iter_without_progress_thresh)
+                        if (test_steps_without_progress >= test_iter_without_progress_thresh && steps_since_last_learning_rate_shrink >= test_iter_without_progress_thresh)
                         {
                             test_steps_without_progress = count_steps_without_decrease_robust(test_previous_loss_values);
                             if (test_steps_without_progress >= test_iter_without_progress_thresh)
@@ -809,13 +809,11 @@ namespace dlib
                 // have a "budget" that prevents us from calling
                 // count_steps_without_decrease() every iteration.  We do this because
                 // it can be expensive to compute when previous_loss_values is large.
-                if (gradient_check_budget > iter_without_progress_thresh
-                    && learning_rate_shrink != 1
-                    && steps_since_last_learning_rate_shrink > iter_without_progress_thresh)
+                if (gradient_check_budget > iter_without_progress_thresh && learning_rate_shrink != 1)
                 {
                     gradient_check_budget = 0;
                     steps_without_progress = count_steps_without_decrease(previous_loss_values);
-                    if (steps_without_progress >= iter_without_progress_thresh)
+                    if (steps_without_progress >= iter_without_progress_thresh && steps_since_last_learning_rate_shrink >= iter_without_progress_thresh)
                     {
                         // Double check that we aren't seeing decrease.  This second check
                         // discards the top 10% largest values and checks again.  We do