Made test_layer() a little more robust.

dlib/dnn/core.h

@@ -1896,6 +1896,8 @@ namespace dlib
         using namespace timpl;
         // Do some setup
         dlib::rand rnd;
+        for (int iter = 0; iter < 5; ++iter)
+        {
             test_layer_subnet subnetwork(rnd);
             resizable_tensor output, out2, out3;
             // Run setup() and forward() as well to make sure any calls to subnet() have
@@ -2017,7 +2019,8 @@ namespace dlib
                 double reference_derivative = (dot(out2,input_grad)-dot(out3, input_grad))/(2*eps);
                 double output_derivative = params_grad.host()[i];
                 double relative_error = (reference_derivative - output_derivative)/(reference_derivative + 1e-100);
-            if (std::abs(relative_error) > 0.01)
+                double absolute_error = (reference_derivative - output_derivative);
+                if (std::abs(relative_error) > 0.02 && std::abs(absolute_error) > 0.001)
                 {
                     using namespace std;
                     sout << "Gradient error in parameter #" << i <<".  Relative error: "<< relative_error << endl;
@@ -2049,7 +2052,8 @@ namespace dlib
                 if (!impl::is_inplace_layer(l,subnetwork))
                     output_derivative -= initial_gradient_input[i];
                 double relative_error = (reference_derivative - output_derivative)/(reference_derivative + 1e-100);
-            if (std::abs(relative_error) > 0.01)
+                double absolute_error = (reference_derivative - output_derivative);
+                if (std::abs(relative_error) > 0.02 && std::abs(absolute_error) > 0.001)
                 {
                     using namespace std;
                     sout << "Gradient error in data variable #" << i <<".  Relative error: "<< relative_error << endl;
@@ -2059,6 +2063,8 @@ namespace dlib
                 }
             }
 
+        } // end for (int iter = 0; iter < 5; ++iter)
+
         return layer_test_results();
     }