diff --git a/GPy/inference/SCG.py b/GPy/inference/SCG.py
index 70b11940..d76fcf9b 100644
--- a/GPy/inference/SCG.py
+++ b/GPy/inference/SCG.py
@@ -49,6 +49,7 @@ def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=500, display=True, xto
     function_eval = 1
     fnow = fold
     gradnew = gradf(x, *optargs)  # Initial gradient.
+    current_grad = np.dot(gradnew, gradnew)
     gradold = gradnew.copy()
     d = -gradnew  # Initial search direction.
     success = True  # Force calculation of directional derivs.
@@ -110,7 +111,7 @@ def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=500, display=True, xto
         iteration += 1
         if display:
             print '\r',
-            print 'i: {0:>5g}  f:{1:> 12e}  b:{2:> 12e} |g|:{3:> 12e}'.format(iteration, fnow, beta, np.dot(gradnew, gradnew)),
+            print 'i: {0:>5g}  f:{1:> 12e}  b:{2:> 12e} |g|:{3:> 12e}'.format(iteration, fnow, beta, current_grad),
             # print 'Iteration:', iteration, ' Objective:', fnow, '  Scale:', beta, '\r',
             sys.stdout.flush()
 
@@ -125,8 +126,9 @@ def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=500, display=True, xto
                 fold = fnew
                 gradold = gradnew
                 gradnew = gradf(x, *optargs)
+                current_grad = np.dot(gradnew, gradnew)
                 # If the gradient is zero then we are done.
-                if (np.dot(gradnew, gradnew)) <= gtol:
+                if current_grad <= gtol:
                     status = 'converged'
                     return x, flog, function_eval, status