Change the default learnRateAdjustInterval to 1 instead of 2.

MachineLearning/cn/SGD.h

@@ -255,7 +255,7 @@ namespace Microsoft { namespace MSR { namespace CNTK {
             m_learnRateIncreaseFactor=learnRateIncreaseFactor;
             m_reduceLearnRateIfImproveLessThan=reduceLearnRateIfImproveLessThan;
              m_continueReduce=continueReduce;
-             m_learnRateAdjustInterval = max((size_t) 2, learnRateAdjustInterval); //minimum interval is 1 epoch
+             m_learnRateAdjustInterval = max((size_t) 1, learnRateAdjustInterval); //minimum interval is 1 epoch
             m_learnRateDecreaseFactor=learnRateDecreaseFactor;
             m_clippingThresholdPerSample=abs(clippingThresholdPerSample);
             m_numMiniBatch4LRSearch=numMiniBatch4LRSearch;
@@ -1441,12 +1441,11 @@ protected:
                 icol = max(0, icol);
 
                 fprintf(stderr, "\n###### d%ls######\n", node->NodeName().c_str());
-                // node->FunctionValues().Print();
+                //node->FunctionValues().Print();
                 ElemType eOrg = node->FunctionValues()(irow,icol);
 
                 node->UpdateEvalTimeStamp();
                 net.ComputeGradient(criterionNodes[npos]);  //use only the first criterion. Is 
-                //ElemType mbEvalCri =
                 criterionNodes[npos]->FunctionValues().Get00Element(); //criterionNode should be a scalar
                 ElemType eGradErr = node->GradientValues()(irow, icol); 
 
@@ -1473,7 +1472,7 @@ protected:
                 bool wrong = (std::isnan(diff) || diff > threshold);
                 if (wrong)
                 {
-                    fprintf (stderr, "\nd%ls Numeric gradient = %e, Error BP gradient = %e\n", node->NodeName().c_str(), eGradNum, eGradErr);
+                    fprintf (stderr, "\nd%ls Numeric gradient = %e, Error BP gradient = %e \n", node->NodeName().c_str(), eGradNum, eGradErr);
                     return false; 
                 }
             }