diff --git a/Source/ComputationNetworkLib/LinearAlgebraNodes.h b/Source/ComputationNetworkLib/LinearAlgebraNodes.h
index 573cca8cd..13c16d65b 100644
--- a/Source/ComputationNetworkLib/LinearAlgebraNodes.h
+++ b/Source/ComputationNetworkLib/LinearAlgebraNodes.h
@@ -143,7 +143,7 @@ public:
     virtual void /*ComputationNode::*/ ForwardProp(const FrameRange& fr) override
     {
         size_t rank = DetermineElementwiseTensorRank();
-        auto result =           ValueTensorFor(rank, fr);
+        auto result =             ValueTensorFor(rank, fr);
         auto input0 = InputRef(0).ValueTensorFor(rank, fr.AllowBroadcast());
         auto input1 = InputRef(1).ValueTensorFor(rank, fr.AllowBroadcast());
         result.AssignDifferenceOf(input0, input1);
@@ -193,7 +193,7 @@ public:
     virtual void /*ComputationNode::*/ ForwardProp(const FrameRange& fr) override
     {
         size_t rank = DetermineElementwiseTensorRank();
-        auto result =           ValueTensorFor(rank, fr);
+        auto result =             ValueTensorFor(rank, fr);
         auto input0 = InputRef(0).ValueTensorFor(rank, fr.AllowBroadcast());
         auto input1 = InputRef(1).ValueTensorFor(rank, fr.AllowBroadcast());
         result.AssignElementwiseProductOf(input0, input1);
@@ -203,7 +203,7 @@ public:
     {
         size_t rank = DetermineElementwiseTensorRank();
         auto gradient        =                     GradientTensorFor(rank, fr);
-        auto inputGradient   =  Input(inputIndex)->GradientTensorFor(rank, fr.AllowBroadcast());
+        auto inputGradient   =     Input(inputIndex)->GradientTensorFor(rank, fr.AllowBroadcast());
         auto otherInputValue = Input(1 - inputIndex)->ValueTensorFor(rank, fr.AllowBroadcast());
 
         // if reduction then mask the respective input(s) (zero out the gaps)
@@ -689,7 +689,7 @@ public:
     virtual void /*ComputationNode::*/ ForwardProp(const FrameRange& fr) override
     {
         size_t rank = DetermineElementwiseTensorRank();
-        auto output =                                ValueTensorFor(                         rank, fr);
+        auto output =                                  ValueTensorFor(                         rank, fr);
         auto input  = TensorView<ElemType>(InputRef(0).ValuePtr(), GetTransposedTensorSliceFor(rank, fr));
         output.AssignCopyOf(input);
     }
@@ -697,7 +697,7 @@ public:
     virtual void /*ComputationNode::*/ BackpropTo(const size_t inputIndex, const FrameRange& fr) override
     {
         size_t rank = DetermineElementwiseTensorRank();
-        auto outputGradient =                                GradientTensorFor(                         rank, fr);
+        auto outputGradient =                                  GradientTensorFor(                         rank, fr);
         auto inputGradient  = TensorView<ElemType>(InputRef(0).GradientPtr(), GetTransposedTensorSliceFor(rank, fr));
         inputGradient.AddCopyOf(outputGradient);
     }
diff --git a/Source/ComputationNetworkLib/NonlinearityNodes.h b/Source/ComputationNetworkLib/NonlinearityNodes.h
index 213b65416..e0f960fbc 100644
--- a/Source/ComputationNetworkLib/NonlinearityNodes.h
+++ b/Source/ComputationNetworkLib/NonlinearityNodes.h
@@ -50,7 +50,7 @@ public:
     virtual void /*ComputationNode::*/ ForwardProp(const FrameRange& fr) override
     {
         size_t rank = DetermineElementwiseTensorRank();
-        auto result =           ValueTensorFor(rank, fr);
+        auto result =             ValueTensorFor(rank, fr);
         auto input  = InputRef(0).ValueTensorFor(rank, fr);
         result.DoUnaryOpOf(0, input, 1, opForward, opSum);
     }
@@ -61,7 +61,7 @@ public:
 
         // get the args
         size_t rank = DetermineElementwiseTensorRank();
-        auto sliceOutputGrad =           GradientTensorFor(rank, fr); // propagate from this one...
+        auto sliceOutputGrad =             GradientTensorFor(rank, fr); // propagate from this one...
         auto sliceInputGrad  = InputRef(0).GradientTensorFor(rank, fr); // ...to this one
 
         GradientOperationType opTypeHolder = opType;  // preventing pragma warning C4127
@@ -544,10 +544,10 @@ public:
         if (inputIndex == 2)
         {
             size_t rank = DetermineElementwiseTensorRank();
-            auto gradient =                         GradientTensorFor(rank, fr);
+            auto gradient =                           GradientTensorFor(rank, fr);
             auto inputGradient = InputRef(inputIndex).GradientTensorFor(rank, fr.AllowBroadcast());
             auto input =         InputRef(inputIndex).ValueTensorFor(rank, fr.AllowBroadcast());
-            auto output =                           ValueTensorFor(rank, fr.AllowBroadcast());
+            auto output =                             ValueTensorFor(rank, fr.AllowBroadcast());
 
             inputGradient.AddCopyIfEqualOf(input, output, gradient);
         }        
diff --git a/Source/ComputationNetworkLib/SpecialPurposeNodes.cpp b/Source/ComputationNetworkLib/SpecialPurposeNodes.cpp
index 55dd5c176..1b315ec97 100644
--- a/Source/ComputationNetworkLib/SpecialPurposeNodes.cpp
+++ b/Source/ComputationNetworkLib/SpecialPurposeNodes.cpp
@@ -126,7 +126,7 @@ template <class ElemType>
         fprintf(stderr, "] %ls %s--> %s\n", m_message.c_str(), logGradientInstead ? "(gradient) " : "", InputRef(0).FormatOperationPrototype("").c_str());
         InputRef(0).WriteMinibatchWithFormatting(stderr, fr, m_onlyUpToRow, m_onlyUpToT, m_formattingOptions.transpose, m_formattingOptions.isCategoryLabel, m_formattingOptions.isSparse, m_labelMapping,
                                                sequenceSeparator, sequencePrologue, sequenceEpilogue, elementSeparator, sampleSeparator,
-											   valueFormatString, logGradientInstead);
+                                               valueFormatString, logGradientInstead);
     }
 }