diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorConcatenation.h b/unsupported/Eigen/CXX11/src/Tensor/TensorConcatenation.h
index 78214df11..c3d6357fa 100644
--- a/unsupported/Eigen/CXX11/src/Tensor/TensorConcatenation.h
+++ b/unsupported/Eigen/CXX11/src/Tensor/TensorConcatenation.h
@@ -135,19 +135,21 @@ struct TensorEvaluator<const TensorConcatenationOp<Axis, LeftArgType, RightArgTy
     eigen_assert(0 <= m_axis && m_axis < NumDims);
     const Dimensions& lhs_dims = m_leftImpl.dimensions();
     const Dimensions& rhs_dims = m_rightImpl.dimensions();
-    int i = 0;
-    for (; i < m_axis; ++i) {
-      eigen_assert(lhs_dims[i] > 0);
-      eigen_assert(lhs_dims[i] == rhs_dims[i]);
-      m_dimensions[i] = lhs_dims[i];
-    }
-    eigen_assert(lhs_dims[i] > 0);  // Now i == m_axis.
-    eigen_assert(rhs_dims[i] > 0);
-    m_dimensions[i] = lhs_dims[i] + rhs_dims[i];
-    for (++i; i < NumDims; ++i) {
-      eigen_assert(lhs_dims[i] > 0);
-      eigen_assert(lhs_dims[i] == rhs_dims[i]);
-      m_dimensions[i] = lhs_dims[i];
+    {
+      int i = 0;
+      for (; i < m_axis; ++i) {
+        eigen_assert(lhs_dims[i] > 0);
+        eigen_assert(lhs_dims[i] == rhs_dims[i]);
+        m_dimensions[i] = lhs_dims[i];
+      }
+      eigen_assert(lhs_dims[i] > 0);  // Now i == m_axis.
+      eigen_assert(rhs_dims[i] > 0);
+      m_dimensions[i] = lhs_dims[i] + rhs_dims[i];
+      for (++i; i < NumDims; ++i) {
+        eigen_assert(lhs_dims[i] > 0);
+        eigen_assert(lhs_dims[i] == rhs_dims[i]);
+        m_dimensions[i] = lhs_dims[i];
+      }
     }
 
     if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorPadding.h b/unsupported/Eigen/CXX11/src/Tensor/TensorPadding.h
index 2a7dd45c0..5a165dab0 100644
--- a/unsupported/Eigen/CXX11/src/Tensor/TensorPadding.h
+++ b/unsupported/Eigen/CXX11/src/Tensor/TensorPadding.h
@@ -185,11 +185,13 @@ struct TensorEvaluator<const TensorPaddingOp<PaddingDimensions, ArgType>, Device
   {
     Index inputIndex;
     if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      const Index idx = coords[0];
-      if (idx < m_padding[0].first || idx >= m_dimensions[0] - m_padding[0].second) {
-        return Scalar(0);
+      {
+        const Index idx = coords[0];
+        if (idx < m_padding[0].first || idx >= m_dimensions[0] - m_padding[0].second) {
+          return Scalar(0);
+        }
+        inputIndex = idx - m_padding[0].first;
       }
-      inputIndex = idx - m_padding[0].first;
       for (int i = 1; i < NumDims; ++i) {
         const Index idx = coords[i];
         if (idx < m_padding[i].first || idx >= m_dimensions[i] - m_padding[i].second) {
@@ -198,11 +200,13 @@ struct TensorEvaluator<const TensorPaddingOp<PaddingDimensions, ArgType>, Device
         inputIndex += (idx - m_padding[i].first) * m_inputStrides[i];
       }
     } else {
-      const Index idx = coords[NumDims-1];
-      if (idx < m_padding[NumDims-1].first || idx >= m_dimensions[NumDims-1] - m_padding[NumDims-1].second) {
-        return Scalar(0);
+      {
+        const Index idx = coords[NumDims-1];
+        if (idx < m_padding[NumDims-1].first || idx >= m_dimensions[NumDims-1] - m_padding[NumDims-1].second) {
+          return Scalar(0);
+        }
+        inputIndex = idx - m_padding[NumDims-1].first;
       }
-      inputIndex = idx - m_padding[NumDims-1].first;
       for (int i = NumDims - 2; i >= 0; --i) {
         const Index idx = coords[i];
         if (idx < m_padding[i].first || idx >= m_dimensions[i] - m_padding[i].second) {
diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorRef.h b/unsupported/Eigen/CXX11/src/Tensor/TensorRef.h
index fba7b20a9..6b25b2ba0 100644
--- a/unsupported/Eigen/CXX11/src/Tensor/TensorRef.h
+++ b/unsupported/Eigen/CXX11/src/Tensor/TensorRef.h
@@ -197,15 +197,15 @@ template<typename PlainObjectType> class TensorRef : public TensorBase<TensorRef
     template<typename... IndexTypes> EIGEN_DEVICE_FUNC
     EIGEN_STRONG_INLINE const Scalar operator()(Index firstIndex, IndexTypes... otherIndices) const
     {
-      const std::size_t NumIndices = (sizeof...(otherIndices) + 1);
-      const array<Index, NumIndices> indices{{firstIndex, otherIndices...}};
+      const std::size_t num_indices = (sizeof...(otherIndices) + 1);
+      const array<Index, num_indices> indices{{firstIndex, otherIndices...}};
       return coeff(indices);
     }
     template<typename... IndexTypes> EIGEN_DEVICE_FUNC
     EIGEN_STRONG_INLINE Scalar& coeffRef(Index firstIndex, IndexTypes... otherIndices)
     {
-      const std::size_t NumIndices = (sizeof...(otherIndices) + 1);
-      const array<Index, NumIndices> indices{{firstIndex, otherIndices...}};
+      const std::size_t num_indices = (sizeof...(otherIndices) + 1);
+      const array<Index, num_indices> indices{{firstIndex, otherIndices...}};
       return coeffRef(indices);
     }
 #else
diff --git a/unsupported/test/cxx11_tensor_reduction.cpp b/unsupported/test/cxx11_tensor_reduction.cpp
index 0269853a9..b2c85a879 100644
--- a/unsupported/test/cxx11_tensor_reduction.cpp
+++ b/unsupported/test/cxx11_tensor_reduction.cpp
@@ -17,11 +17,11 @@ template <int DataLayout>
 static void test_simple_reductions() {
   Tensor<float, 4, DataLayout> tensor(2, 3, 5, 7);
   tensor.setRandom();
-  array<ptrdiff_t, 2> reduction_axis;
-  reduction_axis[0] = 1;
-  reduction_axis[1] = 3;
+  array<ptrdiff_t, 2> reduction_axis2;
+  reduction_axis2[0] = 1;
+  reduction_axis2[1] = 3;
 
-  Tensor<float, 2, DataLayout> result = tensor.sum(reduction_axis);
+  Tensor<float, 2, DataLayout> result = tensor.sum(reduction_axis2);
   VERIFY_IS_EQUAL(result.dimension(0), 2);
   VERIFY_IS_EQUAL(result.dimension(1), 5);
   for (int i = 0; i < 2; ++i) {
@@ -40,20 +40,20 @@ static void test_simple_reductions() {
     Tensor<float, 1, DataLayout> sum1 = tensor.sum();
     VERIFY_IS_EQUAL(sum1.dimension(0), 1);
 
-    array<ptrdiff_t, 4> reduction_axis;
-    reduction_axis[0] = 0;
-    reduction_axis[1] = 1;
-    reduction_axis[2] = 2;
-    reduction_axis[3] = 3;
-    Tensor<float, 1, DataLayout> sum2 = tensor.sum(reduction_axis);
+    array<ptrdiff_t, 4> reduction_axis4;
+    reduction_axis4[0] = 0;
+    reduction_axis4[1] = 1;
+    reduction_axis4[2] = 2;
+    reduction_axis4[3] = 3;
+    Tensor<float, 1, DataLayout> sum2 = tensor.sum(reduction_axis4);
     VERIFY_IS_EQUAL(sum2.dimension(0), 1);
 
     VERIFY_IS_APPROX(sum1(0), sum2(0));
   }
 
-  reduction_axis[0] = 0;
-  reduction_axis[1] = 2;
-  result = tensor.prod(reduction_axis);
+  reduction_axis2[0] = 0;
+  reduction_axis2[1] = 2;
+  result = tensor.prod(reduction_axis2);
   VERIFY_IS_EQUAL(result.dimension(0), 3);
   VERIFY_IS_EQUAL(result.dimension(1), 7);
   for (int i = 0; i < 3; ++i) {
@@ -72,20 +72,20 @@ static void test_simple_reductions() {
     Tensor<float, 1, DataLayout> prod1 = tensor.prod();
     VERIFY_IS_EQUAL(prod1.dimension(0), 1);
 
-    array<ptrdiff_t, 4> reduction_axis;
-    reduction_axis[0] = 0;
-    reduction_axis[1] = 1;
-    reduction_axis[2] = 2;
-    reduction_axis[3] = 3;
-    Tensor<float, 1, DataLayout> prod2 = tensor.prod(reduction_axis);
+    array<ptrdiff_t, 4> reduction_axis4;
+    reduction_axis4[0] = 0;
+    reduction_axis4[1] = 1;
+    reduction_axis4[2] = 2;
+    reduction_axis4[3] = 3;
+    Tensor<float, 1, DataLayout> prod2 = tensor.prod(reduction_axis4);
     VERIFY_IS_EQUAL(prod2.dimension(0), 1);
 
     VERIFY_IS_APPROX(prod1(0), prod2(0));
   }
 
-  reduction_axis[0] = 0;
-  reduction_axis[1] = 2;
-  result = tensor.maximum(reduction_axis);
+  reduction_axis2[0] = 0;
+  reduction_axis2[1] = 2;
+  result = tensor.maximum(reduction_axis2);
   VERIFY_IS_EQUAL(result.dimension(0), 3);
   VERIFY_IS_EQUAL(result.dimension(1), 7);
   for (int i = 0; i < 3; ++i) {
@@ -104,20 +104,20 @@ static void test_simple_reductions() {
     Tensor<float, 1, DataLayout> max1 = tensor.maximum();
     VERIFY_IS_EQUAL(max1.dimension(0), 1);
 
-    array<ptrdiff_t, 4> reduction_axis;
-    reduction_axis[0] = 0;
-    reduction_axis[1] = 1;
-    reduction_axis[2] = 2;
-    reduction_axis[3] = 3;
-    Tensor<float, 1, DataLayout> max2 = tensor.maximum(reduction_axis);
+    array<ptrdiff_t, 4> reduction_axis4;
+    reduction_axis4[0] = 0;
+    reduction_axis4[1] = 1;
+    reduction_axis4[2] = 2;
+    reduction_axis4[3] = 3;
+    Tensor<float, 1, DataLayout> max2 = tensor.maximum(reduction_axis4);
     VERIFY_IS_EQUAL(max2.dimension(0), 1);
 
     VERIFY_IS_APPROX(max1(0), max2(0));
   }
 
-  reduction_axis[0] = 0;
-  reduction_axis[1] = 1;
-  result = tensor.minimum(reduction_axis);
+  reduction_axis2[0] = 0;
+  reduction_axis2[1] = 1;
+  result = tensor.minimum(reduction_axis2);
   VERIFY_IS_EQUAL(result.dimension(0), 5);
   VERIFY_IS_EQUAL(result.dimension(1), 7);
   for (int i = 0; i < 5; ++i) {
@@ -136,20 +136,20 @@ static void test_simple_reductions() {
     Tensor<float, 1, DataLayout> min1 = tensor.minimum();
     VERIFY_IS_EQUAL(min1.dimension(0), 1);
 
-    array<ptrdiff_t, 4> reduction_axis;
-    reduction_axis[0] = 0;
-    reduction_axis[1] = 1;
-    reduction_axis[2] = 2;
-    reduction_axis[3] = 3;
-    Tensor<float, 1, DataLayout> min2 = tensor.minimum(reduction_axis);
+    array<ptrdiff_t, 4> reduction_axis4;
+    reduction_axis4[0] = 0;
+    reduction_axis4[1] = 1;
+    reduction_axis4[2] = 2;
+    reduction_axis4[3] = 3;
+    Tensor<float, 1, DataLayout> min2 = tensor.minimum(reduction_axis4);
     VERIFY_IS_EQUAL(min2.dimension(0), 1);
 
     VERIFY_IS_APPROX(min1(0), min2(0));
   }
 
-  reduction_axis[0] = 0;
-  reduction_axis[1] = 1;
-  result = tensor.mean(reduction_axis);
+  reduction_axis2[0] = 0;
+  reduction_axis2[1] = 1;
+  result = tensor.mean(reduction_axis2);
   VERIFY_IS_EQUAL(result.dimension(0), 5);
   VERIFY_IS_EQUAL(result.dimension(1), 7);
   for (int i = 0; i < 5; ++i) {
@@ -170,12 +170,12 @@ static void test_simple_reductions() {
     Tensor<float, 1, DataLayout> mean1 = tensor.mean();
     VERIFY_IS_EQUAL(mean1.dimension(0), 1);
 
-    array<ptrdiff_t, 4> reduction_axis;
-    reduction_axis[0] = 0;
-    reduction_axis[1] = 1;
-    reduction_axis[2] = 2;
-    reduction_axis[3] = 3;
-    Tensor<float, 1, DataLayout> mean2 = tensor.mean(reduction_axis);
+    array<ptrdiff_t, 4> reduction_axis4;
+    reduction_axis4[0] = 0;
+    reduction_axis4[1] = 1;
+    reduction_axis4[2] = 2;
+    reduction_axis4[3] = 3;
+    Tensor<float, 1, DataLayout> mean2 = tensor.mean(reduction_axis4);
     VERIFY_IS_EQUAL(mean2.dimension(0), 1);
 
     VERIFY_IS_APPROX(mean1(0), mean2(0));