diff --git a/nntrainer/layers/conv2d_layer.cpp b/nntrainer/layers/conv2d_layer.cpp
index ada8752f7c..83013f04ee 100644
--- a/nntrainer/layers/conv2d_layer.cpp
+++ b/nntrainer/layers/conv2d_layer.cpp
@@ -85,7 +85,7 @@ static void col2im(const Tensor &col_matrix, const TensorDim &kdim,
   int h_stride_end = im_eff_height - eff_k_height - pt;
   int w_stride_end = im_eff_width - eff_k_width - pl;
 
-  auto apply_data = [&]<typename T>(T * val) {
+  auto apply_data = [&]<typename T>(T *val) {
     unsigned col_w = 0;
     for (int hs = -pt; hs <= h_stride_end; hs += hstride) {
       for (int ws = -pl; ws <= w_stride_end; ws += wstride) {
@@ -218,7 +218,7 @@ static void im2col(const Tensor &in, const TensorDim &kdim,
     TensorDim({out_height * out_width, in.channel() * k_height * k_width},
               in.getTensorType()));
 
-  auto apply_data = [&]<typename T>(T * out_data) {
+  auto apply_data = [&]<typename T>(T *out_data) {
     int h_stride_end = height - eff_k_height - pt;
     int w_stride_end = width - eff_k_width - pl;
 
diff --git a/nntrainer/layers/fc_layer.cpp b/nntrainer/layers/fc_layer.cpp
index 7fbcc4c467..57a8809d1a 100644
--- a/nntrainer/layers/fc_layer.cpp
+++ b/nntrainer/layers/fc_layer.cpp
@@ -39,8 +39,7 @@ static constexpr size_t SINGLE_INOUT_IDX = 0;
 enum FCParams { weight, bias };
 
 FullyConnectedLayer::FullyConnectedLayer() :
-  LayerImpl(),
-  fc_props(props::Unit()) {
+  LayerImpl(), fc_props(props::Unit()) {
   weight_idx.fill(std::numeric_limits<unsigned>::max());
 }
 
@@ -273,7 +272,6 @@ void FullyConnectedLayer::calcGradient(RunLayerContext &context) {
     }
     input_.dot_deriv_wrt_2(djdw_, derivative_, false, false, !wg_first_access);
   }
-
 }
 
 } /* namespace nntrainer */
diff --git a/nntrainer/layers/pooling2d_layer.cpp b/nntrainer/layers/pooling2d_layer.cpp
index 1638da9efe..bbd7bb6245 100644
--- a/nntrainer/layers/pooling2d_layer.cpp
+++ b/nntrainer/layers/pooling2d_layer.cpp
@@ -188,7 +188,7 @@ void Pooling2DLayer::calcDerivative(RunLayerContext &context) {
   unsigned int out_map_size = deriv.height() * deriv.width();
   unsigned int in_map_size = height * width;
 
-  auto apply_max = [&]<typename T>(T * result_data) {
+  auto apply_max = [&]<typename T>(T *result_data) {
     const int *iter = pool_helper.getData<int>();
     const T *deriv_data = deriv.getData<T>();
     for (unsigned int b = 0; b < batch; ++b) {
@@ -207,7 +207,7 @@ void Pooling2DLayer::calcDerivative(RunLayerContext &context) {
     }
   };
 
-  auto apply_average = [&]<typename T>(T * result_data) {
+  auto apply_average = [&]<typename T>(T *result_data) {
     int height_stride_end = height - p_height + pt;
     int width_stride_end = width - p_width + pl;
     const int *iter = pool_helper.getData<int>();
@@ -239,7 +239,7 @@ void Pooling2DLayer::calcDerivative(RunLayerContext &context) {
     }
   };
 
-  auto apply_global_max = [&]<typename T>(T * result_data) {
+  auto apply_global_max = [&]<typename T>(T *result_data) {
     const T *deriv_data = deriv.getData<T>();
     for (unsigned int b = 0; b < batch; b++) {
       for (unsigned int c = 0; c < channel; c++) {
@@ -372,8 +372,9 @@ void Pooling2DLayer::pooling2d(Tensor &in, bool training, Tensor &output,
     return max_val;
   };
 
-  auto pool_fn_global_max = [&, this ]<typename T>(
-    const T *in_data, int channel_idx, int start_h, int start_w) {
+  auto pool_fn_global_max = [&, this]<typename T>(const T *in_data,
+                                                  int channel_idx, int start_h,
+                                                  int start_w) {
     int end_h = start_h + patch_height;
     int end_w = start_w + patch_width;
 
diff --git a/nntrainer/tensor/tensor.cpp b/nntrainer/tensor/tensor.cpp
index 7fa61cf73b..9065192242 100644
--- a/nntrainer/tensor/tensor.cpp
+++ b/nntrainer/tensor/tensor.cpp
@@ -127,8 +127,7 @@ class SrcSharedTensor {
   SrcSharedTensor() : src(nullptr), off(0) {}
 
   SrcSharedTensor(const Tensor *tensor, size_t offset) :
-    src(tensor),
-    off(offset) {}
+    src(tensor), off(offset) {}
 
   /**
    * @brief   Get the allocated src tensor