diff --git a/paddle/fluid/operators/spectral_norm_op.cc b/paddle/fluid/operators/spectral_norm_op.cc
index 32b8a41ca88ff029791c2b7576784161e1cccc2b..087d97fde68a06127bafec0dcd5d13d615ec9d01 100644
--- a/paddle/fluid/operators/spectral_norm_op.cc
+++ b/paddle/fluid/operators/spectral_norm_op.cc
@@ -94,7 +94,7 @@ class SpectralNormOpMaker : public framework::OpProtoAndCheckerMaker {
              "W is the 2nd dimentions of Weight after reshape"
              "corresponding by Attr(dim). As for Attr(dim) = 1"
              "in conv2d layer with weight shape [M, C, K1, K2]"
-             "Weight will be reshape to [C, M*K1*Kw], V will"
+             "Weight will be reshape to [C, M*K1*K2], V will"
              "be in shape [M*K1*K2, 1].");
     AddOutput("Out",
               "The output weight tensor of spectral_norm operator, "
@@ -105,7 +105,7 @@ class SpectralNormOpMaker : public framework::OpProtoAndCheckerMaker {
                  "it should be set as 0 if Input(Weight) is the"
                  "weight of fc layer, and should be set as 1 if"
                  "Input(Weight) is the weight of conv layer,"
-                 "default is 0."
+                 "default is 0.")
         .SetDefault(0);
     AddAttr<int>("power_iters",
                  "number of power iterations to calculate"
diff --git a/paddle/fluid/operators/spectral_norm_op.h b/paddle/fluid/operators/spectral_norm_op.h
index de6e894c1ce1a82f6461a14c0355f24fef0387e7..eb48e3b7840e18efe809540dd697f243a0a63a52 100644
--- a/paddle/fluid/operators/spectral_norm_op.h
+++ b/paddle/fluid/operators/spectral_norm_op.h
@@ -73,13 +73,13 @@ static inline void CalcMatrixSigmaAndNormWeight(
   const int w = weight->dims()[1];
 
   for (int i = 0; i < power_iters; i++) {
-    // V = W^T * U / ||W^T * U||_2 
+    // V = W^T * U / ||W^T * U||_2
     blas.MatMul(*weight, true, *u, false, T(1), v, T(0));
     auto v_t_norm =
         v_t.square().sum().sqrt().eval().reshape(Array1(1)).broadcast(
             Array1(w));
     v_t.device(place) = v_t / (v_t_norm + v_t_norm.constant(eps));
-    // U = W^T * V / ||W^T * V||_2 
+    // U = W^T * V / ||W^T * V||_2
     blas.MatMul(*weight, false, *v, false, T(1), u, T(0));
     auto u_t_norm =
         u_t.square().sum().sqrt().eval().reshape(Array1(1)).broadcast(