fix doc statement. test=develop

paddle/fluid/operators/spectral_norm_op.cc

@@ -101,9 +101,10 @@ class SpectralNormOpMaker : public framework::OpProtoAndCheckerMaker {
               "This tensor is in same shape with Input(Weight).");
 
     AddAttr<int>("dim",
-                 "dimension corresponding to number of outputs, "
-                 "it should be set as 0 if Input(Weight) is the "
-                 "weight of fc layer, and should be set as 1 if "
+                 "The index of dimention which should be permute "
+                 "to the first before reshape Input(Weight) to "
+                 "matrix, 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 0.")
         .SetDefault(0);
@@ -112,12 +113,12 @@ class SpectralNormOpMaker : public framework::OpProtoAndCheckerMaker {
                  "spectral norm, default 1.")
         .SetDefault(1);
     AddAttr<float>("eps",
-                   "epsilob for numerical stability in "
+                   "epsilon for numerical stability in "
                    "calculating norms")
         .SetDefault(1e-12);
 
     AddComment(R"DOC(
-          This layer calculates the spectral normalize value of weight of
+          This layer calculates the spectral normalization value of weight of
           fc, conv1d, conv2d, conv3d layers which should be 2-D, 3-D, 4-D, 5-D
           tensor.
 

python/paddle/fluid/layers/nn.py

@@ -3353,14 +3353,14 @@ def spectral_norm(weight, dim=0, power_iters=1, eps=1e-12, name=None):
     """
     **Spectral Normalization Layer**
 
-    This layer calculate the spectral normalize value of weight parameters of
+    This layer calculates the spectral normalization value of weight parameters of
     fc, conv1d, conv2d, conv3d layers which should be 2-D, 3-D, 4-D, 5-D
-    Parameters. Calculations are showed as followings.
+    Parameters. Calculations are showed as follows.
 
     Step 1:
     Generate vector U in shape of [H], and V in shape of [W].
     While H is the :attr:`dim` th dimension of the input weights,
-    and W is the product result of remain dimensions.
+    and W is the product result of remaining dimensions.
 
     Step 2:
     :attr:`power_iters` shoule be a positive interger, do following
@@ -3373,7 +3373,7 @@ def spectral_norm(weight, dim=0, power_iters=1, eps=1e-12, name=None):
         \mathbf{u} := \\frac{\mathbf{W}^{T} \mathbf{v}}{\|\mathbf{W}^{T} \mathbf{v}\|_2}
 
     Step 3:
-    Calculate :math:`\sigma(\mathbf{W})` and scale weight values.
+    Calculate :math:`\sigma(\mathbf{W})` and normalize weight values.
 
     .. math::
 
@@ -3392,7 +3392,7 @@ def spectral_norm(weight, dim=0, power_iters=1, eps=1e-12, name=None):
         name (str): The name of this layer. It is optional.
 
     Returns:
-        Variable: A tensor variable of weight after spetral normalization.
+        Variable: A tensor variable of weight parameters after spectral normalization.
 
     Examples: