diff --git a/paddle/fluid/operators/spectral_norm_op.cc b/paddle/fluid/operators/spectral_norm_op.cc
index 56856c45b47a8450e28cc8cc65e15bb4e8a7b21c..0d43e65c86cdfaded7754655305269417c0ce211 100644
--- a/paddle/fluid/operators/spectral_norm_op.cc
+++ b/paddle/fluid/operators/spectral_norm_op.cc
@@ -109,10 +109,32 @@ class SpectralNormOpMaker : public framework::OpProtoAndCheckerMaker {
         .SetDefault(1e-12);
 
     AddComment(R"DOC(
-          This operator samples input X to given output shape by using specified
+          This layer calculate the spectral normalize value of weight of
+          fc, conv1d, conv2d, conv3d layers which should be 2-D, 3-D, 4-D, 5-D
+          tensor.
 
-          
+          Spectral normalization stabilizes the training of critis in GANs
+          (Generative Adversarial Networks). This layers rescaling weight tensor
+          wiht spectral normalize value.
 
+          For spectral normalization calculations, we rescaling weight
+          tensor with \sigma, while \sigma{\mathbf{W}} is
+
+            \sigma(\mathbf{W}) = \max_{\mathbf{h}: \mathbf{h} \ne 0} \dfrac{\|\mathbf{W} \mathbf{h}\|_2}{\|\mathbf{h}\|_2}
+
+          We calculate \sigma{\mathbf{W}} through power iterations as
+
+            \mathbf{v} = \mathbf{W}^{T} \mathbf{u}
+            \mathbf{v} = \frac{\mathbf{v}}{\|\mathbf{v}\|_2}
+            \mathbf{u} = \mathbf{W}^{T} \mathbf{v}
+            \mathbf{u} = \frac{\mathbf{u}}{\|\mathbf{u}\|_2}
+
+          And \sigma should be
+
+            \sigma{\mathbf{W}} = \mathbf{u}^{T} \mathbf{W} \mathbf{v}
+
+          For details of spectral normalization, please refer to paper: 
+          `Spectral Normalization <https://arxiv.org/abs/1802.05957>`_ .
          )DOC");
   }
 };
diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py
index 2eb18e447f789c538ae7e0d1334c343b89f36b7c..4862733e74e77c3e67fe4156cc73e9c7bc44e6e7 100644
--- a/python/paddle/fluid/layers/nn.py
+++ b/python/paddle/fluid/layers/nn.py
@@ -3349,28 +3349,42 @@ def group_norm(input,
 
 
 @templatedoc()
-def spectral_norm(weight,
-                  dim=0,
-                  power_iters=1,
-                  eps=1e-12,
-                  u_attr=None,
-                  v_attr=None,
-                  name=None):
+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
+    fc, conv1d, conv2d, conv3d layers which should be 2-D, 3-D, 4-D, 5-D
+    Parameters. Calculations are showed as followings.
+
+    .. code-block:: text
+
+        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.
+
+        Step 2:
+        While attr:`power_iters` is a positive interger, do following
+        iteration calculations with u and v for attr:`power_iters`
+        round.
+            \mathbf{v} = \mathbf{W}^{T} \mathbf{u}
+            \mathbf{v} = \frac{\mathbf{v}}{\|\mathbf{v}\|_2}
+            \mathbf{u} = \mathbf{W}^{T} \mathbf{v}
+            \mathbf{u} = \frac{\mathbf{u}}{\|\mathbf{u}\|_2}
+
+        Step 3:
+        Calculate \sigma{W} and scale weight values.
+            \sigma{\mathbf{W}} = \mathbf{u}^{T} \mathbf{W} \mathbf{v}
+            \mathbf{W} := \frac{\mathbf{W}}{\sigma{\mathbf{W}}}
+                
+
     Refer to `Spectral Normalization <https://arxiv.org/abs/1802.05957>`_ .
 
     Args:
         weight(${weight_type}): ${weight_comment}
         dim(${dim_type}): ${dim_comment}
         eps(${eps_type}): ${eps_comment}
-        u_attr(ParamAttr|None): The parameter attribute for vector u in 
-            spectral calculatings, set None to use default attribute, which
-            generates random values in normal distribution N(0, 1). Default: None.
-        v_attr(ParamAttr|None): The parameter attribute for vector v in 
-            spectral calculatings, set None to use default attribute, which
-            generates random values in normal distribution N(0, 1). Default: None.
         name (str): The name of this layer. It is optional.
 
     Returns:
@@ -3383,43 +3397,43 @@ def spectral_norm(weight,
         >>> x = fluid.layers.spectral_norm(weight=data, dim=1, power_iters=2)
     """
     helper = LayerHelper('spectral_norm', **locals())
-    dtype = helper.input_dtype()
+    dtype = weight.dtype
 
     # create intput and parameters
     inputs = {'Weight': weight}
-    input_shape = input.shape
-    if data_layout != 'NCHW':
-        raise ValueError("unsupported data layout:" + data_layout)
-    param_shape = [input_shape[1]]
-    if param_attr:
-        scale = helper.create_parameter(
-            attr=helper.param_attr,
-            shape=param_shape,
-            dtype=dtype,
-            default_initializer=Constant(1.0))
-        inputs['Scale'] = scale
-    if bias_attr:
-        bias = helper.create_parameter(
-            attr=helper.bias_attr, shape=param_shape, dtype=dtype, is_bias=True)
-        inputs['Bias'] = bias
+    input_shape = weight.shape
+    h = input_shape[dim]
+    w = np.prod(input_shape) // h
+
+    u = helper.create_parameter(
+        attr=ParamAttr(),
+        shape=[h],
+        dtype=dtype,
+        default_initializer=Normal(0., 1.))
+    u.stop_gradient = True
+    inputs['U'] = u
+    v = helper.create_parameter(
+        attr=ParamAttr(),
+        shape=[w],
+        dtype=dtype,
+        default_initializer=Normal(0., 1.))
+    inputs['V'] = v
+    v.stop_gradient = True
 
     # create output
-    mean_out = helper.create_variable(dtype=dtype, stop_gradient=True)
-    variance_out = helper.create_variable(dtype=dtype, stop_gradient=True)
-    group_norm_out = helper.create_variable(dtype=dtype)
+    out = helper.create_variable(dtype=dtype)
 
     helper.append_op(
-        type="group_norm",
+        type="spectral_norm",
         inputs=inputs,
-        outputs={
-            "Y": group_norm_out,
-            "Mean": mean_out,
-            "Variance": variance_out,
-        },
-        attrs={"epsilon": epsilon,
-               "groups": groups})
+        outputs={"Out": out, },
+        attrs={
+            "dim": dim,
+            "power_iters": power_iters,
+            "eps": eps,
+        })
 
-    return helper.append_activation(group_norm_out)
+    return out
 
 
 def conv2d_transpose(input,
diff --git a/python/paddle/fluid/tests/unittests/test_layers.py b/python/paddle/fluid/tests/unittests/test_layers.py
index 30194f8cacfea2361ffe4afe537287a261cf470b..ff49c1be979a2076952963ec54302fb68361eedf 100644
--- a/python/paddle/fluid/tests/unittests/test_layers.py
+++ b/python/paddle/fluid/tests/unittests/test_layers.py
@@ -1035,6 +1035,19 @@ class TestBook(unittest.TestCase):
 
         print(str(program))
 
+    def test_spectral_norm(self):
+        program = Program()
+        with program_guard(program):
+            weight = layers.data(
+                name='weight',
+                shape=[2, 3, 32, 32],
+                dtype="float32",
+                append_batch_size=False)
+            out = layers.spectral_norm(weight, dim=1, power_iters=1)
+            self.assertIsNotNone(out)
+
+        print(str(program))
+
     def test_shuffle_channel(self):
         program = Program()
         with program_guard(program):