reback nn.py

python/paddle/fluid/dygraph/nn.py

@@ -1375,7 +1375,7 @@ class BatchNorm(layers.Layer):
 
         outputs = {
             "Y": [batch_norm_out],
-            "MeanOut": [],
+            "MeanOut": [mean_out],
             "VarianceOut": [variance_out],
             "SavedMean": [saved_mean],
             "SavedVariance": [saved_variance]
@@ -3031,11 +3031,9 @@ class SpectralNorm(layers.Layer):
         dim(int, optional): The index of dimension which should be permuted to the first before reshaping 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.
         power_iters(int, optional): The number of power iterations to calculate spectral norm. Default: 1.
         eps(float, optional): The epsilon for numerical stability in calculating norms. Default: 1e-12.
-        fix_state(bool, optional): whether to update the two vectors `u` and `v`. Default: True.
         name (str, optional): The default value is None.  Normally there is no need for user to set this property.  For more information, please refer to :ref:`api_guide_Name` .
         dtype (str, optional): Data type, it can be "float32" or "float64". Default: "float32".
 
-
     Returns:
         None
 
@@ -3057,12 +3055,10 @@ class SpectralNorm(layers.Layer):
                  dim=0,
                  power_iters=1,
                  eps=1e-12,
-                 fix_state=True,
                  dtype='float32'):
         super(SpectralNorm, self).__init__()
         self._power_iters = power_iters
         self._eps = eps
-        self._fix_state = fix_state
         self._dim = dim
         self._dtype = dtype
 
@@ -3084,31 +3080,10 @@ class SpectralNorm(layers.Layer):
             default_initializer=Normal(0., 1.))
         self.weight_v.stop_gradient = True
 
-        if fix_state:
-            self.out_weight_u = self.create_parameter(
-                attr=ParamAttr(),
-                shape=[h],
-                dtype=self._dtype,
-                default_initializer=Normal(0., 1.))
-            self.out_weight_u.stop_gradient = True
-
-            self.out_weight_v = self.create_parameter(
-                attr=ParamAttr(),
-                shape=[w],
-                dtype=self._dtype,
-                default_initializer=Normal(0., 1.))
-            self.out_weight_v.stop_gradient = True
-        else:
-            self.out_weight_u = self.weight_u
-            self.out_weight_v = self.weight_v
-
     def forward(self, weight):
         check_variable_and_dtype(weight, "weight", ['float32', 'float64'],
                                  'SpectralNorm')
-        inputs = {
-            'Weight': weight, 'U': self.weight_u, 'V': self.weight_v,
-            'UOut': self.out_weight_u, 'VOut': self.out_weight_v,
-        }
+        inputs = {'Weight': weight, 'U': self.weight_u, 'V': self.weight_v}
         out = self._helper.create_variable_for_type_inference(self._dtype)
         self._helper.append_op(
             type="spectral_norm",