Complete update equation.

demo/mnist/api_train_v2.py

@@ -49,7 +49,7 @@ def main():
         :return:
         """
         mu = 0.09
-        e = 0.0001
+        e = 0.00001
 
         vel_t = mu * vel_t_1 - e * g
 

python/paddle/v2/trainer.py

@@ -25,7 +25,7 @@ class CompleteTrainOneBatch(BaseEvent):
         self.pass_id = pass_id
         self.batch_id = batch_id
         self.cost = cost
-        self.paramters = parameters
+        self.parameters = parameters
 
 
 def default_event_handler(event):
@@ -44,6 +44,17 @@ class ITrainer(object):
 
 class LazyParameterPool(v2_parameters.IParameterPool):
     """
+    Lazy Parameter Pool stores a reference to GradientMachine. User could invoke
+    `get_parameter` if needed, but the operation is lazy. It means the parameter
+    will only fetched from GPU or Parameter Server if `get_parameter` is
+    invoked. Also, set flag = writable will make a extra host2device copy after
+    reading/modifying parameter.
+
+    This class is not exposed to User. User should treat this class as a normal
+    IParameterPool.
+
+    See IParameterPool for usage documentation.
+
     :type __gradient_machine__: api.GradientMachine
     """
 
@@ -130,12 +141,22 @@ class CustomizeUpdateEquation(object):
                 shape)
             g = param.getBuf(api.PARAMETER_GRADIENT).toNumpyArrayInplace(
             ).reshape(shape)
-            args = [v, g]
-            for arg in self.local_params[conf.name]:
-                args.append(arg)
-            self.__callback__(*args)
+
         else:
-            raise NotImplementedError()
+            v = param.getBuf(api.PARAMETER_VALUE).copyToNumpyArray().reshape(
+                shape)
+            g = param.getBuf(api.PARAMETER_GRADIENT).copyToNumpyArray().reshape(
+                shape)
+
+        args = [v, g]
+        for arg in self.local_params[conf.name]:
+            args.append(arg)
+        self.__callback__(*args)
+
+        if api.isUsingGpu():
+            param.getBuf(api.PARAMETER_VALUE).copyFromNumpyArray(v.flatten(
+            ).astype('float32'))
+            # discard gradient changed.
 
 
 class SGDTrainer(ITrainer):