Add get/set method

demo/mnist/api_train_v2.py

@@ -28,16 +28,16 @@ def main():
     topology = parse_network_config(network_config)
     parameters = paddle.parameters.create(topology)
     for param_name in parameters.keys():
-        array = parameters[param_name]
+        array = parameters.get(param_name)
         array[:] = numpy.random.uniform(low=-1.0, high=1.0, size=array.shape)
-        parameters[param_name] = array
+        parameters.set(parameter_name=param_name, value=array)
 
     adam_optimizer = paddle.optimizer.Optimizer(
         learning_rate=0.01, learning_method=AdamOptimizer())
 
     def event_handler(event):
         if isinstance(event, paddle.event.EndIteration):
-            para = parameters['___fc_layer_2__.w0']
+            para = parameters.get('___fc_layer_2__.w0')
             print "Pass %d, Batch %d, Cost %f, Weight Mean Of Fc 2 is %f" % (
                 event.pass_id, event.batch_id, event.cost, para.mean())
 

python/paddle/v2/parameters.py

@@ -26,6 +26,10 @@ def create(*topologies):
 
 
 class Parameters(object):
+    """
+    The parameters
+    """
+
     def __init__(self):
         self.__param_conf__ = dict()
         self.__gradient_machines__ = []
@@ -66,7 +70,8 @@ class Parameters(object):
                 assert isinstance(param, api.Parameter)
                 val = param.getBuf(api.PARAMETER_VALUE)
                 assert isinstance(val, api.Vector)
-                return val.copyToNumpyArray().reshape(shape=shape)
+                val = val.copyToNumpyArray()
+                return val
                 # else continue
 
             raise RuntimeError("Unexpected branch")
@@ -96,6 +101,12 @@ class Parameters(object):
                 __copy_parameter_to_gradient_machine__(each_gradient_machine,
                                                        key, value)
 
+    def get(self, parameter_name):
+        return self.__getitem__(key=parameter_name)
+
+    def set(self, parameter_name, value):
+        self.__setitem__(key=parameter_name, value=value)
+
     def append_gradient_machine(self, gradient_machine):
         if not isinstance(gradient_machine, api.GradientMachine):
             raise ValueError("gradient_machine should be api.GradientMachine")
@@ -108,6 +119,7 @@ class Parameters(object):
                 except ValueError:
                     # If no such parameter in gradient machine, then don't copy
                     pass
+            self.__gradient_machines__.append(gradient_machine)
 
 
 def __get_parameter_in_gradient_machine__(gradient_machine, name):