Merge branch 'feature/serialize_deserialize_in_parameters' into feature/recommendation_v2_api

demo/mnist/.gitignore

@@ -5,3 +5,4 @@ plot.png
 train.log
 *pyc
 .ipynb_checkpoints
+params.pkl

demo/mnist/api_train_v2.py

@@ -1,4 +1,5 @@
 import paddle.v2 as paddle
+import cPickle
 
 
 def main():
@@ -16,7 +17,11 @@ def main():
                                 act=paddle.activation.Softmax())
     cost = paddle.layer.classification_cost(input=inference, label=label)
 
-    parameters = paddle.parameters.create(cost)
+    try:
+        with open('params.pkl', 'r') as f:
+            parameters = cPickle.load(f)
+    except IOError:
+        parameters = paddle.parameters.create(cost)
 
     adam_optimizer = paddle.optimizer.Adam(learning_rate=0.01)
 
@@ -35,6 +40,10 @@ def main():
                           event.pass_id, event.batch_id, event.cost,
                           event.metrics,
                           result.cost, result.metrics)
+
+                with open('params.pkl', 'w') as f:
+                    cPickle.dump(
+                        parameters, f, protocol=cPickle.HIGHEST_PROTOCOL)
         else:
             pass
 

python/paddle/v2/parameters.py

@@ -222,6 +222,33 @@ class Parameters(object):
 
         self.__gradient_machines__.append(gradient_machine)
 
+    def __getstate__(self):
+        params = {}
+        for name in self.names():
+            params[name] = self.get(name)
+
+        param_conf = {}
+        for name in self.__param_conf__:
+            conf = self.__param_conf__[name]
+            assert isinstance(conf, ParameterConfig)
+            param_conf[name] = conf.SerializeToString()
+
+        return {'conf': param_conf, 'params': params}
+
+    def __setstate__(self, obj):
+        Parameters.__init__(self)
+
+        def __impl__(conf, params):
+            for name in conf:
+                p = ParameterConfig()
+                p.ParseFromString(conf[name])
+                self.__append_config__(p)
+            for name in params:
+                shape = self.get_shape(name)
+                self.set(name, params[name].reshape(shape))
+
+        __impl__(**obj)
+
 
 def __get_parameter_in_gradient_machine__(gradient_machine, name):
     """

python/paddle/v2/trainer.py

@@ -66,9 +66,9 @@ class SGD(ITrainer):
             self.__topology_in_proto__, api.CREATE_MODE_NORMAL,
             self.__optimizer__.enable_types())
         assert isinstance(gm, api.GradientMachine)
-        parameters.append_gradient_machine(gm)
         self.__gradient_machine__ = gm
         self.__gradient_machine__.randParameters()
+        parameters.append_gradient_machine(gm)
 
     def train(self, reader, num_passes=1, event_handler=None, reader_dict=None):
         """