Try expose ParamUpdater::update

demo/mnist/api_train.py

@@ -45,7 +45,6 @@ def main():
         config.model_config, api.CREATE_MODE_NORMAL, enable_types)
     assert isinstance(m, api.GradientMachine)
     init_parameter(network=m)
-
     updater = api.ParameterUpdater.createLocalUpdater(opt_config)
     assert isinstance(updater, api.ParameterUpdater)
     updater.init(m)
@@ -62,7 +61,7 @@ def main():
         train_data_generator = input_order_converter(
             read_from_mnist(train_file))
         for data_batch in generator_to_batch(train_data_generator, 128):
-            inArgs = converter(data_batch)
+            trainRole = updater.startBatch(len(data_batch))
 
         updater.finishPass()
 

paddle/api/PaddleAPI.h

@@ -799,6 +799,12 @@ public:
 
   void finishPass();
 
+  PassType startBatch(int64_t batchSize);
+
+  void finishBatch(float cost);
+
+  void update(Parameter* param);
+
 private:
   ParameterUpdaterPrivate* m;
 };

paddle/api/ParameterUpdater.cpp

@@ -35,3 +35,16 @@ void ParameterUpdater::init(const GradientMachine &gm) {
 void ParameterUpdater::startPass() { m->updater->startPass(); }
 
 void ParameterUpdater::finishPass() { m->updater->finishPass(); }
+
+PassType ParameterUpdater::startBatch(int64_t batchSize) {
+  return m->updater->startBatch(batchSize);
+}
+
+void ParameterUpdater::finishBatch(float cost) {
+  m->updater->finishBatch(cost);
+}
+
+void ParameterUpdater::update(Parameter *param) {
+  auto paddleParam = param->m->getPtr();
+  m->updater->update(paddleParam);
+}