A simplest train file for mnist added.

demo/mnist/api_train.py

@@ -58,11 +58,25 @@ def main():
 
     for _ in xrange(100):
         updater.startPass()
+        outArgs = api.Arguments.createArguments(0)
         train_data_generator = input_order_converter(
             read_from_mnist(train_file))
-        for data_batch in generator_to_batch(train_data_generator, 128):
+        for batch_id, data_batch in enumerate(
+                generator_to_batch(train_data_generator, 256)):
             trainRole = updater.startBatch(len(data_batch))
 
+            def update_callback(param):
+                updater.update(param)
+
+            m.forwardBackward(
+                converter(data_batch), outArgs, trainRole, update_callback)
+
+            cost_vec = outArgs.getSlotValue(0)
+            cost_vec = cost_vec.copyToNumpyMat()
+            cost = cost_vec.sum() / len(data_batch)
+            print 'Batch id', batch_id, 'with cost=', cost
+            updater.finishBatch(cost)
+
         updater.finishPass()
 
     m.finish()

paddle/api/PaddleAPI.h

@@ -799,7 +799,7 @@ public:
 
   void finishPass();
 
-  PassType startBatch(int64_t batchSize);
+  PassType startBatch(size_t batchSize);
 
   void finishBatch(float cost);
 

paddle/api/ParameterUpdater.cpp

@@ -36,8 +36,8 @@ void ParameterUpdater::startPass() { m->updater->startPass(); }
 
 void ParameterUpdater::finishPass() { m->updater->finishPass(); }
 
-PassType ParameterUpdater::startBatch(int64_t batchSize) {
-  return m->updater->startBatch(batchSize);
+PassType ParameterUpdater::startBatch(size_t batchSize) {
+  return m->updater->startBatch((int64_t)batchSize);
 }
 
 void ParameterUpdater::finishBatch(float cost) {