updates

source/train/TTrain.cpp

@@ -306,6 +306,7 @@ run the neural network
 */
 bool TTModel::RunSimple(XList * inputs, XList * outputs, XList * golds, XList* losses)
 {
+    //fprintf(stderr, "run simple 0\n");
     CheckNTErrors(inputs != NULL && inputs->count >= 1, "Wrong arguments!");
     CheckNTErrors(outputs != NULL && outputs->count >= 1, "Wrong arguments!");
     CheckNTErrors(golds != NULL && golds->count >= 1, "Wrong arguments!");
@@ -338,6 +339,8 @@ bool TTModel::RunSimple(XList * inputs, XList * outputs, XList * golds, XList* l
     net.Backward(*loss);
 
     delete[] dims;
+    
+    //fprintf(stderr, "run simple 1\n");
 
     return true;
 }

source/train/XLeader.cpp

@@ -119,6 +119,12 @@ float XLeader::GetLoss()
 {
     return serverRecord.lossAll;
 }
+    
+/* get sample number */
+int XLeader::GetSampleNum()
+{
+    return serverRecord.sampleNum;
+}
 
 /* get prediction number */
 int XLeader::GetPredictNum()

source/train/XLeader.h

@@ -111,6 +111,9 @@ public:
 
     /* get loss */
     float GetLoss();
+    
+    /* get sample number */
+    int GetSampleNum();
 
     /* get prediction number */
     int GetPredictNum();
@@ -143,4 +146,4 @@ public:
 
 }
 
-#endif // __XLEADER_H__
\ No newline at end of file
+#endif // __XLEADER_H__

source/train/XNNRecord.cpp

@@ -45,6 +45,7 @@ XNNRecord::~XNNRecord()
 void XNNRecord::Clear()
 {
 	lossAll = 0;
+    sampleNum = 0;
 	predictNum = 0;
 	state = XWORKER_UNSTARTED;
 }
@@ -53,6 +54,7 @@ void XNNRecord::Clear()
 void XNNRecord::Update(XNNRecord & record)
 {
 	lossAll += record.lossAll;
+    sampleNum += record.sampleNum;
 	predictNum += record.predictNum;
 
 }

source/train/XNNRecord.h

@@ -39,6 +39,9 @@ class XNNRecord
 public:
 	/* loss over all samples */
 	float lossAll;
+    
+    /* sample number */
+    int sampleNum;
 
 	/* prediction number */
 	int predictNum;
@@ -61,4 +64,4 @@ public:
 };
 }
 
-#endif
\ No newline at end of file
+#endif

source/train/XTrainer.cpp

@@ -103,9 +103,6 @@ void XTrainer::Run(XConfig * config, DataDistributeBase * dataDistributor,
     int * ids = new int[MAX_DEVICE_NUM_TRAINING];
     GetDevIDs(config, ids, jobNum, MAX_DEVICE_NUM_TRAINING);
 
-    float lossAll = 0;
-    int predictNum = 0;
-
     /* create the server and workers */
     XLeader leader;
     leader.Init();
@@ -127,11 +124,11 @@ void XTrainer::Run(XConfig * config, DataDistributeBase * dataDistributor,
             /* one step of udpate */
             ok = leader.Run(config, dataDistributor, model, optimizer);
 
-            float loss = leader.GetLoss() / leader.GetPredictNum();
+            float loss = leader.GetLoss() / leader.GetSampleNum();
 
-            if ((step + 1) % 100 == 0)
-                fprintf(stderr, "epoch:%d step:%d loss:%f predict:%d\n", 
-                        epoch + 1, step + 1, loss, leader.GetPredictNum());
+            if ((step + 1) % 1 == 0)
+                fprintf(stderr, "epoch:%d step:%d sample:%d loss:%f predict:%d\n",
+                        epoch + 1, step + 1, leader.GetSampleNum(), loss, leader.GetPredictNum());
 
             if (step++ >= nstep)
                 break;

source/train/XWorkerBroadcast.cpp

@@ -144,4 +144,4 @@ bool XWorkerBroadcast::AddJobBroadcast(XModel * source, XList * targetList)
     return true;
 }
 
-}
\ No newline at end of file
+}

source/train/XWorkerJob.cpp

@@ -134,13 +134,16 @@ XNNRecord * XWorkerJob::GetRecord()
 void XWorkerJob::RecordMe()
 {
     float lossAll = 0;
+    int sampleNum = 0;
 
     for (int i = 0; i < losses.count; i++) {
         XTensor* loss = (XTensor*)losses[i];
         lossAll += ReduceSumAllValue(*loss);
+        sampleNum += loss->GetSize();
     }
 
     record.lossAll = lossAll;
+    record.sampleNum = sampleNum;
 
     int predictNum = 0;
 
@@ -157,6 +160,12 @@ float XWorkerJob::GetLossAll()
 {
     return record.lossAll;
 }
+    
+/* get the number of samples */
+int XWorkerJob::GetSampleNum()
+{
+    return record.sampleNum;
+}
 
 /* get the number of outputs (predictoins) */
 int XWorkerJob::GetPredictNum()

source/train/XWorkerJob.h

@@ -99,6 +99,9 @@ public:
 
     /* get the sum of losses over samples */
     float GetLossAll();
+    
+    /* get the number of samples */
+    int GetSampleNum();
 
     /* get the number of outputs (predictoins) */
     int GetPredictNum();