Refine ChunkEvalutor by following comments

paddle/operators/chunk_eval_op.cc

@@ -80,14 +80,16 @@ class ChunkEvalOpMaker : public framework::OpProtoAndCheckerMaker {
               "sensitivity) of chunks on the given mini-batch.");
     AddOutput("F1-Score",
               "(float). The evaluated F1-Score on the given mini-batch.");
+    AddOutput("NumInferChunks",
+              "(int64_t). The number of chunks in Inference on the given "
+              "mini-batch.");
     AddOutput(
-        "NumInferChunks",
-        "(int). The number of chunks in Inference on the given mini-batch.");
-    AddOutput("NumLabelChunks",
-              "(int). The number of chunks in Label on the given mini-batch.");
-    AddOutput("NumCorrectChunks",
-              "(int). The number of chunks both in Inference and Label on the "
-              "given mini-batch.");
+        "NumLabelChunks",
+        "(int64_t). The number of chunks in Label on the given mini-batch.");
+    AddOutput(
+        "NumCorrectChunks",
+        "(int64_t). The number of chunks both in Inference and Label on the "
+        "given mini-batch.");
     AddAttr<int>("num_chunk_types",
                  "(int). The number of chunk type. See below for details.");
     AddAttr<std::string>(

python/paddle/v2/fluid/tests/book/test_label_semantic_roles.py

@@ -178,20 +178,19 @@ def main():
     for pass_id in xrange(PASS_NUM):
         chunk_evaluator.reset(exe)
         for data in train_data():
-            outs = exe.run(fluid.default_main_program(),
-                           feed=feeder.feed(data),
-                           fetch_list=[avg_cost] + chunk_evaluator.metrics)
-            precision, recall, f1_score = chunk_evaluator.eval(exe)
-            avg_cost_val = np.array(outs[0])
-            precision_val = np.array(precision)
-            recall_val = np.array(recall)
-            f1_score_val = np.array(f1_score)
+            cost, precision, recall, f1_score = exe.run(
+                fluid.default_main_program(),
+                feed=feeder.feed(data),
+                fetch_list=[avg_cost] + chunk_evaluator.metrics)
+            pass_precision, pass_recall, pass_f1_score = chunk_evaluator.eval(
+                exe)
 
             if batch_id % 10 == 0:
-                print("avg_cost=" + str(avg_cost_val))
-                print("precision_val=" + str(precision_val))
-                print("recall_val:" + str(recall_val))
-                print("f1_score_val:" + str(f1_score_val))
+                print("avg_cost:" + str(cost) + " precision:" + str(
+                    precision) + " recall:" + str(recall) + " f1_score:" + str(
+                        f1_score) + " pass_precision:" + str(
+                            pass_precision) + " pass_recall:" + str(pass_recall)
+                      + " pass_f1_score:" + str(pass_f1_score))
 
             # exit early for CI
             exit(0)