fix hs problem and polish code

fluid/PaddleRec/word2vec/README.md

@@ -31,8 +31,9 @@ python preprocess.py --data_path ./data/1-billion-word-language-modeling-benchma
 The command line options for training can be listed by `python train.py -h`.
 
 ### Local Train:
+we set CPU_NUM=1 as default CPU_NUM to execute
 ```bash
-export CPU_NUM=1
+export CPU_NUM=1 && \
 python train.py \
         --train_data_path ./data/1-billion-word-language-modeling-benchmark-r13output/training-monolingual.tokenized.shuffled \
         --dict_path data/1-billion_dict \

fluid/PaddleRec/word2vec/infer.py

@@ -113,6 +113,13 @@ class PQ_Entry(object):
 
 def topK(k, emb, test_emb):
     pq = PriorityQueue(k + 1)
+    while not pq.empty():
+        try:
+            pq.get(False)
+        except Empty:
+            continue
+        pq.task_done()
+
     if len(emb) <= k:
         for i in range(len(emb)):
             x = cosine_similarity([emb[i]], [test_emb])
@@ -181,5 +188,7 @@ if __name__ == '__main__':
     # while setting infer_once please specify the dir to models file with --model_output_dir
     if args.infer_once:
         infer_once(args)
-    if args.infer_during_train:
+    elif args.infer_during_train:
         infer_during_train(args)
+    else:
+        pass

fluid/PaddleRec/word2vec/reader.py

@@ -125,10 +125,10 @@ class Word2VecReader(object):
                                 for context_id in context_word_ids:
                                     yield [target_id], [context_id], [
                                         self.word_to_code[self.id_to_word[
-                                            context_id]]
+                                            target_id]]
                                     ], [
                                         self.word_to_path[self.id_to_word[
-                                            context_id]]
+                                            target_id]]
                                     ]
                         else:
                             pass

fluid/PaddleRec/word2vec/train.py

@@ -257,9 +257,9 @@ def train(args):
 
     optimizer = None
     if args.with_Adam:
-        optimizer = fluid.optimizer.Adam(learning_rate=1e-3)
+        optimizer = fluid.optimizer.Adam(learning_rate=1e-4)
     else:
-        optimizer = fluid.optimizer.SGD(learning_rate=1e-3)
+        optimizer = fluid.optimizer.SGD(learning_rate=1e-4)
 
     optimizer.minimize(loss)