Merge branch 'master' into fixdebug

models/rank/dataset/Criteo_data/get_slot_data.py

@@ -87,7 +87,7 @@ class Reader(dg.MultiSlotDataGenerator):
                 v = i[1]
                 for j in v:
                     s += " " + k + ":" + str(j)
-            print s.strip()
+            print(s.strip())
             yield None
 
         return data_iter

models/rank/dcn/data/get_slot_data.py

@@ -92,7 +92,7 @@ class Reader(dg.MultiSlotDataGenerator):
                 v = i[1]
                 for j in v:
                     s += " " + k + ":" + str(j)
-            print s.strip()
+            print(s.strip())
             yield None
 
         return data_iter

models/rank/deepfm/data/get_slot_data.py

@@ -79,7 +79,7 @@ class Reader(dg.MultiSlotDataGenerator):
                 v = i[1]
                 for j in v:
                     s += " " + k + ":" + str(j)
-            print s.strip()
+            print(s.strip())
             yield None
 
         return data_iter

models/rank/dnn/README.md

@@ -185,7 +185,7 @@ inputs = [dense_input] + sparse_input_ids + [label]
 
 ### CTR-DNN模型组网
 
-CTR-DNN模型的组网比较直观，本质是一个二分类任务，代码参考`network_conf.py`。模型主要组成是一个`Embedding`层，三个`FC`层，以及相应的分类任务的loss计算和auc计算。
+CTR-DNN模型的组网比较直观，本质是一个二分类任务，代码参考`model.py`。模型主要组成是一个`Embedding`层，三个`FC`层，以及相应的分类任务的loss计算和auc计算。
 
 #### Embedding层
 首先介绍Embedding层的搭建方式：`Embedding`层的输入是`sparse_input`，shape由超参的`sparse_feature_dim`和`embedding_size`定义。需要特别解释的是`is_sparse`参数，当我们指定`is_sprase=True`后，计算图会将该参数视为稀疏参数，反向更新以及分布式通信时，都以稀疏的方式进行，会极大的提升运行效率，同时保证效果一致。
@@ -235,7 +235,7 @@ fc3 = fluid.layers.fc(
 )
 ```
 #### Loss及Auc计算
-- 预测的结果通过一个输出shape为2的FC层给出，该FC层的激活函数时softmax，会给出每条样本分属于正负样本的概率。
+- 预测的结果通过一个输出shape为2的FC层给出，该FC层的激活函数是softmax，会给出每条样本分属于正负样本的概率。
 - 每条样本的损失由交叉熵给出，交叉熵的输入维度为[batch_size,2]，数据类型为float，label的输入维度为[batch_size,1]，数据类型为int。
 - 该batch的损失`avg_cost`是各条样本的损失之和
 - 我们同时还会计算预测的auc，auc的结果由`fluid.layers.auc()`给出，该层的返回值有三个，分别是全局auc: `auc_var`，当前batch的auc: `batch_auc_var`，以及auc_states: `auc_states`，auc_states包含了`batch_stat_pos, batch_stat_neg, stat_pos, stat_neg`信息。`batch_auc`我们取近20个batch的平均，由参数`slide_steps=20`指定，roc曲线的离散化的临界数值设置为4096，由`num_thresholds=2**12`指定。

models/rank/dnn/data/get_slot_data.py

@@ -61,7 +61,7 @@ class CriteoDataset(dg.MultiSlotDataGenerator):
                 s += " dense_feature:" + str(i)
             for i in range(1, 1 + len(categorical_range_)):
                 s += " " + str(i) + ":" + str(sparse_feature[i - 1][0])
-            print s.strip()
+            print(s.strip())
             yield None
 
         return reader

models/rank/logistic_regression/data/get_slot_data.py

@@ -88,7 +88,7 @@ class Reader(dg.MultiSlotDataGenerator):
                 v = i[1]
                 for j in v:
                     s += " " + k + ":" + str(j)
-            print s.strip()
+            print(s.strip())
             yield None
 
         return data_iter

models/rank/nfm/data/get_slot_data.py

@@ -87,7 +87,7 @@ class Reader(dg.MultiSlotDataGenerator):
                 v = i[1]
                 for j in v:
                     s += " " + k + ":" + str(j)
-            print s.strip()
+            print(s.strip())
             yield None
 
         return data_iter

models/rank/wide_deep/data/get_slot_data.py

@@ -50,6 +50,7 @@ class Reader(dg.MultiSlotDataGenerator):
                 v = i[1]
                 for j in v:
                     s += " " + k + ":" + str(j)
+            print(s.strip())
             yield None
 
         return data_iter

models/rank/xdeepfm/data/get_slot_data.py

@@ -49,7 +49,7 @@ class Reader(dg.MultiSlotDataGenerator):
                 v = i[1]
                 for j in v:
                     s += " " + k + ":" + str(j)
-            print s.strip()
+            print(s.strip())
             yield None
 
         return data_iter

models/recall/gnn/evaluate_reader.py

@@ -95,7 +95,8 @@ class Reader(ReaderBase):
             (batch_size, max_uniq_len, max_uniq_len))
         mask = np.array(mask).astype("float32").reshape((batch_size, -1, 1))
         label = np.array(label).astype("int64").reshape((batch_size, 1))
-        return zip(items, seq_index, last_index, adj_in, adj_out, mask, label)
+        return list(
+            zip(items, seq_index, last_index, adj_in, adj_out, mask, label))
 
     def batch_reader(self, batch_size, batch_group_size, train=True):
         def _reader():

models/recall/gnn/reader.py

@@ -94,7 +94,8 @@ class Reader(ReaderBase):
             (batch_size, max_uniq_len, max_uniq_len))
         mask = np.array(mask).astype("float32").reshape((batch_size, -1, 1))
         label = np.array(label).astype("int64").reshape((batch_size, 1))
-        return zip(items, seq_index, last_index, adj_in, adj_out, mask, label)
+        return list(
+            zip(items, seq_index, last_index, adj_in, adj_out, mask, label))
 
     def batch_reader(self, batch_size, batch_group_size, train=True):
         def _reader():

models/recall/gru4rec/rsc15_reader.py

@@ -37,6 +37,6 @@ class Reader(ReaderBase):
             trg_seq = l[1:]
             trg_seq = [int(e) for e in trg_seq]
             feature_name = ["src_wordseq", "dst_wordseq"]
-            yield zip(feature_name, [src_seq] + [trg_seq])
+            yield list(zip(feature_name, [src_seq] + [trg_seq]))
 
         return reader

models/recall/ncf/movielens_infer_reader.py

@@ -35,7 +35,7 @@ class Reader(ReaderBase):
             features = line.strip().split(',')
 
             feature_name = ["user_input", "item_input"]
-            yield zip(feature_name,
-                      [[int(features[0])]] + [[int(features[1])]])
+            yield list(
+                zip(feature_name, [[int(features[0])]] + [[int(features[1])]]))
 
         return reader

models/recall/ncf/movielens_reader.py

@@ -35,7 +35,8 @@ class Reader(ReaderBase):
             features = line.strip().split(',')
 
             feature_name = ["user_input", "item_input", "label"]
-            yield zip(feature_name, [[int(features[0])]] +
-                      [[int(features[1])]] + [[int(features[2])]])
+            yield list(
+                zip(feature_name, [[int(features[0])]] + [[int(features[1])]] +
+                    [[int(features[2])]]))
 
         return reader

models/recall/ssr/ssr_infer_reader.py

@@ -40,9 +40,9 @@ class Reader(ReaderBase):
             src = conv_ids[:boundary]
             pos_tgt = [conv_ids[boundary]]
             feature_name = ["user", "all_item", "p_item"]
-            yield zip(
-                feature_name,
-                [src] + [np.arange(self.vocab_size).astype("int64").tolist()] +
-                [pos_tgt])
+            yield list(
+                zip(feature_name, [src] + [
+                    np.arange(self.vocab_size).astype("int64").tolist()
+                ] + [pos_tgt]))
 
         return reader

models/recall/ssr/ssr_reader.py

@@ -42,6 +42,6 @@ class Reader(ReaderBase):
             pos_tgt = [conv_ids[boundary]]
             neg_tgt = [self.sample_neg_from_seq(src)]
             feature_name = ["user", "p_item", "n_item"]
-            yield zip(feature_name, [src] + [pos_tgt] + [neg_tgt])
+            yield list(zip(feature_name, [src] + [pos_tgt] + [neg_tgt]))
 
         return reader

models/recall/youtube_dnn/random_reader.py

@@ -41,10 +41,11 @@ class Reader(ReaderBase):
             """
 
             feature_name = ["watch_vec", "search_vec", "other_feat", "label"]
-            yield zip(feature_name,
-                      [np.random.rand(self.watch_vec_size).tolist()] +
-                      [np.random.rand(self.search_vec_size).tolist()] +
-                      [np.random.rand(self.other_feat_size).tolist()] +
-                      [[np.random.randint(self.output_size)]])
+            yield list(
+                zip(feature_name, [
+                    np.random.rand(self.watch_vec_size).tolist()
+                ] + [np.random.rand(self.search_vec_size).tolist()] + [
+                    np.random.rand(self.other_feat_size).tolist()
+                ] + [[np.random.randint(self.output_size)]]))
 
         return reader