fix some bugs

PaddleRec/ctr/wide_deep/data_preparation.py

@@ -81,11 +81,11 @@ def build_model_columns(train_data_path, test_data_path):
     train_df['label'] = train_df['income_bracket'].apply(lambda x : 1 if x == '>50K' else 0)
     test_df['label'] = test_df['income_bracket'].apply(lambda x : 1 if x == '>50K' else 0)
     
-    with io.open('train_data/columns.txt','w') as f:
+    with open('train_data/columns.txt','w') as f:
         write_str = str(len(wide_columns)) + '\n' + str(len(deep_columns)) + '\n'
         f.write(write_str)
         f.close()
-    with io.open('test_data/columns.txt','w') as f:
+    with open('test_data/columns.txt','w') as f:
         write_str = str(len(wide_columns)) + '\n' + str(len(deep_columns)) + '\n'
         f.write(write_str)
         f.close()

PaddleRec/ctr/wide_deep/infer.py

@@ -69,5 +69,7 @@ def run_infer(args,test_data_path):
 if __name__ == "__main__":
   
     args = args.parse_args()
+    logger.info("batch_size: {}, use_gpu: {}, test_epoch: {}, test_data_path: {}, model_dir:{}, hidden1_units: {}, hidden2_units: {}, hidden3_units: {}".format(
+        args.batch_size, args.use_gpu, args.test_epoch, args.test_data_path, args.model_dir, args.hidden1_units, args.hidden2_units, args.hidden3_units))
     run_infer(args, args.test_data_path)
               
\ No newline at end of file

PaddleRec/ctr/wide_deep/train.py

@@ -47,4 +47,6 @@ def train(args, train_data_path):
   
 if __name__ == "__main__":
     args = args.parse_args()
+    logger.info("epoch:{}, batch_size: {}, use_gpu: {}, train_data_path: {}, model_dir: {}, hidden1_units: {}, hidden2_units: {}, hidden3_units: {}".format(
+        args.epoch, args.batch_size, args.use_gpu, args.train_data_path, args.model_dir, args.hidden1_units, args.hidden2_units, args.hidden3_units))
     train(args, args.train_data_path)

PaddleRec/dssm/README.md

@@ -23,6 +23,16 @@ DSSM[《Learning Deep Structured Semantic Models for Web Search using Clickthrou
 
  python3.7 
 
+## 数据集说明
+
+由于论文没有公开数据集，本项目构造数据验证网络的正确性，其说明如下：
+
+query：随机构造的query向量表示
+
+doc_pos：随机构造doc正例向量表示
+
+doc_neg_0~3为四个doc负例向量表示
+
 ## 单机训练
 
 GPU环境

PaddleRec/dssm/dssm.py

@@ -57,7 +57,10 @@ def model(TRIGRAM_D = 1000, L1_N = 300, L2_N = 300, L3_N = 128, Neg = 4):
     return avg_loss, R_Q_D_p, [query] + [doc_pos] + doc_negs
     
 args = args.parse_args()
-loss,R_Q_D_p, data_list = model(args.TRIGRAM_D,args.L1_N,args.L2_N,args.L3_N,args.Neg)
+logger.info("use_gpu: {}, batch_size: {}, TRIGRAM_D: {}, L1_N:{}, L2_N: {}, L3_N: {}, Neg: {}, base_lr: {}, model_dir: {}".format(
+    args.use_gpu, args.batch_size, args.TRIGRAM_D, args.L1_N, args.L2_N, args.L3_N, args.Neg, args.base_lr, args.model_dir))
+    
+loss,R_Q_D_p, data_list = model(args.TRIGRAM_D, args.L1_N, args.L2_N, args.L3_N, args.Neg)
 
 sgd = fluid.optimizer.SGD(learning_rate=args.base_lr)
 sgd.minimize(loss)

PaddleRec/dssm/infer.py

@@ -37,4 +37,6 @@ def infer(args):
 
 if __name__ == "__main__":
     args = args.parse_args()
+    logger.info("use_gpu: {}, model_dir: {}".format(args.use_gpu, args.model_dir))
+    
     infer(args)
\ No newline at end of file

PaddleRec/multi_task/esmm/README.md

@@ -96,6 +96,8 @@ GPU环境
 ```sh
 python infer.py --use_gpu 1\  #是否使用gpu
                 --batch_size 64\  #batch_size大小
+                --cpu_num 2\  #cpu数量
+                --model_dir ./model_dir \ #模型保存路径
                 --test_data_path ./test_data \  #训练数据路径
                 --vocab_path ./vocab_size.txt #embedding词汇表大小路径
 ```
@@ -114,6 +116,7 @@ CPU环境
 python infer.py --use_gpu 0\  #是否使用gpu
                 --batch_size 64\  #batch_size大小
                 --cpu_num 2\  #cpu数量
+                --model_dir ./model_dir \ #模型保存路径
                 --test_data_path ./test_data \  #训练数据路径
                 --vocab_path ./vocab_size.txt #embedding词汇表大小路径
 ```

PaddleRec/multi_task/esmm/cpu_infer.sh

 python infer.py --use_gpu 0 \  
 			    --batch_size 64 \  
 			    --cpu_num 2 \  
+				--model_dir ./model_dir \
 			    --test_data_path ./test_data \  
 			    --vocab_path ./vocab_size.txt 
\ No newline at end of file

PaddleRec/multi_task/esmm/dataset_generator.py

@@ -9,7 +9,7 @@ all_field_id_dict = defaultdict(int)
 for i,field_id in enumerate(all_field_id):
     all_field_id_dict[field_id] = [False,i]
 
-class CriteoDataset(dg.MultiSlotStringDataGenerator):
+class Dataset(dg.MultiSlotStringDataGenerator):
    
     def generate_sample(self, line):
         
@@ -40,5 +40,5 @@ class CriteoDataset(dg.MultiSlotStringDataGenerator):
             yield output
         return reader
 
-d = CriteoDataset()
+d = Dataset()
 d.run_from_stdin()
\ No newline at end of file

PaddleRec/multi_task/esmm/gpu_infer.sh

-python infer.py --use_gpu 1\  
+CUDA_VISIBLE_DEVICES=0 python infer.py --use_gpu 1\  
 									--batch_size 64\  
+									--cpu_num 2 \
+									--model_dir ./model_dir \
 									--test_data_path ./test_data\  
 									--vocab_path ./vocab_size.txt 
\ No newline at end of file

PaddleRec/multi_task/esmm/gpu_train.sh

-CUDA_VISIBLE_DEVICES=0 python train.py --use_gpu True\  
+CUDA_VISIBLE_DEVICES=0 python train.py --use_gpu 1\  
 									   --epochs 100\  
 									   --batch_size 64\  
 									   --embed_size 12\  
 									   --cpu_num 2\  
-									   --model_dir './model_dir'\
-									   --train_data_path './train_data'\  
-									   --vocab_path './vocab/vocab_size.txt' 
\ No newline at end of file
+									   --model_dir ./model_dir\
+									   --train_data_path ./train_data\  
+									   --vocab_path ./vocab/vocab_size.txt
\ No newline at end of file

PaddleRec/multi_task/esmm/infer.py

@@ -33,7 +33,7 @@ def run_infer(args, model_path, test_data_path, vocab_size):
             inputs = esmm_model.input_data()
             avg_cost,auc_ctr,auc_ctcvr= esmm_model.net(inputs, vocab_size, args.embed_size)
             
-            dataset, file_list = utils.get_dataset(inputs, test_data_path, args.batch_size,args.cpu_num)
+            dataset, file_list = utils.get_dataset(inputs, test_data_path, args.batch_size, args.cpu_num)
             
             exe = fluid.Executor(place)
             fluid.load(fluid.default_main_program(), os.path.join(model_path, "checkpoint"), exe)
@@ -51,6 +51,9 @@ def run_infer(args, model_path, test_data_path, vocab_size):
 if __name__ == "__main__":
   
     args = args.parse_args()
+    
+    logger.info("use_gpu: {}, epochs: {}, batch_size: {}, cpu_num: {}, model_dir: {}, test_data_path: {}, vocab_path: {}".format(args.use_gpu, args.epochs, 
+        args.batch_size, args.cpu_num, args.model_dir, args.test_data_path, args.vocab_path))
     model_list = []
     for _, dir, _ in os.walk(args.model_dir):
         for model in dir:
@@ -58,10 +61,10 @@ if __name__ == "__main__":
                 path = os.path.join(args.model_dir, model)
                 model_list.append(path)
                 
-    vocab_size =utils.get_vocab_size(args.vocab_path)  
+    vocab_size = utils.get_vocab_size(args.vocab_path)  
     
     for model in model_list:
         logger.info("Test model {}".format(model))
-        run_infer(args, model,args.test_data_path)
+        run_infer(args, model,args.test_data_path, vocab_size)
                 
              
\ No newline at end of file

PaddleRec/multi_task/esmm/train.py

@@ -5,6 +5,11 @@ from net import ESMM
 import paddle
 import utils
 import args
+import logging
+
+logging.basicConfig(format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger("fluid")
+logger.setLevel(logging.INFO)
 
 def train(args, vocab_size, train_data_path):
     esmm_model = ESMM()
@@ -39,5 +44,8 @@ def train(args, vocab_size, train_data_path):
 
 if __name__ == "__main__":
     args = args.parse_args()
+    logger.info("use_gpu: {}, epochs: {}, batch_size: {}, embed_size: {}, cpu_num: {}, model_dir: {}, train_data_path: {}, vocab_path: {}".format(args.use_gpu, args.epochs, 
+        args.batch_size, args.embed_size, args.cpu_num, args.model_dir, args.train_data_path, args.vocab_path))
+    
     vocab_size =utils.get_vocab_size(args.vocab_path)
     train(args, vocab_size, args.train_data_path)

PaddleRec/multi_task/esmm/utils.py

@@ -14,7 +14,7 @@ all_field_id_dict = defaultdict(int)
 for i,field_id in enumerate(all_field_id):
     all_field_id_dict[field_id] = [False,i]
 
-def get_dataset(inputs,files,batch_size,cpu_num):
+def get_dataset(inputs, files,batch_size, cpu_num):
     dataset = fluid.DatasetFactory().create_dataset()
     dataset.set_use_var(inputs)
     dataset.set_pipe_command("python dataset_generator.py")

PaddleRec/ncf/evaluate.py

@@ -25,7 +25,7 @@ _model_path = None
 
 
 def run_infer(args, model_path, test_data_path):
-    test_data_generator = utils.CriteoDataset()
+    test_data_generator = utils.Dataset()
 
     with fluid.scope_guard(fluid.Scope()):
         test_reader = fluid.io.batch(

PaddleRec/ncf/get_train_data.py

@@ -32,7 +32,7 @@ def get_train_data(filename, write_file, num_negatives):
         file = open(write_file, 'w') 
         print("writing " + write_file)
         
-        for (u, i) in mat.keys():
+        for (u, i) in mat:
             # positive instance
             user_input = str(u)
             item_input = str(i)

PaddleRec/rerank/listwise/README.md

@@ -27,6 +27,20 @@
 
  python3.7 
 
+## 数据集说明
+
+本项目构造数据集验证模型的正确性，字段说明如下：
+
+user_slot_name：用户端特征群id
+
+item_slot_name：item段特征群id
+
+lenght：item的长度
+
+label：用户对给定的是否点击item的list
+
+注意：由于构造数据集的限制，本项目只用一个epoch，如果多个epoch，则多个epoch的数据是变化的，没有意义，因此只采用一个epoch。
+
 ## 单机训练
 
 GPU环境

PaddleRec/rerank/listwise/args.py

@@ -22,7 +22,7 @@ import sys
 
 def parse_args():
     parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument("--epochs", type=int, default=20, help="epochs")
+    parser.add_argument("--epochs", type=int, default=1, help="epochs")
     parser.add_argument("--batch_size", type=int, default=32, help="batch_size")
     parser.add_argument("--test_epoch", type=int, default=1, help="test_epoch")
     parser.add_argument('--use_gpu', type=int, default=0, help='whether using gpu')

PaddleRec/rerank/listwise/train.py

@@ -51,7 +51,7 @@ def train(args):
     train_reader = fluid.io.batch(train_data_generator.get_train_data(), batch_size=args.batch_size)
     loader = fluid.io.DataLoader.from_generator(feed_list=inputs, capacity=args.batch_size, iterable=True)
     loader.set_sample_list_generator(train_reader, places=place)
-
+    for epoch in range(args.epochs):
         for i in range(args.sample_size):
             for batch_id, data in enumerate(loader()):
                 begin = time.time()
@@ -60,8 +60,8 @@ def train(args):
                         fetch_list=[loss.name, auc_val],
                         return_numpy=True)
                 end = time.time()
-            logger.info("batch_id: {}, batch_time: {:.5f}s, loss: {:.5f}, auc: {:.5f}".format(
-                batch_id, end-begin, float(np.array(loss_val)), float(np.array(auc))))
+                logger.info("epoch: {}, batch_id: {}, batch_time: {:.5f}s, loss: {:.5f}, auc: {:.5f}".format(
+                    epoch, batch_id, end-begin, float(np.array(loss_val)), float(np.array(auc))))
         
     #save model
     model_dir = os.path.join(args.model_dir, 'epoch_' + str(1), "checkpoint")

PaddleRec/youtube_dnn/README.md

@@ -34,6 +34,16 @@
 
  python3.7 
 
+## 数据集说明
+
+由于原论文没有开源数据集，本项目随机构造数据，其字段如下：
+
+watch_vecs：随机构造用户历史观看视频的embedding表示
+
+search_vec：随机构造用户搜索历史的embedding表示
+
+other_feat：随机构造其他特征的tembedding表示
+
 ## 单机训练
 
 GPU环境

PaddleRec/youtube_dnn/infer.py

@@ -48,6 +48,9 @@ def infer(args):
 
 if __name__ == "__main__":
     args = args.parse_args()
+    logger.info("use_gpu: {}, test_epoch: {}, model_dir: {}, user_vec_path: {}".format(
+        args.use_gpu, args.test_epoch, args.model_dir, args.user_vec_path))
+    
     if(os.path.exists(args.user_vec_path)):
         os.system("rm " + args.user_vec_path)
     infer(args)
\ No newline at end of file

PaddleRec/youtube_dnn/train.py

@@ -70,6 +70,9 @@ def train(args):
 
 if __name__ == "__main__":
     args = args.parse_args()
+    logger.info("use_gpu: {}, batch_size: {}, epochs: {}, watch_vec_size: {}, search_vec_size: {}, other_feat_size: {}, output_size: {}, model_dir: {}, test_epoch: {}, base_lr: {}, video_vec_path: {}".format(
+        args.use_gpu, args.batch_size, args.epochs, args.watch_vec_size, args.search_vec_size, args.other_feat_size, args.output_size, args.model_dir, args.test_epoch, args.base_lr, args.video_vec_path))
+    
     if(os.path.exists(args.video_vec_path)):
         os.system("rm " + args.video_vec_path)
     train(args)