Update doc for deepfm

deep_fm/README.md

-# Deep Factorization Machines (DeepFM) for Click-Through Rate prediction
+# Deep Factorization Machine for Click-Through Rate prediction
 
 ## Introduction
 This model implements the DeepFM proposed in the following paper:
 
 ```text
-    Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li and Xiuqiang He. DeepFM:
-    A Factorization-Machine based Neural Network for CTR Prediction.
-    Proceedings of the Twenty-Sixth International Joint Conference on
-    Artificial Intelligence (IJCAI-17), 2017
+Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li and Xiuqiang He. DeepFM:
+A Factorization-Machine based Neural Network for CTR Prediction. Proceedings
+of the Twenty-Sixth International Joint Conference on Artificial Intelligence
+(IJCAI-17), 2017
 ```
 
-The DeepFm combines factorization machines and deep neural networks to model
+The DeepFm combines factorization machine and deep neural networks to model
 both low order and high order feature interactions. For details of the
 factorization machines, please refer to the paper [factorization
 machines](https://www.csie.ntu.edu.tw/~b97053/paper/Rendle2010FM.pdf)
@@ -48,7 +48,7 @@ def fm_layer(input, factor_size):
     second_order = paddle.layer.factorization_machine(input=input, factor_size=factor_size)
     fm = paddle.layer.addto(input=[first_order, second_order],
                             act=paddle.activation.Linear(),
-                            ias_attr=False)
+                            bias_attr=False)
     return fm
 ```
 
@@ -73,8 +73,6 @@ python train.py \
         2>&1 | train.log
 ```
 
-## Evaluate
-
 After training pass 9 batch 40000, the testing AUC is `0.807178` and the testing
 cost is `0.445196`.
 

deep_fm/infer.py

@@ -15,22 +15,22 @@ def parse_args():
         '--model_gz_path',
         type=str,
         required=True,
-        help="path of model parameters gz file")
+        help="The path of model parameters gz file")
     parser.add_argument(
         '--data_path',
         type=str,
         required=True,
-        help="path of the dataset to infer")
+        help="The path of the dataset to infer")
     parser.add_argument(
         '--prediction_output_path',
         type=str,
         required=True,
-        help="path to output the prediction")
+        help="The path to output the prediction")
     parser.add_argument(
         '--factor_size',
         type=int,
         default=10,
-        help="the factor size for the factorization machine (default:10)")
+        help="The factor size for the factorization machine (default:10)")
 
     return parser.parse_args()
 

deep_fm/train.py

@@ -19,32 +19,32 @@ def parse_args():
         '--train_data_path',
         type=str,
         required=True,
-        help="path of training dataset")
+        help="The path of training dataset")
     parser.add_argument(
         '--test_data_path',
         type=str,
         required=True,
-        help="path of testing dataset")
+        help="The path of testing dataset")
     parser.add_argument(
         '--batch_size',
         type=int,
         default=1000,
-        help="size of mini-batch (default:1000)")
+        help="The size of mini-batch (default:1000)")
     parser.add_argument(
         '--num_passes',
         type=int,
         default=10,
-        help="number of passes to train (default: 10)")
+        help="The number of passes to train (default: 10)")
     parser.add_argument(
         '--factor_size',
         type=int,
         default=10,
-        help="the factor size for the factorization machine (default:10)")
+        help="The factor size for the factorization machine (default:10)")
     parser.add_argument(
         '--model_output_dir',
         type=str,
         default='models',
-        help='path for model to store (default: models)')
+        help='The path for model to store (default: models)')
 
     return parser.parse_args()