remove w,b input params in train.py and remove def main in README.md

fit_a_line/README.md

@@ -114,9 +114,8 @@ fit_a_line下trainer.py演示了训练的整体过程
 ### 首先初始化paddle  
 
 ```python
-def main():
-    # init
-    paddle.init(use_gpu=False, trainer_count=1)
+# init
+paddle.init(use_gpu=False, trainer_count=1)
 ```
 
 ### 然后进行模型配置  
@@ -124,30 +123,28 @@ def main():
 使用`fc_layer`和`LinearActivation`来表示线性回归的模型本身。  
 
 ```python
-    #输入数据，13维的房屋信息
-    x = paddle.layer.data(name='x', type=paddle.data_type.dense_vector(13))
-    y_predict = paddle.layer.fc(input=x,
-                                param_attr=paddle.attr.Param(name='w'),
+#输入数据，13维的房屋信息
+x = paddle.layer.data(name='x', type=paddle.data_type.dense_vector(13))
+y_predict = paddle.layer.fc(input=x,
                                 size=1,
-                                act=paddle.activation.Linear(),
-                                bias_attr=paddle.attr.Param(name='b'))
-    y = paddle.layer.data(name='y', type=paddle.data_type.dense_vector(1))
-    cost = paddle.layer.regression_cost(input=y_predict, label=y)
+                                act=paddle.activation.Linear())
+y = paddle.layer.data(name='y', type=paddle.data_type.dense_vector(1))
+cost = paddle.layer.regression_cost(input=y_predict, label=y)
 ```
 ### 接着创建参数和优化器  
 
 ```python
-    # create parameters
-    parameters = paddle.parameters.create(cost)
+# create parameters
+parameters = paddle.parameters.create(cost)
 
-    # create optimizer
-    optimizer = paddle.optimizer.Momentum(momentum=0)
+# create optimizer
+optimizer = paddle.optimizer.Momentum(momentum=0)
 ```
 
 ### 创建trainer  
 
 ```python
-    trainer = paddle.trainer.SGD(cost=cost,
+trainer = paddle.trainer.SGD(cost=cost,
                              parameters=parameters,
                              update_equation=optimizer)
 ```
@@ -157,11 +154,11 @@ def main():
 reader_dict中设置了训练数据和测试数据的下标,reader通过下标区分训练和测试数据。
 
 ```python
-    reader_dict={'x': 0,
+reader_dict={'x': 0,
              'y': 1}
 
-    # event_handler to print training and testing info
-    def event_handler(event):
+# event_handler to print training and testing info
+def event_handler(event):
     if isinstance(event, paddle.event.EndIteration):
         if event.batch_id % 100 == 0:
             print "Pass %d, Batch %d, Cost %f" % (
@@ -177,8 +174,8 @@ reader_dict中设置了训练数据和测试数据的下标,reader通过下标
 ### 开始训练  
 
 ```python
-    # training
-    trainer.train(
+# training
+trainer.train(
     reader=paddle.reader.batched(
         paddle.reader.shuffle(
             uci_housing.train(), buf_size=500),
@@ -188,7 +185,7 @@ reader_dict中设置了训练数据和测试数据的下标,reader通过下标
     num_passes=30)
 ```
 
-## 执行训练程序  
+## bash中执行训练程序  
 **注意设置好paddle的安装包路径**
 
 ```bash

fit_a_line/train.py

@@ -9,10 +9,8 @@ def main():
     # network config
     x = paddle.layer.data(name='x', type=paddle.data_type.dense_vector(13))
     y_predict = paddle.layer.fc(input=x,
-                                param_attr=paddle.attr.Param(name='w'),
                                 size=1,
-                                act=paddle.activation.Linear(),
-                                bias_attr=paddle.attr.Param(name='b'))
+                                act=paddle.activation.Linear())
     y = paddle.layer.data(name='y', type=paddle.data_type.dense_vector(1))
     cost = paddle.layer.regression_cost(input=y_predict, label=y)