Rewrite the content of PyReader_cn.rst develop=test (#1396)

* fix PyReader develop=test

* rewrite PyReader_cn.rst develop=test

doc/fluid/api_cn/io_cn/PyReader_cn.rst

@@ -23,7 +23,7 @@ PyReader
 
 **代码示例**
 
-1.如果iterable=False，则创建的PyReader对象几乎与 ``fluid.layers.py_reader（）`` 相同。算子将被插入program中。用户应该在每个epoch之前调用start（），并在epoch结束时捕获 ``Executor.run（）`` 抛出的 ``fluid.core.EOFException `` 。一旦捕获到异常，用户应该调用reset（）手动重置reader。
+1.如果iterable=False，则创建的PyReader对象几乎与 ``fluid.layers.py_reader（）`` 相同。算子将被插入program中。用户应该在每个epoch之前调用 ``start（）`` ，并在epoch结束时捕获 ``Executor.run（）`` 抛出的 ``fluid.core.EOFException`` 。一旦捕获到异常，用户应该调用 ``reset（）`` 手动重置reader。
 
 .. code-block:: python
 
@@ -35,6 +35,11 @@ PyReader
     ITER_NUM = 5
     BATCH_SIZE = 3
     
+    def network(image, label):
+        # 用户定义网络，此处以softmax回归为例
+        predict = fluid.layers.fc(input=image, size=10, act='softmax')
+        return fluid.layers.cross_entropy(input=predict, label=label) 
+
     def reader_creator_random_image_and_label(height, width):
         def reader():
             for i in range(ITER_NUM):
@@ -55,8 +60,9 @@ PyReader
     user_defined_reader = reader_creator_random_image_and_label(784, 784)
     reader.decorate_sample_list_generator(
         paddle.batch(user_defined_reader, batch_size=BATCH_SIZE))
-    # 此处省略网络定义
-    executor = fluid.Executor(fluid.CUDAPlace(0))
+    
+    loss = network(image, label)
+    executor = fluid.Executor(fluid.CPUPlace())
     executor.run(fluid.default_startup_program())
     for i in range(EPOCH_NUM):
         reader.start()
@@ -80,26 +86,34 @@ PyReader
    ITER_NUM = 5
    BATCH_SIZE = 10
 
+   def network(image, label):
+        # 用户定义网络，此处以softmax回归为例
+        predict = fluid.layers.fc(input=image, size=10, act='softmax')
+        return fluid.layers.cross_entropy(input=predict, label=label)   
+
    def reader_creator_random_image(height, width):
        def reader():
            for i in range(ITER_NUM):
-               yield np.random.uniform(low=0, high=255, size=[height, width]),
+               fake_image = np.random.uniform(low=0, high=255, size=[height, width]),
+               fake_label = np.ones([1])
+               yield fake_image, fake_label
        return reader
 
    image = fluid.layers.data(name='image', shape=[784, 784], dtype='float32')
-   reader = fluid.io.PyReader(feed_list=[image], capacity=4, iterable=True, return_list=False)
+   label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+   reader = fluid.io.PyReader(feed_list=[image, label], capacity=4, iterable=True, return_list=False)
 
    user_defined_reader = reader_creator_random_image(784, 784)
    reader.decorate_sample_list_generator(
        paddle.batch(user_defined_reader, batch_size=BATCH_SIZE),
-       fluid.core.CUDAPlace(0))
-   # 此处省略网络定义
-   executor = fluid.Executor(fluid.CUDAPlace(0))
-   executor.run(fluid.default_main_program())
+       fluid.core.CPUPlace())
+   loss = network(image, label)
+   executor = fluid.Executor(fluid.CPUPlace())
+   executor.run(fluid.default_startup_program())
 
    for _ in range(EPOCH_NUM):
        for data in reader():
-           executor.run(feed=data)
+           executor.run(feed=data, fetch_list=[loss])
 
 3. return_list=True，返回值将用list表示而非dict，通常用于动态图模式中。
 
@@ -153,7 +167,7 @@ PyReader
   reader.decorate_sample_list_generator(
     paddle.batch(generator, batch_size=BATCH_SIZE))
      
-  executor = fluid.Executor(fluid.CUDAPlace(0))
+  executor = fluid.Executor(fluid.CPUPlace())
   executor.run(fluid.default_startup_program())
   for i in range(3):
     reader.start()
@@ -187,7 +201,7 @@ PyReader
             reader.decorate_sample_list_generator(
                 paddle.batch(generator, batch_size=BATCH_SIZE))
             
-            executor = fluid.Executor(fluid.CUDAPlace(0))
+            executor = fluid.Executor(fluid.CPUPlace())
             executor.run(fluid.default_startup_program())
             for i in range(3):
                 reader.start()
@@ -225,6 +239,11 @@ PyReader
             ITER_NUM = 15
             BATCH_SIZE = 3
             
+            def network(image, label):
+                # 用户定义网络，此处以softmax回归为例
+                predict = fluid.layers.fc(input=image, size=10, act='softmax')
+                return fluid.layers.cross_entropy(input=predict, label=label)    
+            
             def random_image_and_label_generator(height, width):
                 def generator():
                     for i in range(ITER_NUM):
@@ -236,20 +255,20 @@ PyReader
                 return generator
             
             image = fluid.layers.data(name='image', shape=[784, 784], dtype='float32')
-            label = fluid.layers.data(name='label', shape=[1], dtype='int32')
+            label = fluid.layers.data(name='label', shape=[1], dtype='int64')
             reader = fluid.io.PyReader(feed_list=[image, label], capacity=4, iterable=True)
             
             user_defined_generator = random_image_and_label_generator(784, 784)
             reader.decorate_sample_generator(user_defined_generator,
                                              batch_size=BATCH_SIZE,
-                                             places=[fluid.CUDAPlace(0)])
-            # 省略了网络的定义
-            executor = fluid.Executor(fluid.CUDAPlace(0))
-            executor.run(fluid.default_main_program())
+                                             places=[fluid.CPUPlace()])
+            loss = network(image, label)
+            executor = fluid.Executor(fluid.CPUPlace())
+            executor.run(fluid.default_startup_program())
      
             for _ in range(EPOCH_NUM):
                 for data in reader():
-                    executor.run(feed=data)
+                    executor.run(feed=data, fetch_list=[loss])
 
 .. py:method:: decorate_sample_list_generator(reader, places=None)
 
@@ -275,6 +294,11 @@ PyReader
             ITER_NUM = 15
             BATCH_SIZE = 3
             
+            def network(image, label):
+                # 用户定义网络，此处以softmax回归为例
+                predict = fluid.layers.fc(input=image, size=10, act='softmax')
+                return fluid.layers.cross_entropy(input=predict, label=label)
+               
             def random_image_and_label_generator(height, width):
                 def generator():
                     for i in range(ITER_NUM):
@@ -286,20 +310,20 @@ PyReader
                 return generator
             
             image = fluid.layers.data(name='image', shape=[784, 784], dtype='float32')
-            label = fluid.layers.data(name='label', shape=[1], dtype='int32')
+            label = fluid.layers.data(name='label', shape=[1], dtype='int64')
             reader = fluid.io.PyReader(feed_list=[image, label], capacity=4, iterable=True)
             
             user_defined_generator = random_image_and_label_generator(784, 784)
             reader.decorate_sample_list_generator(
                 paddle.batch(user_defined_generator, batch_size=BATCH_SIZE),
-                fluid.core.CUDAPlace(0))
-            # 省略了网络的定义
-            executor = fluid.Executor(fluid.core.CUDAPlace(0))
-            executor.run(fluid.default_main_program())
+                fluid.core.CPUPlace())
+            loss = network(image, label)
+            executor = fluid.Executor(fluid.core.CPUPlace())
+            executor.run(fluid.default_startup_program())
      
             for _ in range(EPOCH_NUM):
                 for data in reader():
-                    executor.run(feed=data)
+                    executor.run(feed=data, fetch_list=[loss])
 
 .. py:method:: decorate_batch_generator(reader, places=None)
 
@@ -324,6 +348,11 @@ PyReader
             ITER_NUM = 15
             BATCH_SIZE = 3
             
+            def network(image, label):
+                # 用户定义网络，此处以softmax回归为例
+                predict = fluid.layers.fc(input=image, size=10, act='softmax')
+                return fluid.layers.cross_entropy(input=predict, label=label)
+            
             def random_image_and_label_generator(height, width):
                 def generator():
                     for i in range(ITER_NUM):
@@ -331,22 +360,25 @@ PyReader
                                                         high=255,
                                                         size=[BATCH_SIZE, height, width])
                         batch_label = np.ones([BATCH_SIZE, 1])
+                        batch_image = batch_image.astype('float32')
+                        batch_label = batch_label.astype('int64')
                         yield batch_image, batch_label
                 return generator
             
             image = fluid.layers.data(name='image', shape=[784, 784], dtype='float32')
-            label = fluid.layers.data(name='label', shape=[1], dtype='int32')
+            label = fluid.layers.data(name='label', shape=[1], dtype='int64')
             reader = fluid.io.PyReader(feed_list=[image, label], capacity=4, iterable=True)
             
             user_defined_generator = random_image_and_label_generator(784, 784)
-            reader.decorate_batch_generator(user_defined_generator, fluid.CUDAPlace(0))
-            # 省略了网络的定义
-            executor = fluid.Executor(fluid.CUDAPlace(0))
-            executor.run(fluid.default_main_program())
+            reader.decorate_batch_generator(user_defined_generator, fluid.CPUPlace())
+            
+            loss = network(image, label)
+            executor = fluid.Executor(fluid.CPUPlace())
+            executor.run(fluid.default_startup_program())
      
             for _ in range(EPOCH_NUM):
                 for data in reader():
-                    executor.run(feed=data)
+                    executor.run(feed=data, fetch_list=[loss])
 
 
 .. py:method:: next()