fix example code error (PaddlePaddle#2224)

doc/fluid/api_cn/layers_cn/double_buffer_cn.rst

@@ -27,13 +27,13 @@ double_buffer
 
 ..  code-block:: python
 
-  import paddle.fluid as fluid
-  reader = fluid.layers.open_files(filenames=['mnist.recordio'],
-           shapes=[[-1, 784], [-1, 1]],
-           lod_levels=[0, 0],
-           dtypes=['float32', 'int64'])
-  reader = fluid.layers.double_buffer(reader)
-  img, label = fluid.layers.read_file(reader)
+    import paddle.fluid as fluid
+    reader = fluid.layers.py_reader(capacity=64,
+                                    shapes=[(-1, 1, 28, 28), (-1, 1)],
+                                    dtypes=['float32', 'int64'],
+                                    use_double_buffer=False)
+    reader = fluid.layers.double_buffer(reader)
+    image, label = fluid.layers.read_file(reader)
 
 
 

doc/fluid/api_cn/layers_cn/py_reader_cn.rst

@@ -76,70 +76,69 @@ py_reader
 
 ..  code-block:: python
 
-  import paddle
-  import paddle.fluid as fluid
-  import paddle.dataset.mnist as mnist
-
-  def network(reader):
-      img, label = fluid.layers.read_file(reader)
-      # 用户自定义网络，此处以softmax回归为例
-      predict = fluid.layers.fc(input=img, size=10, act='softmax')
-      loss = fluid.layers.cross_entropy(input=predict, label=label)        
-      return fluid.layers.mean(loss)
-
-  # 新建 train_main_prog 和 train_startup_prog
-  train_main_prog = fluid.Program()
-  train_startup_prog = fluid.Program()
-  with fluid.program_guard(train_main_prog, train_startup_prog):
-      # 使用 fluid.unique_name.guard() 实现与test program的参数共享
-      with fluid.unique_name.guard():
-          train_reader = fluid.layers.py_reader(capacity=64, 
-                                                shapes=[(-1, 1, 28, 28), (-1, 1)], 
-                                                dtypes=['float32', 'int64'], 
+    import paddle
+    import paddle.fluid as fluid
+    import paddle.dataset.mnist as mnist
+
+    def network(reader):
+        img, label = fluid.layers.read_file(reader)
+        # 用户自定义网络，此处以softmax回归为例
+        predict = fluid.layers.fc(input=img, size=10, act='softmax')
+        loss = fluid.layers.cross_entropy(input=predict, label=label)
+        return fluid.layers.mean(loss)
+
+    # 新建 train_main_prog 和 train_startup_prog
+    train_main_prog = fluid.Program()
+    train_startup_prog = fluid.Program()
+    with fluid.program_guard(train_main_prog, train_startup_prog):
+        # 使用 fluid.unique_name.guard() 实现与test program的参数共享
+        with fluid.unique_name.guard():
+            train_reader = fluid.layers.py_reader(capacity=64,
+                                                shapes=[(-1, 1, 28, 28), (-1, 1)],
+                                                dtypes=['float32', 'int64'],
                                                 name='train_reader')
-          train_reader.decorate_paddle_reader(
-          paddle.reader.shuffle(paddle.batch(mnist.train(), 
-                                batch_size=5), 
+            train_reader.decorate_paddle_reader(
+            paddle.reader.shuffle(paddle.batch(mnist.train(),
+                                batch_size=5),
                                 buf_size=500))
-          train_loss = network(train_reader) # 一些网络定义
-          adam = fluid.optimizer.Adam(learning_rate=0.01)
-          adam.minimize(train_loss)
-
-  # Create test_main_prog and test_startup_prog
-  test_main_prog = fluid.Program()
-  test_startup_prog = fluid.Program()
-  with fluid.program_guard(test_main_prog, test_startup_prog):
-      # 使用 fluid.unique_name.guard() 实现与train program的参数共享
-      with fluid.unique_name.guard():
-      test_reader = fluid.layers.py_reader(capacity=32, 
-                                           shapes=[(-1, 1, 28, 28), (-1, 1)], 
-                                           dtypes=['float32', 'int64'], 
-                                           name='test_reader')
-      test_reader.decorate_paddle_reader(paddle.batch(mnist.test(), 512))
-    
-      test_loss = network(test_reader)
-
-  fluid.Executor(fluid.CUDAPlace(0)).run(train_startup_prog)
-  fluid.Executor(fluid.CUDAPlace(0)).run(test_startup_prog)
-
-  train_exe = fluid.ParallelExecutor(use_cuda=True,
-      loss_name=train_loss.name, main_program=train_main_prog)
-  test_exe = fluid.ParallelExecutor(use_cuda=True,
-      loss_name=test_loss.name, main_program=test_main_prog)
-  for epoch_id in range(10):
-      train_reader.start()
-      try:
-          while True:
-              train_exe.run(fetch_list=[train_loss.name])
-      except fluid.core.EOFException:
-          train_reader.reset()
-
-  test_reader.start()
-  try:
-      while True:
-          test_exe.run(fetch_list=[test_loss.name])
-  except fluid.core.EOFException:
-      test_reader.reset()
+            train_loss = network(train_reader) # 一些网络定义
+            adam = fluid.optimizer.Adam(learning_rate=0.01)
+            adam.minimize(train_loss)
+
+    # Create test_main_prog and test_startup_prog
+    test_main_prog = fluid.Program()
+    test_startup_prog = fluid.Program()
+    with fluid.program_guard(test_main_prog, test_startup_prog):
+        # 使用 fluid.unique_name.guard() 实现与train program的参数共享
+        with fluid.unique_name.guard():
+            test_reader = fluid.layers.py_reader(capacity=32,
+                                                shapes=[(-1, 1, 28, 28), (-1, 1)],
+                                                dtypes=['float32', 'int64'],
+                                                name='test_reader')
+            test_reader.decorate_paddle_reader(paddle.batch(mnist.test(), 512))
+            test_loss = network(test_reader)
+
+    fluid.Executor(fluid.CUDAPlace(0)).run(train_startup_prog)
+    fluid.Executor(fluid.CUDAPlace(0)).run(test_startup_prog)
+
+    train_exe = fluid.ParallelExecutor(use_cuda=True,
+        loss_name=train_loss.name, main_program=train_main_prog)
+    test_exe = fluid.ParallelExecutor(use_cuda=True,
+        loss_name=test_loss.name, main_program=test_main_prog)
+    for epoch_id in range(10):
+        train_reader.start()
+        try:
+            while True:
+                train_exe.run(fetch_list=[train_loss.name])
+        except fluid.core.EOFException:
+            train_reader.reset()
+
+    test_reader.start()
+    try:
+        while True:
+            test_exe.run(fetch_list=[test_loss.name])
+    except fluid.core.EOFException:
+        test_reader.reset()