sync recognize to 1.8 (#2113)

LGTM

doc/fluid/user_guides/simple_case/recognize_digits/README.cn.md

@@ -365,12 +365,12 @@ def optimizer_program():
 BATCH_SIZE = 64
 
 # 每次读取训练集中的500个数据并随机打乱，传入batched reader中，batched reader 每次 yield 64个数据
-train_reader = paddle.batch(
+train_reader = fluid.io.batch(
         paddle.reader.shuffle(
             paddle.dataset.mnist.train(), buf_size=500),
         batch_size=BATCH_SIZE)
 # 读取测试集的数据，每次 yield 64个数据
-test_reader = paddle.batch(
+test_reader = fluid.io.batch(
             paddle.dataset.mnist.test(), batch_size=BATCH_SIZE)
 ```
 

doc/fluid/user_guides/simple_case/recognize_digits/README.md

@@ -343,12 +343,12 @@ Next We start the training process. `Paddle.dataset.mnist.train()` and `paddle.d
 BATCH_SIZE = 64
 
 # read 500 data in train dataset, randomly disorder them and then transfer it into batched reader which yield 64 data each time.
-train_reader = paddle.batch(
+train_reader = fluid.io.batch(
         paddle.reader.shuffle(
             paddle.dataset.mnist.train(), buf_size=500),
         batch_size=BATCH_SIZE)
 # read data in test dataset and yield 64 data every time
-test_reader = paddle.batch(
+test_reader = fluid.io.batch(
             paddle.dataset.mnist.test(), batch_size=BATCH_SIZE)
 ```
 

doc/fluid/user_guides/simple_case/recognize_digits/index.cn.html

@@ -407,12 +407,12 @@ def optimizer_program():
 BATCH_SIZE = 64
 
 # 每次读取训练集中的500个数据并随机打乱，传入batched reader中，batched reader 每次 yield 64个数据
-train_reader = paddle.batch(
+train_reader = fluid.io.batch(
         paddle.reader.shuffle(
             paddle.dataset.mnist.train(), buf_size=500),
         batch_size=BATCH_SIZE)
 # 读取测试集的数据，每次 yield 64个数据
-test_reader = paddle.batch(
+test_reader = fluid.io.batch(
             paddle.dataset.mnist.test(), batch_size=BATCH_SIZE)
 ```
 

doc/fluid/user_guides/simple_case/recognize_digits/index.html

@@ -385,12 +385,12 @@ Next We start the training process. `Paddle.dataset.mnist.train()` and `paddle.d
 BATCH_SIZE = 64
 
 # read 500 data in train dataset, randomly disorder them and then transfer it into batched reader which yield 64 data each time.
-train_reader = paddle.batch(
+train_reader = fluid.io.batch(
         paddle.reader.shuffle(
             paddle.dataset.mnist.train(), buf_size=500),
         batch_size=BATCH_SIZE)
 # read data in test dataset and yield 64 data every time
-test_reader = paddle.batch(
+test_reader = fluid.io.batch(
             paddle.dataset.mnist.test(), batch_size=BATCH_SIZE)
 ```
 

doc/fluid/user_guides/simple_case/recognize_digits/train.py

@@ -88,17 +88,17 @@ def train(nn_type,
     main_program = fluid.default_main_program()
 
     if args.enable_ce:
-        train_reader = paddle.batch(
+        train_reader = fluid.io.batch(
             paddle.dataset.mnist.train(), batch_size=BATCH_SIZE)
-        test_reader = paddle.batch(
+        test_reader = fluid.io.batch(
             paddle.dataset.mnist.test(), batch_size=BATCH_SIZE)
         startup_program.random_seed = 90
         main_program.random_seed = 90
     else:
-        train_reader = paddle.batch(
+        train_reader = fluid.io.batch(
             paddle.reader.shuffle(paddle.dataset.mnist.train(), buf_size=500),
             batch_size=BATCH_SIZE)
-        test_reader = paddle.batch(
+        test_reader = fluid.io.batch(
             paddle.dataset.mnist.test(), batch_size=BATCH_SIZE)
 
     img = fluid.data(name='img', shape=[None, 1, 28, 28], dtype='float32')