Upgrade APIs for word2vec & label_semantic_roles (#2115)

* Upgrade APIs for word2vec & label_semantic_roles

* Some more fixes

doc/fluid/user_guides/nlp_case/label_semantic_roles/README.md

@@ -388,8 +388,8 @@ The data introduction section mentions the payment of the CoNLL 2005 training se
 crf_decode = fluid.layers.crf_decoding(
     input=feature_out, param_attr=fluid.ParamAttr(name='crfw'))
 
-train_data = paddle.batch(
-    paddle.reader.shuffle(
+train_data = fluid.io.batch(
+    fluid.io.shuffle(
         paddle.dataset.conll05.test(), buf_size=8192),
     batch_size=BATCH_SIZE)
 

doc/fluid/user_guides/nlp_case/label_semantic_roles/train.py

@@ -143,8 +143,8 @@ def train(use_cuda, save_dirname=None, is_local=True):
 
     # define network topology
     feature_out = db_lstm(**locals())
-    target = fluid.layers.data(
-        name='target', shape=[1], dtype='int64', lod_level=1)
+    target = fluid.data(
+        name='target', shape=[None, 1], dtype='int64', lod_level=1)
     crf_cost = fluid.layers.linear_chain_crf(
         input=feature_out,
         label=target,
@@ -165,11 +165,11 @@ def train(use_cuda, save_dirname=None, is_local=True):
         input=feature_out, param_attr=fluid.ParamAttr(name='crfw'))
 
     if args.enable_ce:
-        train_data = paddle.batch(
+        train_data = fluid.io.batch(
             paddle.dataset.conll05.test(), batch_size=BATCH_SIZE)
     else:
-        train_data = paddle.batch(
-            paddle.reader.shuffle(
+        train_data = fluid.io.batch(
+            fluid.io.shuffle(
                 paddle.dataset.conll05.test(), buf_size=8192),
             batch_size=BATCH_SIZE)
 

doc/fluid/user_guides/simple_case/word2vec/README.md

@@ -289,15 +289,15 @@ def optimizer_func():
 
 - Now we can start training. This version is much simpler than before. We have ready-made training and test sets: `paddle.dataset.imikolov.train()` and `paddle.dataset.imikolov.test()`. Both will return a reader. In PaddlePaddle, the reader is a Python function that reads the next piece of data when called each time . It is a Python generator.
 
-`paddle.batch` will read in a reader and output a batched reader. We can also output the training of each step and batch during the training process.
+`fluid.io.batch` will read in a reader and output a batched reader. We can also output the training of each step and batch during the training process.
 
 ```python
 def train(if_use_cuda, params_dirname, is_sparse=True):
     place = fluid.CUDAPlace(0) if if_use_cuda else fluid.CPUPlace()
 
-    train_reader = paddle.batch(
+    train_reader = fluid.io.batch(
         paddle.dataset.imikolov.train(word_dict, N), BATCH_SIZE)
-    test_reader = paddle.batch(
+    test_reader = fluid.io.batch(
         paddle.dataset.imikolov.test(word_dict, N), BATCH_SIZE)
 
     first_word = fluid.data(name='firstw', shape=[None, 1], dtype='int64')

doc/fluid/user_guides/simple_case/word2vec/train.py

@@ -98,9 +98,9 @@ def optimizer_func():
 def train(if_use_cuda, params_dirname, is_sparse=True):
     place = fluid.CUDAPlace(0) if if_use_cuda else fluid.CPUPlace()
 
-    train_reader = paddle.batch(
+    train_reader = fluid.io.batch(
         paddle.dataset.imikolov.train(word_dict, N), BATCH_SIZE)
-    test_reader = paddle.batch(
+    test_reader = fluid.io.batch(
         paddle.dataset.imikolov.test(word_dict, N), BATCH_SIZE)
 
     first_word = fluid.data(name='firstw', shape=[None, 1], dtype='int64')