diff --git a/doc/faq/local/index_cn.rst b/doc/faq/local/index_cn.rst
index 75c4ba028e497e29e9030a86514348726d9c0a80..0e939a2671ace8682c90cdc1c1bb2da1dda0d568 100644
--- a/doc/faq/local/index_cn.rst
+++ b/doc/faq/local/index_cn.rst
@@ -174,7 +174,7 @@ decoder_inputs = paddle.layer.fc(
 1. 两者都是对梯度的截断，但截断时机不同，前者在 :code:`optimzier` 更新网络参数时应用；后者在激活函数反向计算时被调用；
 2. 截断对象不同：前者截断可学习参数的梯度，后者截断回传给前层的梯度;
 
-除此之外，还可以通过减小学习律或者对数据进行归一化处理来解决这类问题。
+除此之外，还可以通过减小学习率或者对数据进行归一化处理来解决这类问题。
 
 5.  如何调用 infer 接口输出多个layer的预测结果
 -----------------------------------------------
diff --git a/paddle/gserver/tests/test_PyDataProvider2.py b/paddle/gserver/tests/test_PyDataProvider2.py
index 2e6225519f4681238f4b40fb33764ead4a16b24a..0d0fe476ff5eac8bf8ad1c9fe09b32c1a8f73ebc 100644
--- a/paddle/gserver/tests/test_PyDataProvider2.py
+++ b/paddle/gserver/tests/test_PyDataProvider2.py
@@ -51,7 +51,10 @@ def test_sparse_non_value_no_seq(setting, filename):
         yield [(i + 1) * (j + 1) for j in xrange(10)]
 
 
-@provider(input_types=[sparse_vector(30000, seq_type=SequenceType.NO_SEQUENCE)])
+@provider(input_types=[
+    sparse_float_vector(
+        30000, seq_type=SequenceType.NO_SEQUENCE)
+])
 def test_sparse_value_no_seq(setting, filename):
     for i in xrange(200):
         yield [((i + 1) * (j + 1), float(j) / float(i + 1)) for j in xrange(10)]
diff --git a/python/paddle/trainer/PyDataProvider2.py b/python/paddle/trainer/PyDataProvider2.py
index 248da4ae8d1fb24652625ae8fc9ef314a028b912..05635833bf1645f78f5ba15caee3e9b8da9f5544 100644
--- a/python/paddle/trainer/PyDataProvider2.py
+++ b/python/paddle/trainer/PyDataProvider2.py
@@ -175,7 +175,7 @@ def index_slot(value_range, seq_type=SequenceType.NO_SEQUENCE):
 
 dense_vector = dense_slot
 sparse_binary_vector = sparse_non_value_slot
-sparse_vector = sparse_value_slot
+sparse_float_vector = sparse_value_slot
 integer_value = index_slot
 
 # dense_array can be used for variable-length input feature.
@@ -216,7 +216,7 @@ def sparse_binary_vector_sub_sequence(dim):
     return sparse_binary_vector(dim, seq_type=SequenceType.SUB_SEQUENCE)
 
 
-def sparse_vector_sequence(dim):
+def sparse_float_vector_sequence(dim):
     """
     Data type of a sequence of sparse vector, which most elements are zero,
     others could be any float value.
@@ -226,11 +226,11 @@ def sparse_vector_sequence(dim):
     :return: An input type object
     :rtype: InputType
     """
-    return sparse_vector(dim, seq_type=SequenceType.SEQUENCE)
+    return sparse_float_vector(dim, seq_type=SequenceType.SEQUENCE)
 
 
-def sparse_vector_sub_sequence(dim):
-    return sparse_vector(dim, seq_type=SequenceType.SUB_SEQUENCE)
+def sparse_float_vector_sub_sequence(dim):
+    return sparse_float_vector(dim, seq_type=SequenceType.SUB_SEQUENCE)
 
 
 def integer_value_sequence(value_range):
diff --git a/python/paddle/v2/tests/test_data_feeder.py b/python/paddle/v2/tests/test_data_feeder.py
index 83da678da387ed1c86868847f140c6c09fbec3b5..63905c04cf737d0f1d226a4a5a27777351dbf5a3 100644
--- a/python/paddle/v2/tests/test_data_feeder.py
+++ b/python/paddle/v2/tests/test_data_feeder.py
@@ -97,7 +97,7 @@ class DataFeederTest(unittest.TestCase):
             each_sample.append(zip(a, b))
             data.append(each_sample)
 
-        feeder = DataFeeder([('input', data_type.sparse_vector(dim))],
+        feeder = DataFeeder([('input', data_type.sparse_float_vector(dim))],
                             {'input': 0})
         arg = feeder(data)
         output = arg.getSlotValue(0)