diff --git a/paddle/fluid/operators/reshape_op.cc b/paddle/fluid/operators/reshape_op.cc
index 4b1aaf584900d55269b8dbcd14960d4d499af4c5..b87b8e6b26cdeb017e700870998a53c1b295988c 100644
--- a/paddle/fluid/operators/reshape_op.cc
+++ b/paddle/fluid/operators/reshape_op.cc
@@ -49,14 +49,14 @@ Examples:
 specified by Attr(shape) is [6, 8], the reshape operator will transform Input(X)
 into a 2-D tensor with shape [6, 8] and leaving Input(X)'s data unchanged.
 
-1. Given a 3-D tensor Input(X) with a shape [2, 4, 6], and the target shape
+2. Given a 3-D tensor Input(X) with a shape [2, 4, 6], and the target shape
 specified by Attr(shape) is [2, 3, -1, 2], the reshape operator will transform
 Input(X) into a 4-D tensor with shape [2, 3, 4, 2] and leaving Input(X)'s data
 unchanged. In this case, one and only dimension of Attr(shape) can be set to -1,
 the value of this dimension is inferred from the total element number of
 Input(X) and remaining dimensions.
 
-1. Given a 3-D tensor Input(X) with a shape [2, 4, 6], and the target shape
+3. Given a 3-D tensor Input(X) with a shape [2, 4, 6], and the target shape
 specified by Attr(shape) is [-1, 0, 3, 2], the reshape operator will transform
 Input(X) into a 4-D tensor with shape [2, 4, 3, 2] and leaving Input(X)'s data
 unchanged. In this case, besides -1, 0 means the actual dimension value is going
@@ -67,11 +67,13 @@ Note:
 1. One and only one dimension in Attr(shape) can be set -1. In this case,
 the actual dimension value will be infered from the total element number of
 Input(X) and remaining dimensions.
-1. More than one dimensions in Attr(shape) can be set to 0, which means the real
+
+2. More than one dimensions in Attr(shape) can be set to 0, which means the real
 dimension value will be copied from Input(X) at runtime. Note that the index of
 0 can not exceed Rank(X). For example, Input(X) is a 3-D tensor with shape
 [2, 3, 4], Attr(shape) = [2, 3, 2, 0] is an invalid input.
-1. Input(Shape) has a higher priority than Attr(shape) if it is provided, while
+
+3. Input(Shape) has a higher priority than Attr(shape) if it is provided, while
 Attr(shape) still should be set correctly to gurantee shape inference in 
 compile-time.
 
diff --git a/paddle/fluid/operators/reshape_op.h b/paddle/fluid/operators/reshape_op.h
index 3a9a769229ae6030945471fe79a935b98d797e98..871b4d38d56f10f3c0c178caa566508ab75f316c 100644
--- a/paddle/fluid/operators/reshape_op.h
+++ b/paddle/fluid/operators/reshape_op.h
@@ -66,7 +66,6 @@ class ReshapeOp : public framework::OperatorWithKernel {
     int64_t capacity = 1;
     int unk_dim_idx = -1;
     for (size_t i = 0; i < shape.size(); ++i) {
-      // std::cout<< shape[i] << "haha";
       if (shape[i] == unk_dim_val) {
         PADDLE_ENFORCE(
             unk_dim_idx == -1,
diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py
index c2d32954b5518a4f5f70a91a0da9cf71837643a5..ed82fa8940e69be834fa39c9ec523bb1419e04c5 100644
--- a/python/paddle/fluid/layers/nn.py
+++ b/python/paddle/fluid/layers/nn.py
@@ -3337,7 +3337,7 @@ def reshape(x, shape, actual_shape=None, act=None, inplace=True, name=None):
     number of x and remaining dimensions. Thus one and only one dimension can
     be set -1.
 
-    1. 0 means the actual dimension value is going to be copied from the
+    2. 0 means the actual dimension value is going to be copied from the
     corresponding dimension of x. The indice of 0s in shape can not exceed
     Rank(X).
 
@@ -3347,14 +3347,14 @@ def reshape(x, shape, actual_shape=None, act=None, inplace=True, name=None):
     is [6, 8], the reshape operator will transform x into a 2-D tensor with 
     shape [6, 8] and leaving x's data unchanged.
 
-    1. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape
+    2. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape
     specified is [2, 3, -1, 2], the reshape operator will transform x into a
     4-D tensor with shape [2, 3, 4, 2] and leaving x's data unchanged. In this
     case, one dimension of the target shape is set to -1, the value of this 
     dimension is inferred from the total element number of x and remaining 
     dimensions.
 
-    1. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape
+    3. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape
     is [-1, 0, 3, 2], the reshape operator will transform x into a 4-D tensor
     with shape [2, 4, 3, 2] and leaving x's data unchanged. In this case,
     besides -1, 0 means the actual dimension value is going to be copied from