fix cn doc test=develop (#1490)

doc/fluid/api_cn/layers_cn/equal_cn.rst

@@ -23,13 +23,13 @@ equal
     import paddle.fluid as fluid
     import numpy as np
     
-    out_cond =fluid.layers.data(name="input1", shape=[2], dtype='bool', append_batch_size=False)
+    out_cond =fluid.data(name="input1", shape=[2], dtype='bool')
     label = fluid.layers.assign(np.array([3, 3], dtype="int32"))
     limit = fluid.layers.assign(np.array([3, 2], dtype="int32"))
     label_cond = fluid.layers.assign(np.array([1, 2], dtype="int32"))
     
     out1 = fluid.layers.equal(x=label,y=limit) #out1=[True, False]
-    out2 = fluid.layers.equal(x=label_cond,y=limit, out=out_cond) #out2=[False, True] out_cond=[False, True]
+    out2 = fluid.layers.equal(x=label_cond,y=limit, cond=out_cond) #out2=[False, True] out_cond=[False, True]
     
 
 

doc/fluid/api_cn/layers_cn/reduce_max_cn.rst

@@ -26,7 +26,7 @@ reduce_max
       #    [[0.2, 0.3, 0.5, 0.9]
       #     [0.1, 0.2, 0.6, 0.7]]
       # 接下来的示例中，我们在每处函数调用后面都标注出了它的结果张量。
-      x = fluid.layers.data(name='x', shape=[2, 4], dtype='float32')
+      x = fluid.data(name='x', shape=[2, 4], dtype='float32')
       fluid.layers.reduce_max(x)  # [0.9]
       fluid.layers.reduce_max(x, dim=0)  # [0.2, 0.3, 0.6, 0.9]
       fluid.layers.reduce_max(x, dim=-1)  # [0.9, 0.7]
@@ -36,7 +36,7 @@ reduce_max
       #      [[[1.0, 2.0], [3.0, 4.0]],
       #      [[5.0, 6.0], [7.0, 8.0]]]
       # 接下来的示例中，我们在每处函数调用后面都标注出了它的结果张量。
-      y = fluid.layers.data(name='y', shape=[2, 2, 2], dtype='float32')
+      y = fluid.data(name='y', shape=[2, 2, 2], dtype='float32')
       fluid.layers.reduce_max(y, dim=[1, 2]) # [4.0, 8.0]
       fluid.layers.reduce_max(y, dim=[0, 1]) # [7.0, 8.0]
 

doc/fluid/api_cn/layers_cn/reduce_mean_cn.rst

@@ -26,7 +26,7 @@ reduce_mean
       #    [[0.2, 0.3, 0.5, 0.9]
       #     [0.1, 0.2, 0.6, 0.7]]
       # 接下来的示例中，我们在每处函数调用后面都标注出了它的结果张量。
-      x = fluid.layers.data(name='x', shape=[2, 4], dtype='float32')
+      x = fluid.data(name='x', shape=[2, 4], dtype='float32')
       fluid.layers.reduce_mean(x)  # [0.4375]
       fluid.layers.reduce_mean(x, dim=0)  # [0.15, 0.25, 0.55, 0.8]
       fluid.layers.reduce_mean(x, dim=-1)  # [0.475, 0.4]
@@ -36,7 +36,7 @@ reduce_mean
       #      [[[1.0, 2.0], [3.0, 4.0]],
       #      [[5.0, 6.0], [7.0, 8.0]]]
       # 接下来的示例中，我们在每处函数调用后面都标注出了它的结果张量。。
-      y = fluid.layers.data(name='y', shape=[2, 2, 2], dtype='float32')
+      y = fluid.data(name='y', shape=[2, 2, 2], dtype='float32')
       fluid.layers.reduce_mean(y, dim=[1, 2]) # [2.5, 6.5]
       fluid.layers.reduce_mean(y, dim=[0, 1]) # [4.0, 5.0]
 

doc/fluid/api_cn/layers_cn/reduce_min_cn.rst

@@ -26,7 +26,7 @@ reduce_min
       #    [[0.2, 0.3, 0.5, 0.9]
       #     [0.1, 0.2, 0.6, 0.7]]
       # 接下来的示例中，我们在每处函数调用后面都标注出了它的结果张量。
-      x = fluid.layers.data(name='x', shape=[2, 4], dtype='float32')
+      x = fluid.data(name='x', shape=[2, 4], dtype='float32')
       fluid.layers.reduce_min(x)  # [0.1]
       fluid.layers.reduce_min(x, dim=0)  # [0.1, 0.2, 0.5, 0.7]
       fluid.layers.reduce_min(x, dim=-1)  # [0.2, 0.1]
@@ -36,7 +36,7 @@ reduce_min
       #      [[[1.0, 2.0], [3.0, 4.0]],
       #      [[5.0, 6.0], [7.0, 8.0]]]
       # 接下来的示例中，我们在每处函数调用后面都标注出了它的结果张量。
-      y = fluid.layers.data(name='y', shape=[2, 2, 2], dtype='float32')
+      y = fluid.data(name='y', shape=[2, 2, 2], dtype='float32')
       fluid.layers.reduce_min(y, dim=[1, 2]) # [1.0, 5.0]
       fluid.layers.reduce_min(y, dim=[0, 1]) # [1.0, 2.0]
 

doc/fluid/api_cn/layers_cn/reduce_prod_cn.rst

@@ -26,7 +26,7 @@ reduce_prod
       #    [[0.2, 0.3, 0.5, 0.9]
       #     [0.1, 0.2, 0.6, 0.7]]
       # 接下来的示例中，我们在每处函数调用后面都标注出了它的结果张量。
-      x = fluid.layers.data(name='x', shape=[2, 4], dtype='float32')
+      x = fluid.data(name='x', shape=[2, 4], dtype='float32')
       fluid.layers.reduce_prod(x)  # [0.0002268]
       fluid.layers.reduce_prod(x, dim=0)  # [0.02, 0.06, 0.3, 0.63]
       fluid.layers.reduce_prod(x, dim=-1)  # [0.027, 0.0084]
@@ -37,7 +37,7 @@ reduce_prod
       #      [[[1.0, 2.0], [3.0, 4.0]],
       #      [[5.0, 6.0], [7.0, 8.0]]]
       # 接下来的示例中，我们在每处函数调用后面都标注出了它的结果张量。
-      y = fluid.layers.data(name='y', shape=[2, 2, 2], dtype='float32')
+      y = fluid.data(name='y', shape=[2, 2, 2], dtype='float32')
       fluid.layers.reduce_prod(y, dim=[1, 2]) # [24.0, 1680.0]
       fluid.layers.reduce_prod(y, dim=[0, 1]) # [105.0, 384.0]
 

doc/fluid/api_cn/layers_cn/reduce_sum_cn.rst

@@ -26,7 +26,7 @@ reduce_sum
       #    [[0.2, 0.3, 0.5, 0.9]
       #     [0.1, 0.2, 0.6, 0.7]]
       # 接下来的示例中，我们在每处函数调用后面都标注出了它的结果张量。
-      x = fluid.layers.data(name='x', shape=[2, 4], dtype='float32')
+      x = fluid.data(name='x', shape=[2, 4], dtype='float32')
       fluid.layers.reduce_sum(x)  # [3.5]
       fluid.layers.reduce_sum(x, dim=0)  # [0.3, 0.5, 1.1, 1.6]
       fluid.layers.reduce_sum(x, dim=-1)  # [1.9, 1.6]
@@ -36,7 +36,7 @@ reduce_sum
       #      [[[1, 2], [3, 4]],
       #      [[5, 6], [7, 8]]]
       # 接下来的示例中，我们在每处函数调用后面都标注出了它的结果张量。
-      y = fluid.layers.data(name='y', shape=[2, 2, 2], dtype='float32')
+      y = fluid.data(name='y', shape=[2, 2, 2], dtype='float32')
       fluid.layers.reduce_sum(y, dim=[1, 2]) # [10, 26]
       fluid.layers.reduce_sum(y, dim=[0, 1]) # [16, 20]