sync ch sample to eng

doc/fluid/api_cn/layers_cn/batch_norm_cn.rst

@@ -67,18 +67,9 @@ moving_mean和moving_var是训练过程中统计得到的全局均值和方差
 .. code-block:: python
 
     import paddle.fluid as fluid
-    import numpy as np
-    x = fluid.layers.data(name='x', shape=[3, 7, 3, 7], dtype='float32', append_batch_size=False)
-    hidden1 = fluid.layers.fc(input=x, size=200)
-    param_attr = fluid.ParamAttr(name='batch_norm_w', initializer=fluid.initializer.Constant(value=1.0))
-    bias_attr = fluid.ParamAttr(name='batch_norm_b', initializer=fluid.initializer.Constant(value=0.0))
-    hidden2 = fluid.layers.batch_norm(input=hidden1, param_attr = param_attr, bias_attr = bias_attr)
-    place = fluid.CPUPlace()
-    exe = fluid.Executor(place)
-    exe.run(fluid.default_startup_program())
-    np_x = np.random.random(size=(3, 7, 3, 7)).astype('float32')
-    output = exe.run(feed={"x": np_x}, fetch_list = [hidden2])
-    print(output)
+    x = fluid.data(name='x', shape=[3, 7, 3, 7], dtype='float32', append_batch_size=False)
+    hidden1 = fluid.layers.fc(input=x, size=200, param_attr='fc1.w')
+    hidden2 = fluid.layers.batch_norm(input=hidden1)
 
 .. code-block:: python
 

doc/fluid/api_cn/layers_cn/cos_sim_cn.rst

@@ -28,17 +28,9 @@ cos_sim
 ..  code-block:: python
 
     import paddle.fluid as fluid
-    import numpy as np
-    x = fluid.layers.data(name='x', shape=[3, 7], dtype='float32', append_batch_size=False)
-    y = fluid.layers.data(name='y', shape=[1, 7], dtype='float32', append_batch_size=False)
+    x = fluid.data(name='x', shape=[3, 7], dtype='float32')
+    y = fluid.data(name='y', shape=[1, 7], dtype='float32')
     out = fluid.layers.cos_sim(x, y)
-    place = fluid.CPUPlace()
-    exe = fluid.Executor(place)
-    exe.run(fluid.default_startup_program())
-    np_x = np.random.random(size=(3, 7)).astype('float32')
-    np_y = np.random.random(size=(1, 7)).astype('float32')
-    output = exe.run(feed={"x": np_x, "y": np_y}, fetch_list = [out])
-    print(output)
 
 
 

doc/fluid/api_cn/layers_cn/dropout_cn.rst

@@ -46,13 +46,6 @@ dropout操作符可以从程序中移除，使程序变得高效。
 .. code-block:: python
 
     import paddle.fluid as fluid
-    import numpy as np
-    x = fluid.layers.data(name="x", shape=[32, 32], dtype="float32")
-    droped = fluid.layers.dropout(x, dropout_prob=0.5)
-    place = fluid.CPUPlace()
-    exe = fluid.Executor(place)
-    exe.run(fluid.default_startup_program())
-    np_x = np.random.random(size=(32, 32)).astype('float32')
-    output = exe.run(feed={"x": np_x}, fetch_list = [droped])
-    print(output)
+    x = fluid.data(name="data", shape=[None, 32, 32], dtype="float32")
+    dropped = fluid.layers.dropout(x, dropout_prob=0.5)
 

doc/fluid/api_cn/layers_cn/instance_norm_cn.rst

@@ -39,16 +39,7 @@ NCHW[batch,in_channels,in_height,in_width]
 .. code-block:: python
     
     import paddle.fluid as fluid
-    import numpy as np
-    x = fluid.layers.data(name='x', shape=[3, 7, 3, 7], dtype='float32', append_batch_size=False)
-    hidden1 = fluid.layers.fc(input=x, size=200)
-    param_attr = fluid.ParamAttr(name='instance_norm_w', initializer=fluid.initializer.Constant(value=1.0))
-    bias_attr = fluid.ParamAttr(name='instance_norm_b', initializer=fluid.initializer.Constant(value=0.0))
-    hidden2 = fluid.layers.instance_norm(input=hidden1, param_attr = param_attr, bias_attr = bias_attr)
-    place = fluid.CPUPlace()
-    exe = fluid.Executor(place)
-    exe.run(fluid.default_startup_program())
-    np_x = np.random.random(size=(3, 7, 3, 7)).astype('float32')
-    output = exe.run(feed={"x": np_x}, fetch_list = [hidden2])
-    print(output)
+    x = fluid.data(name='x', shape=[3, 7, 3, 7], dtype='float32')
+    hidden1 = fluid.layers.fc(input=x, size=200, param_attr='fc1.w')
+    hidden2 = fluid.layers.instance_norm(input=hidden1)
 

doc/fluid/api_cn/layers_cn/npair_loss_cn.rst

@@ -26,23 +26,14 @@ NPair损失需要成对的数据。NPair损失分为两部分：第一部分是
 .. code-block:: python
 
     import paddle.fluid as fluid
-    import numpy as np
-    anchor = fluid.layers.data(
-              name = 'anchor', shape = [18, 6], dtype = 'float32', append_batch_size=False)
-    positive = fluid.layers.data(
-              name = 'positive', shape = [18, 6], dtype = 'float32', append_batch_size=False)
-    labels = fluid.layers.data(
-              name = 'labels', shape = [18], dtype = 'float32', append_batch_size=False)
-
-    res = fluid.layers.npair_loss(anchor, positive, labels, l2_reg = 0.002)
-    place = fluid.CPUPlace()
-    exe = fluid.Executor(place)
-    exe.run(fluid.default_startup_program())
-    a = np.random.rand(18, 6).astype("float32")
-    p = np.random.rand(18, 6).astype("float32")
-    l = np.random.rand(18).astype("float32")
-    output = exe.run(feed={"anchor": a, "positive": p, "labels": l}, fetch_list=[res])
-    print(output)
+    anchor = fluid.data(
+                  name = 'anchor', shape = [18, 6], dtype = 'float32', append_batch_size=False)
+    positive = fluid.data(
+                  name = 'positive', shape = [18, 6], dtype = 'float32', append_batch_size=False)
+    labels = fluid.data(
+                  name = 'labels', shape = [18], dtype = 'float32', append_batch_size=False)
+
+    npair_loss = fluid.layers.npair_loss(anchor, positive, labels, l2_reg = 0.002)
 
 
 

doc/fluid/api_cn/nets_cn/img_conv_group_cn.rst

@@ -32,8 +32,7 @@ Image Convolution Group由Convolution2d，BatchNorm，DropOut和Pool2d组成。
 .. code-block:: python
 
     import paddle.fluid as fluid
-    import numpy as np
-    img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
+    img = fluid.data(name='img', shape=[None, 1, 28, 28], dtype='float32')
     conv_pool = fluid.nets.img_conv_group(input=img,
                                           conv_padding=1,
                                           conv_num_filter=[3, 3],
@@ -41,12 +40,6 @@ Image Convolution Group由Convolution2d，BatchNorm，DropOut和Pool2d组成。
                                           conv_act="relu",
                                           pool_size=2,
                                           pool_stride=2)
-    place = fluid.CPUPlace()
-    exe = fluid.Executor(place)
-    exe.run(fluid.default_startup_program())
-    np_x = np.random.random(size=(1, 1, 28, 28)).astype('float32')
-    output = exe.run(feed={"img": np_x}, fetch_list = [conv_pool])
-    print(output)