Fix GradientClipByNorm doc (#1399)

* fix GradientClipByNorm
test=document_preview

doc/fluid/api_cn/clip_cn/GradientClipByNorm_cn.rst

@@ -5,16 +5,26 @@ GradientClipByNorm
 
 .. py:class:: paddle.fluid.clip.GradientClipByNorm(clip_norm)
 
-将张量转换为L2范数不超过 ``clip_norm`` 的张量
+将输入多维Tensor :math:`X` 转换为L2范数不超过给定的二范数最大值（ ``clip_norm`` ）的多维Tensor。（多维Tensor不是从该类传入， 而是通过 ``fluid.program_guard`` 的 ``main_program`` 参数传入）。
 
-该operator 限制了 输入张量 :math:`X` 的L2范数不会超过 :math:`max\_norm` 。如果 :math:`X` 的 ``L2`` 范数小于或等于 :math:`max\_norm` ,输出和 :math:`X` 一样，如果 :math:`X` 的L2范数大于 :math:`max\_norm` , :math:`X` 将被线性缩放到L2范数等于 :math:`max\_norm` ,如以下公式所示:
+该类限制了输入多维Tensor :math:`X` 的L2范数不会超过 ``clip_norm`` 。
 
 .. math::
-            \\Out = \frac{max\_norm∗X}{norm(X)}\\
 
-其中 :math:`norm（X）` 代表 :math:`X` 的 L2 范数
+  Out=
+  \left\{
+  \begin{aligned}
+  &  X & & if (norm(X) \leq clip\_norm)\\
+  &  \frac{clip\_norm∗X}{norm(X)} & & if (norm(X) > clip\_norm) \\
+  \end{aligned}
+  \right.
 
 
+其中 :math:`norm（X）` 代表 :math:`X` 的L2范数
+
+.. math::
+  \\norm(X) = (\sum_{i=1}^{n}|x_i|^2)^{\frac{1}{2}}\\
+
 参数:
  - **clip_norm** (float) - 二范数最大值
 
@@ -24,19 +34,46 @@ GradientClipByNorm
 .. code-block:: python
 
   import paddle.fluid as fluid
-    w_param_attrs = fluid.ParamAttr(name=None,
-                                    initializer=fluid.initializer.UniformInitializer(low=-1.0, high=1.0, seed=0),
-                                    learning_rate=1.0,
-                                    regularizer=fluid.regularizer.L1Decay(1.0),
-                                    trainable=True,
-                                    gradient_clip=fluid.clip.GradientClipByNorm(clip_norm=2.0))
-    x = fluid.layers.data(name='x', shape=[10], dtype='float32')
-    y_predict = fluid.layers.fc(input=x, size=1, param_attr=w_param_attrs)
-
-
-
-
-
-
-
-
+  import paddle.fluid.core as core
+  import paddle
+  place = core.CPUPlace()
+  prog = fluid.framework.Program()
+  startup_program = fluid.framework.Program()
+  with fluid.program_guard(
+              main_program=prog, startup_program=startup_program):
+      image = fluid.layers.data(name='x', shape=[784], dtype='float32')
+      label = fluid.layers.data(name='y', shape=[1], dtype='int64')
+      hidden1 = fluid.layers.fc(input=image, size=128, act='relu')
+      hidden2 = fluid.layers.fc(input=hidden1, size=64, act='relu')
+      predict = fluid.layers.fc(input=hidden2, size=10, act='softmax')
+      cost = fluid.layers.cross_entropy(input=predict, label=label)
+      avg_cost = fluid.layers.mean(cost)
+  prog_clip = prog.clone()
+  avg_cost_clip = prog_clip.block(0).var(avg_cost.name)
+  p_g = fluid.backward.append_backward(loss=avg_cost)
+  p_g_clip = fluid.backward.append_backward(loss=avg_cost_clip)
+  with fluid.program_guard(main_program=prog_clip, startup_program=startup_program):
+      fluid.clip.set_gradient_clip(
+          fluid.clip.GradientClipByNorm(clip_norm=2.0))
+      p_g_clip = fluid.clip.append_gradient_clip_ops(p_g_clip)
+  grad_list = [elem[1] for elem in p_g]
+  grad_clip_list = [elem[1] for elem in p_g_clip]
+  train_reader = paddle.batch(
+      paddle.reader.shuffle(
+          paddle.dataset.mnist.train(), buf_size=8192),
+      batch_size=128)
+
+  exe = fluid.Executor(place)
+  feeder = fluid.DataFeeder(feed_list=[image, label], place=place)
+  exe.run(startup_program)
+
+  count = 0
+  for data in train_reader():
+      count += 1
+      print("count:%s" % count)
+      if count > 5:
+         break
+      out = exe.run(prog, feed=feeder.feed(data), fetch_list=grad_list)
+      out_clip = exe.run(prog_clip,
+                         feed=feeder.feed(data),
+                         fetch_list=grad_clip_list)