Release/1.5 synchronize api_en and api_cn (#1068)

* synchronize api_en and api_cn

* synchronize api_en and api_cn

* synchronize api_en and api_cn

* synchronize api_en and api_cn

* synchronize api_en and api_cn

* synchronize api_en and api_cn

* synchronize api_en and api_cn

* synchronize api_en and api_cn

* synchronize api_en and api_cn

* synchronize api_en and api_cn

* synchronize api_en and api_cn

* synchronize api_en and api_cn

* synchronize api_en and api_cn

* synchronize api_en and api_cn

* synchronize api_en and api_cn

* synchronize api_en and api_cn

* synchronize api_en and api_cn

* synchronize api_en and api_cn

* synchronize api_en and api_cn

* synchronize api_en and api_cn

* synchronize api_en and api_cn

doc/fluid/api_cn/clip_cn.rst

@@ -183,7 +183,7 @@ GradientClipByValue
      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.regualrizer.L1Decay(1.0),
+                                     regularizer=fluid.regularizer.L1Decay(1.0),
                                      trainable=True,
                                      gradient_clip=fluid.clip.GradientClipByValue(-1.0, 1.0))
      x = fluid.layers.data(name='x', shape=[10], dtype='float32')

doc/fluid/api_cn/dataset_cn.rst

@@ -22,7 +22,7 @@ DatasetFactory是一个按数据集名称创建数据集的 "工厂"，可以创
 .. code-block:: python
 
     import paddle.fluid as fluid
-    dataset = paddle.fluid.DatasetFactory().create_dataset("InMemoryDataset")
+    dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset")
 
 .. py:method:: create_dataset(datafeed_class='QueueDataset')
 
@@ -127,7 +127,7 @@ InMemoryDataset会向内存中加载数据并在训练前缓冲数据。此类
     dataset.global_shuffle(fleet)
     exe = fluid.Executor(fluid.CPUPlace())
     exe.run(fluid.default_startup_program())
-    exe.train_from_dataset(fluid.default_main_program(), dataset)dataset.release_memory()
+    exe.train_from_dataset(fluid.default_main_program(), dataset)
     dataset.release_memory()
 
 .. py:method:: get_memory_data_size(fleet=None)

doc/fluid/api_cn/fluid_cn.rst

@@ -1041,8 +1041,9 @@ DistributeTranspiler
   t = fluid.DistributeTranspiler(config=config)
   t.transpile(trainer_id=trainer_id, trainers=trainer_endpoints, current_endpoint="192.168.0.1:6174")
   exe = fluid.ParallelExecutor(
+     use_cuda=True,
      loss_name=avg_loss.name,
-     num_trainers=len(trainer_num,
+     num_trainers=trainer_num,
      trainer_id=trainer_id
   )
 
@@ -1273,11 +1274,11 @@ ExecutionStrategy
 int型成员。它表明了清空执行时产生的临时变量需要的程序执行迭代次数。因为临时变量的形状可能在两次重复过程中保持一致，所以它会使整体执行过程更快。默认值为1。
 
 .. note::
-  1. 如果在调用 ``run`` 方法时获取结果数据，``ParallelExecutor`` 会在当前程序重复执行尾部清空临时变量
+  1. 如果在调用 ``run`` 方法时获取结果数据，``llelExecutor`` 会在当前程序重复执行尾部清空临时变量
 
   2. 在一些NLP模型里，该成员会致使GPU内存不足。此时，你应减少 ``num_iteration_per_drop_scope`` 的值
 
-.. py:attribute:: num_iteration_per_run
+.. py:attribute:: num_iteration_per_runPara
 它配置了当用户在python脚本中调用pe.run()时执行器会执行的迭代次数。
 
 .. py:attribute:: num_threads
@@ -1508,7 +1509,7 @@ infer_from_dataset的文档与train_from_dataset几乎完全相同，只是在
         filelist = [] # 您可以设置您自己的filelist，如filelist = ["dataA.txt"]
         dataset.set_filelist(filelist)
         exe.run(fluid.default_startup_program())
-        exe.infer_from_dataset(program=fluid.default_main_program(),
+        exe.train_from_dataset(program=fluid.default_main_program(),
                                dataset=dataset)
 
 
@@ -2100,6 +2101,7 @@ ParallelExecutor
                 loss = fluid.layers.mean(hidden)
      
             place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
+            exe = fluid.Executor(place)
             exe.run(startup_program)
      
             parallel_exe = fluid.ParallelExecutor(use_cuda=use_cuda,
@@ -2143,8 +2145,9 @@ ParamAttr
    
    w_param_attrs = fluid.ParamAttr(name="fc_weight",
                                    learning_rate=0.5,
-                                   regularizer=fluid.L2Decay(1.0),
+                                   regularizer=fluid.regularizer.L2Decay(1.0),
                                    trainable=True)
+   x = fluid.layers.data(name='X', shape=[1], dtype='float32')
    y_predict = fluid.layers.fc(input=x, size=10, param_attr=w_param_attrs)
 
 

doc/fluid/api_cn/initializer_cn.rst

@@ -227,7 +227,7 @@ NormalInitializer
         import paddle.fluid as fluid
         x = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
         fc = fluid.layers.fc(input=x, size=10,
-            param_attr=fluid.initializer.Normal(loc=0.0, scale=2.0)
+            param_attr=fluid.initializer.Normal(loc=0.0, scale=2.0))
 
 
 .. _cn_api_fluid_initializer_NumpyArrayInitializer:

doc/fluid/api_cn/layers_cn/detection_cn.rst

@@ -138,7 +138,7 @@ box_clip
         name='boxes', shape=[8, 4], dtype='float32', lod_level=1)
     im_info = fluid.layers.data(name='im_info', shape=[3])
     out = fluid.layers.box_clip(
-        input=boxes, im_info=im_info, inplace=True)
+        input=boxes, im_info=im_info)
 
 
 
@@ -286,7 +286,7 @@ box decode过程得出decode_box，然后分配方案如下所述：
     pb = fluid.layers.data(
         name='prior_box', shape=[4], dtype='float32')
     pbv = fluid.layers.data(
-        name='prior_box_var', shape=[4], dtype='float32', append_batch_size=False))
+        name='prior_box_var', shape=[4], dtype='float32', append_batch_size=False)
     loc = fluid.layers.data(
         name='target_box', shape=[4*81], dtype='float32')
     scores = fluid.layers.data(
@@ -1288,14 +1288,16 @@ rpn_target_assign
 ..  code-block:: python
 
         import paddle.fluid as fluid
-        bbox_pred = fluid.layers.data(name=’bbox_pred’, shape=[100, 4],
-                append_batch_size=False, dtype=’float32’)
-        cls_logits = fluid.layers.data(name=’cls_logits’, shape=[100, 1],
-                append_batch_size=False, dtype=’float32’)
-        anchor_box = fluid.layers.data(name=’anchor_box’, shape=[20, 4],
-                append_batch_size=False, dtype=’float32’)
-        gt_boxes = fluid.layers.data(name=’gt_boxes’, shape=[10, 4],
-                append_batch_size=False, dtype=’float32’)
+        bbox_pred = fluid.layers.data(name='bbox_pred', shape=[100, 4],
+                append_batch_size=False, dtype='float32')
+        cls_logits = fluid.layers.data(name='cls_logits', shape=[100, 1],
+                append_batch_size=False, dtype='float32')
+        anchor_box = fluid.layers.data(name='anchor_box', shape=[20, 4],
+                append_batch_size=False, dtype='float32')
+        anchor_var = fluid.layers.data(name='anchor_var', shape=[20, 4],	 	 
+                append_batch_size=False, dtype='float32')
+        gt_boxes = fluid.layers.data(name='gt_boxes', shape=[10, 4],
+                append_batch_size=False, dtype='float32')
         is_crowd = fluid.layers.data(name='is_crowd', shape=[1],
                     append_batch_size=False, dtype='float32')
         im_info = fluid.layers.data(name='im_infoss', shape=[1, 3],
@@ -1684,9 +1686,9 @@ yolov3_loss
 
     import paddle.fluid as fluid
     x = fluid.layers.data(name='x', shape=[255, 13, 13], dtype='float32')
-    gt_box = fluid.layers.data(name='gtbox', shape=[6, 4], dtype='float32')
-    gt_label = fluid.layers.data(name='gtlabel', shape=[6], dtype='int32')
-    gt_score = fluid.layers.data(name='gtscore', shape=[6], dtype='float32')
+    gt_box = fluid.layers.data(name='gt_box', shape=[6, 4], dtype='float32')
+    gt_label = fluid.layers.data(name='gt_label', shape=[6], dtype='int32')
+    gt_score = fluid.layers.data(name='gt_score', shape=[6], dtype='float32')
     anchors = [10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326]
     anchor_mask = [0, 1, 2]
     loss = fluid.layers.yolov3_loss(x=x, gt_box=gt_box, gt_label=gt_label,

doc/fluid/api_cn/layers_cn/io_cn.rst

@@ -85,8 +85,8 @@ create_py_reader_by_data
         loss = fluid.layers.cross_entropy(input=predict, label=label)
         return fluid.layers.mean(loss)
     
-    image = fluid.layers.data(name='image', shape=[1, 28, 28], dtypes='float32')
-    label = fluid.layers.data(name='label', shape=[1], dtypes='int64')
+    image = fluid.layers.data(name='image', shape=[1, 28, 28], dtype='float32')
+    label = fluid.layers.data(name='label', shape=[1], dtype='int64')
     reader = fluid.layers.create_py_reader_by_data(capacity=64,
                                                    feed_list=[image, label])
     reader.decorate_paddle_reader(

doc/fluid/api_cn/layers_cn/metric_op_cn.rst

@@ -85,7 +85,7 @@ auc
     data = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
     label = fluid.layers.data(name="label", shape=[1], dtype="int32")
     predict = fluid.layers.fc(input=data, size=2)
-    auc_out=fluid.layers.auc(input=prediction, label=label)
+    auc_out=fluid.layers.auc(input=predict, label=label)
 
 
 

doc/fluid/api_cn/layers_cn/nn_cn.rst

@@ -144,7 +144,7 @@ pooling3d操作根据输入 ``input`` ，``pool_size`` ， ``pool_type`` 参数
 
     data = fluid.layers.data(
     name='data', shape=[3, 32, 32, 32], dtype='float32')
-    pool_out, mask = fluid.layers.adaptive_pool3d(
+    pool_out = fluid.layers.adaptive_pool3d(
                       input=data,
                       pool_size=[3, 3, 3],
                       pool_type='avg')
@@ -520,7 +520,7 @@ beam_search
         name='probs', shape=[10000], dtype='float32')
     topk_scores, topk_indices = fluid.layers.topk(probs, k=beam_size)
     accu_scores = fluid.layers.elementwise_add(
-                                          x=fluid.layers.log(x=topk_scores)),
+                                          x=fluid.layers.log(x=topk_scores),
                                           y=fluid.layers.reshape(
                                               pre_scores, shape=[-1]),
                                           axis=0)
@@ -698,7 +698,7 @@ BRelu 激活函数
 .. code-block:: python
 
     import paddle.fluid as fluid
-    x = fluid.layers.data(name="x", shape=[2,3,16,16], dtype=”float32”)
+    x = fluid.layers.data(name="x", shape=[2,3,16,16], dtype="float32")
     y = fluid.layers.brelu(x, t_min=1.0, t_max=20.0)
 
 
@@ -1456,7 +1456,7 @@ crop
 
     ## or
     z = fluid.layers.data(name="z", shape=[3, 5], dtype="float32")
-    crop = fluid.layers.crop(z, shape=[2, 3])
+    crop = fluid.layers.crop(z, shape=[-1, 2, 3])
 
 
 
@@ -3481,7 +3481,7 @@ gaussian_random算子。
 
     import paddle.fluid as fluid
     import paddle.fluid.layers as layers
-    out = fluid.layers.gaussian_random(shape=[20, 30])
+    out = layers.gaussian_random(shape=[20, 30])
 
 
 
@@ -3629,9 +3629,9 @@ step 2：
 
     import paddle.fluid as fluid
 
-    x = fluid.layers.data(name='x', shape=[3, 10, 32, 32], dtype='float32')
-    theta = fluid.layers.data(name='theta', shape=[3, 2, 3], dtype='float32')
-    grid = fluid.layers.affine_grid(theta=theta, out_shape=[3, 10, 32, 32]})
+    x = fluid.layers.data(name='x', shape=[10, 32, 32], dtype='float32')
+    theta = fluid.layers.data(name='theta', shape=[2, 3], dtype='float32')
+    grid = fluid.layers.affine_grid(theta=theta, out_shape=[3, 10, 32, 32])
     out = fluid.layers.grid_sampler(x=x, grid=grid)
 
 
@@ -4383,9 +4383,9 @@ label_smooth
     import paddle.fluid as fluid
     import paddle.fluid.layers as layers
 
-    label = fluid.layers.data(name="label", shape=[1], dtype="float32")
-    one_hot_label = fluid.layers.one_hot(input=label, depth=10)
-    smooth_label = fluid.layers.label_smooth(
+    label = layers.data(name="label", shape=[1], dtype="float32")
+    one_hot_label = layers.one_hot(input=label, depth=10)
+    smooth_label = layers.label_smooth(
     label=one_hot_label, epsilon=0.1, dtype="float32")
 
 
@@ -5043,7 +5043,7 @@ sigmoid的计算公式为： :math:`sigmoid(x) = 1 / (1 + e^{-x})` 。
   init_h = layers.fill_constant( [num_layers, batch_size, hidden_size], 'float32', 0.0 )
   init_c = layers.fill_constant( [num_layers, batch_size, hidden_size], 'float32', 0.0 )
 
-  rnn_out, last_h, last_c = fluid.layers.lstm(emb, init_h, init_c, max_len, hidden_size, num_layers, dropout_prob=dropout_prob)
+  rnn_out, last_h, last_c = layers.lstm(emb, init_h, init_c, max_len, hidden_size, num_layers, dropout_prob=dropout_prob)
 
 
 
@@ -5118,8 +5118,8 @@ lstm单元的输入包括 :math:`x_{t}` ， :math:`h_{t-1}` 和 :math:`c_{t-1}`
     pre_cell = fluid.layers.data(name='pre_cell', shape=[hidden_dim], dtype='float32')
     hidden = fluid.layers.lstm_unit(
         x_t=x,
-        hidden_t_prev=prev_hidden,
-        cell_t_prev=prev_cell)
+        hidden_t_prev=pre_hidden,
+        cell_t_prev=pre_cell)
 
 
 
@@ -5373,7 +5373,7 @@ mean_iou
    import paddle.fluid as fluid
    predict = fluid.layers.data(name='predict', shape=[3, 32, 32])
    label = fluid.layers.data(name='label', shape=[1])
-   iou, wrongs, corrects = fluid.layers.mean_iou(predict, label, num_classes)
+   iou, wrongs, corrects = fluid.layers.mean_iou(predict, label, num_classes=5)
 
 
 
@@ -7652,7 +7652,7 @@ sequence_expand
     x = fluid.layers.data(name='x', shape=[10], dtype='float32')
     y = fluid.layers.data(name='y', shape=[10, 20],
                  dtype='float32', lod_level=1)
-    out = fluid.layers.sequence_expand(x=x, y=y, ref_level=0)
+    out = layers.sequence_expand(x=x, y=y, ref_level=0)
 
 
 
@@ -8806,7 +8806,7 @@ SoftRelu 激活函数
 
     import paddle.fluid as fluid
 
-    x = fluid.layers.data(name=”x”, shape=[2,3,16,16], dtype=”float32”)
+    x = fluid.layers.data(name="x", shape=[3,16,16], dtype="float32")
     y = fluid.layers.soft_relu(x, threshold=20.0)
 
 
@@ -9078,7 +9078,7 @@ split
     # x1.shape  [-1, 3, 3, 5]
     # x2.shape  [-1, 3, 3, 5]
     
-    x0, x1, x2 = fluid.layers.split(input, num_or_sections=[2, 3, 4], dim=2)
+    x0, x1, x2 = fluid.layers.split(input, num_or_sections=3, dim=2)
     # x0.shape  [-1, 3, 2, 5]
     # x1.shape  [-1, 3, 3, 5]
     # x2.shape  [-1, 3, 4, 5]
@@ -9180,8 +9180,8 @@ squeeze
 
     import paddle.fluid as fluid
     import paddle.fluid.layers as layers
-    x = fluid.layers.data(name='x', shape=[5, 1, 10])
-    y = fluid.layers.sequeeze(input=x, axes=[1])
+    x = layers.data(name='x', shape=[5, 1, 10])
+    y = layers.squeeze(input=x, axes=[1])
 
 
 
@@ -9331,9 +9331,9 @@ sum算子。
 
     import paddle.fluid as fluid
     import paddle.fluid.layers as layers
-    input0 = fluid.layers.data(name="input0", shape=[13, 11], dtype='float32')
+    input0 = layers.data(name="input0", shape=[13, 11], dtype='float32')
     input1 = layers.data(name="input1", shape=[13, 11], dtype='float32')
-    out = fluid.layers.sum([input0,input1])
+    out = layers.sum([input0,input1])
 
 
 
@@ -9518,7 +9518,7 @@ topk
     import paddle.fluid as fluid
     import paddle.fluid.layers as layers
     input = layers.data(name="input", shape=[13, 11], dtype='float32')
-    top5_values, top5_indices = fluid.layers.topk(input, k=5)
+    top5_values, top5_indices = layers.topk(input, k=5)
 
 
 
@@ -9651,8 +9651,8 @@ uniform_random_batch_size_like算子。
     import paddle.fluid as fluid
     import paddle.fluid.layers as layers
 
-    input = fluid.layers.data(name="input", shape=[13, 11], dtype='float32')
-    out = fluid.layers.uniform_random_batch_size_like(input, [-1, 11])
+    input = layers.data(name="input", shape=[13, 11], dtype='float32')
+    out = layers.uniform_random_batch_size_like(input, [-1, 11])
 
 
 
@@ -9685,7 +9685,7 @@ unsqueeze
 
     import paddle.fluid as fluid
     x = fluid.layers.data(name='x', shape=[5, 10])
-    y = fluid.layers.unsequeeze(input=x, axes=[1])
+    y = fluid.layers.unsqueeze(input=x, axes=[1])
 
 
 

doc/fluid/api_cn/layers_cn/tensor_cn.rst

@@ -28,8 +28,8 @@ argmax
     
     import paddle.fluid as fluid
     x = fluid.layers.data(name="x", shape=[3, 4], dtype="float32")
-    out = fluid.layers.argmax(x=in, axis=0)
-    out = fluid.layers.argmax(x=in, axis=-1)
+    out = fluid.layers.argmax(x, axis=0)
+    out = fluid.layers.argmax(x, axis=-1)
 
 
 
@@ -64,8 +64,8 @@ argmin
     
     import paddle.fluid as fluid
     x = fluid.layers.data(name="x", shape=[3, 4], dtype="float32")
-    out = fluid.layers.argmin(x=in, axis=0)
-    out = fluid.layers.argmin(x=in, axis=-1)
+    out = fluid.layers.argmin(x, axis=0)
+    out = fluid.layers.argmin(x, axis=-1)
 
 
 
@@ -224,7 +224,7 @@ concat
     b = fluid.layers.data(name='b', shape=[2, 3], dtype='float32')
     c = fluid.layers.data(name='c', shape=[2, 2], dtype='float32')
     d = fluid.layers.data(name='d', shape=[2, 5], dtype='float32')
-    out = fluid.layers.concat(input=[Efirst, Esecond, Ethird, Efourth])
+    out = fluid.layers.concat(input=[a, b, c, d], axis=2)
 
 
 
@@ -298,7 +298,7 @@ create_parameter
 
     import paddle.fluid as fluid
     import paddle.fluid.layers as layers
-    W = fluid.layers.create_parameter(shape=[784, 200], dtype='float32')
+    W = layers.create_parameter(shape=[784, 200], dtype='float32')
 
 
 

doc/fluid/api_cn/metrics_cn.rst

@@ -357,7 +357,6 @@ EditDistance
     seq_num_batch0 = batch_size
 
     distance_evaluator.update(edit_distances_batch0, seq_num_batch0)
-    distance, instance_error = distance_evaluator.eval()
     avg_distance, wrong_instance_ratio = distance_evaluator.eval()
     print("the average edit distance for batch0 is %.2f and the wrong instance ratio is %.2f " % (avg_distance, wrong_instance_ratio))
     edit_distances_batch1 = np.random.randint(low = 0, high = 10, size = (batch_size, 1))
@@ -367,6 +366,8 @@ EditDistance
     avg_distance, wrong_instance_ratio = distance_evaluator.eval()
     print("the average edit distance for batch0 and batch1 is %.2f and the wrong instance ratio is %.2f " % (avg_distance, wrong_instance_ratio))
 
+    distance_evaluator.reset()
+
 
 .. py:method:: distance_evaluator.reset()