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--- a/doc/_sources/ui/api/trainer_config_helpers/layers.txt
+++ b/doc/_sources/ui/api/trainer_config_helpers/layers.txt
@@ -130,12 +130,6 @@ gru_step_layer
 Recurrent Layer Group
 =====================
-memory
------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: memory
-    :noindex:
 recurrent_group
 ---------------
 ..  automodule:: paddle.trainer_config_helpers.layers

--- a/doc/searchindex.js
+++ b/doc/searchindex.js
--- a/doc/ui/api/trainer_config_helpers/layers.html
+++ b/doc/ui/api/trainer_config_helpers/layers.html
@@ -901,49 +901,6 @@ will get a warning.</li>
 </div>
 <div class="section" id="recurrent-layer-group">
 <h1>Recurrent Layer Group<a class="headerlink" href="#recurrent-layer-group" title="Permalink to this headline">¶</a></h1>
-<div class="section" id="memory">
-<h2>memory<a class="headerlink" href="#memory" title="Permalink to this headline">¶</a></h2>
-<dl class="function">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">memory</code><span class="sig-paren">(</span><em>name</em>, <em>size</em>, <em>is_seq=False</em>, <em>boot_layer=None</em>, <em>boot_bias=None</em>, <em>boot_bias_active_type=None</em>, <em>boot_with_const_id=None</em><span class="sig-paren">)</span></dt>
-<dd><p>The memory layers is a layer cross each time step. Reference this output
-as previous time step layer <code class="code docutils literal"><span class="pre">name</span></code> &#8216;s output.</p>
-<p>The default memory is zero in first time step, previous time step&#8217;s
-output in the rest time steps.</p>
-<p>If boot_bias, the first time step value is this bias and
-with activation.</p>
-<p>If boot_with_const_id, then the first time stop is a IndexSlot, the
-Arguments.ids()[0] is this <code class="code docutils literal"><span class="pre">cost_id</span></code>.</p>
-<p>If boot_layer is not null, the memory is just the boot_layer&#8217;s output.
-Set <code class="code docutils literal"><span class="pre">is_seq</span></code> is true boot layer is sequence.</p>
-<p>The same name layer in recurrent group will set memory on each time
-step.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>name</strong> (<em>basestring</em>) &#8211; memory&#8217;s name.</li>
-<li><strong>size</strong> (<em>int</em>) &#8211; size of memory.</li>
-<li><strong>is_seq</strong> (<em>bool</em>) &#8211; is sequence for boot_layer</li>
-<li><strong>boot_layer</strong> (<em>LayerOutput|None</em>) &#8211; boot layer of memory.</li>
-<li><strong>boot_bias</strong> (<em>ParameterAttribute|None</em>) &#8211; boot layer&#8217;s bias</li>
-<li><strong>boot_bias_active_type</strong> (<em>BaseActivation</em>) &#8211; boot layer&#8217;s active type.</li>
-<li><strong>boot_with_const_id</strong> (<em>int</em>) &#8211; boot layer&#8217;s id.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">LayerOutput object which is a memory.</p>
-</td>
-</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-</div>
 <div class="section" id="recurrent-group">
 <h2>recurrent_group<a class="headerlink" href="#recurrent-group" title="Permalink to this headline">¶</a></h2>
 <dl class="function">
@@ -2690,7 +2647,6 @@ It is used by recurrent layer group.</p>
 </ul>
 </li>
 <li><a class="reference internal" href="#recurrent-layer-group">Recurrent Layer Group</a><ul>
-<li><a class="reference internal" href="#memory">memory</a></li>
 <li><a class="reference internal" href="#recurrent-group">recurrent_group</a></li>
 <li><a class="reference internal" href="#beam-search">beam_search</a></li>
 <li><a class="reference internal" href="#get-output-layer">get_output_layer</a></li>

--- a/doc/ui/api/trainer_config_helpers/layers_index.html
+++ b/doc/ui/api/trainer_config_helpers/layers_index.html
@@ -114,7 +114,6 @@ var _hmt = _hmt || [];
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="layers.html#recurrent-layer-group">Recurrent Layer Group</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="layers.html#memory">memory</a></li>
 <li class="toctree-l2"><a class="reference internal" href="layers.html#recurrent-group">recurrent_group</a></li>
 <li class="toctree-l2"><a class="reference internal" href="layers.html#beam-search">beam_search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="layers.html#get-output-layer">get_output_layer</a></li>

--- a/doc_cn/_sources/algorithm/rnn/hierarchical-layer.txt
+++ b/doc_cn/_sources/algorithm/rnn/hierarchical-layer.txt
-# 支持双层序列作为输入的Layer
-## 概述
-在自然语言处理任务中，序列是一种常见的数据类型。一个独立的词语，可以看作是一个非序列输入，或者，我们称之为一个0层的序列；由词语构成的句子，是一个单层序列；若干个句子构成一个段落，是一个双层的序列。
-双层序列是一个嵌套的序列，它的每一个元素，又是一个单层的序列。这是一种非常灵活的数据组织方式，帮助我们构造一些复杂的输入信息。
-我们可以按照如下层次定义非序列，单层序列，以及双层序列。
-+ 0层序列：一个独立的元素，类型可以是PaddlePaddle支持的任意输入数据类型
-+ 单层序列：排成一列的多个元素，每个元素是一个0层序列，元素之间的顺序是重要的输入信息
-+ 双层序列：排成一列的多个元素，每个元素是一个单层序列，称之为双层序列的一个子序列（subseq），subseq的每个元素是一个0层序列
-在 PaddlePaddle中，下面这些Layer能够接受双层序列作为输入，完成相应的计算。
-## pooling_layer
-pooling_layer的使用示例如下，详细见<a href = "../../../doc/ui/api/trainer_config_helpers/layers.html#pooling-layer">配置API</a>。
-```python
-seq_pool = pooling_layer(input=layer,
-                         pooling_type=AvgPooling(),
-                         agg_level=AggregateLevel.EACH_SEQUENCE)
-```
- `pooling_type` 目前支持两种，分别是：MaxPooling()和AvgPooling()。
- `agg_level=AggregateLevel.TIMESTEP`时（默认值）：
-  - 作用：双层序列经过运算变成一个0层序列，或单层序列经过运算变成一个0层序列
-  - 输入：一个双层序列，或一个单层序列
-  - 输出：一个0层序列，即整个输入序列（单层或双层）的平均值（或最大值）
- `agg_level=AggregateLevel.EACH_SEQUENCE`时：
-  - 作用：一个双层序列经过运算变成一个单层序列
-  - 输入：必须是一个双层序列
-  - 输出：一个单层序列，序列的每个元素是原来双层序列每个subseq元素的平均值（或最大值）
-## last_seq 和 first_seq
-last_seq的使用示例如下（first_seq类似），详细见<a href = "../../../doc/ui/api/trainer_config_helpers/layers.html#last-seq">配置API</a>。
-```python
-last = last_seq(input=layer,
-                agg_level=AggregateLevel.EACH_SEQUENCE)
-```
- `agg_level=AggregateLevel.TIMESTEP`时（默认值）：
-  - 作用：一个双层序列经过运算变成一个0层序列，或一个单层序列经过运算变成一个0层序列
-  - 输入：一个双层序列或一个单层序列
-  - 输出：一个0层序列，即整个输入序列（双层或者单层）最后一个，或第一个元素。
- `agg_level=AggregateLevel.EACH_SEQUENCE`时：
-  - 作用：一个双层序列经过运算变成一个单层序列
-  - 输入：必须是一个双层序列
-  - 输出：一个单层序列，其中每个元素是双层序列中每个subseq最后一个（或第一个）元素。
-## expand_layer
-expand_layer的使用示例如下，详细见<a href = "../../../doc/ui/api/trainer_config_helpers/layers.html#expand-layer">配置API</a>。
-```python
-expand = expand_layer(input=layer1,
-                      expand_as=layer2,
-                      expand_level=ExpandLevel.FROM_TIMESTEP)
-```
- `expand_level=ExpandLevel.FROM_TIMESTEP`时（默认值）：
-  - 作用：一个0层序列经过运算扩展成一个单层序列，或者一个双层序列
-  - 输入：layer1必须是一个0层序列，是待扩展的数据；layer2可以是一个单层序列，或者是一个双层序列，提供扩展的长度信息
-  - 输出：一个单层序列，或一个双层序列，输出序列的类型（双层序列，或单层序列）和序列中含有元素的数目同 layer2一致。若输出是单层序列，单层序列的每个元素（0层序列），都是对layer1元素的拷贝；若输出是双层序列，双层序列每个subseq中每个元素（0层序列），都是对layer1元素的拷贝
- `expand_level=ExpandLevel.FROM_SEQUENCE`时：
-  - 作用：一个单层序列经过运算扩展成一个双层序列
-  - 输入：layer1必须是一个单层序列，是待扩展的数据；layer2必须是一个双层序列，提供扩展的长度信息
-  - 输出：一个双层序列，序列中含有元素的数目同layer2一致。要求单层序列含有元素的数目（0层序列），和双层序列含有subseq 的数目一致。单层序列第i个元素（0层序列），被扩展为一个单层序列，构成了输出双层序列的第i个subseq。
\ No newline at end of file
--- a/doc_cn/_sources/algorithm/rnn/hierarchical-rnn.txt
+++ b/doc_cn/_sources/algorithm/rnn/hierarchical-rnn.txt
-# 双层RNN配置与示例
-我们在`paddle/gserver/tests/test_RecurrentGradientMachine`单测中，通过多组语义相同的单双层RNN配置，讲解如何使用双层RNN。
-## 示例1：双进双出，subseq间无memory
-配置：单层RNN（`sequence_layer_group`）和双层RNN（`sequence_nest_layer_group`），语义完全相同。
-### 读取双层序列的方法
-首先，我们看一下单双层序列的不同数据组织形式（您也可以采用别的组织形式）：
- 单层序列的数据（`Sequence/tour_train_wdseg`）如下，一共有10个样本。每个样本由两部分组成，一个label（此处都为2）和一个已经分词后的句子。
-```text
-2  	酒店 有 很 舒适 的 床垫 子 ， 床上用品 也 应该 是 一人 一 换 ， 感觉 很 利落 对 卫生 很 放心 呀 。
-2  	很 温馨 ， 也 挺 干净 的 * 地段 不错 ， 出来 就 有 全家 ， 离 地铁站 也 近 ， 交通 很方便 * 就是 都 不 给 刷牙 的 杯子 啊 ， 就 第一天 给 了 一次性杯子 *
-2  	位置 方便 ， 强烈推荐 ， 十一 出去玩 的 时候 选 的 ， 对面 就是 华润万家 ， 周围 吃饭 的 也 不少 。
-2  	交通便利 ， 吃 很 便利 ， 乾 浄 、 安静 ， 商务 房 有 电脑 、 上网 快 ， 价格 可以 ， 就 早餐 不 好吃 。 整体 是 不错 的 。 適 合 出差 來 住 。
-2  	本来 准备 住 两 晚 ， 第 2 天 一早 居然 停电 ， 且 无 通知 ， 只有 口头 道歉 。 总体来说 性价比 尚可 ， 房间 较 新 ， 还是 推荐 .
-2  	这个 酒店 去过 很多 次 了 ， 选择 的 主要原因 是 离 客户 最 便宜 相对 又 近 的 酒店
-2  	挺好 的 汉庭 ， 前台 服务 很 热情 ， 卫生 很 整洁 ， 房间 安静 ， 水温 适中 ， 挺好 ！
-2  	HowardJohnson 的 品质 ， 服务 相当 好 的 一 家 五星级 。 房间 不错 、 泳池 不错 、 楼层 安排 很 合理 。 还有 就是 地理位置 ， 简直 一 流 。 就 在 天一阁 、 月湖 旁边 ， 离 天一广场 也 不远 。 下次 来 宁波 还会 住 。
-2  	酒店 很干净 ， 很安静 ， 很 温馨 ， 服务员 服务 好 ， 各方面 都 不错 *
-2  	挺好 的 ， 就是 没 窗户 ， 不过 对 得 起 这 价格
-```
- 双层序列的数据（`Sequence/tour_train_wdseg.nest`）如下，一共有4个样本。样本间用空行分开，代表不同的双层序列，序列数据和上面的完全一样。每个样本的子句数分别为2,3,2,3。
-```text
-2  	酒店 有 很 舒适 的 床垫 子 ， 床上用品 也 应该 是 一人 一 换 ， 感觉 很 利落 对 卫生 很 放心 呀 。
-2  	很 温馨 ， 也 挺 干净 的 * 地段 不错 ， 出来 就 有 全家 ， 离 地铁站 也 近 ， 交通 很方便 * 就是 都 不 给 刷牙 的 杯子 啊 ， 就 第一天 给 了 一次性杯子 *
-2  	位置 方便 ， 强烈推荐 ， 十一 出去玩 的 时候 选 的 ， 对面 就是 华润万家 ， 周围 吃饭 的 也 不少 。
-2  	交通便利 ， 吃 很 便利 ， 乾 浄 、 安静 ， 商务 房 有 电脑 、 上网 快 ， 价格 可以 ， 就 早餐 不 好吃 。 整体 是 不错 的 。 適 合 出差 來 住 。
-2  	本来 准备 住 两 晚 ， 第 2 天 一早 居然 停电 ， 且 无 通知 ， 只有 口头 道歉 。 总体来说 性价比 尚可 ， 房间 较 新 ， 还是 推荐 .
-2  	这个 酒店 去过 很多 次 了 ， 选择 的 主要原因 是 离 客户 最 便宜 相对 又 近 的 酒店
-2  	挺好 的 汉庭 ， 前台 服务 很 热情 ， 卫生 很 整洁 ， 房间 安静 ， 水温 适中 ， 挺好 ！
-2  	HowardJohnson 的 品质 ， 服务 相当 好 的 一 家 五星级 。 房间 不错 、 泳池 不错 、 楼层 安排 很 合理 。 还有 就是 地理位置 ， 简直 一 流 。 就 在 天一阁 、 月湖 旁边 ， 离 天一广场 也 不远 。 下次 来 宁波 还会 住 。
-2  	酒店 很干净 ， 很安静 ， 很 温馨 ， 服务员 服务 好 ， 各方面 都 不错 *
-2  	挺好 的 ， 就是 没 窗户 ， 不过 对 得 起 这 价格
-```
-其次，我们看一下单双层序列的不同dataprovider（见`sequenceGen.py`）：
- 单层序列的dataprovider如下：
-  - word_slot是integer_value_sequence类型，代表单层序列。
-  - label是integer_value类型，代表一个向量。
-```python
-def hook(settings, dict_file, **kwargs):
-    settings.word_dict = dict_file
-    settings.input_types = [integer_value_sequence(len(settings.word_dict)), 
-                            integer_value(3)]
-@provider(init_hook=hook)
-def process(settings, file_name):
-    with open(file_name, 'r') as fdata:
-        for line in fdata:
-            label, comment = line.strip().split('\t')
-            label = int(''.join(label.split()))
-            words = comment.split()
-            word_slot = [settings.word_dict[w] for w in words if w in settings.word_dict]
-            yield word_slot, label
-```
- 双层序列的dataprovider如下：
-  - word_slot是integer_value_sub_sequence类型，代表双层序列。
-  - label是integer_value_sequence类型，代表单层序列，即一个子句一个label。注意：也可以为integer_value类型，代表一个向量，即一个句子一个label。通常根据任务需求进行不同设置。
-  - 关于dataprovider中input_types的详细用法，参见PyDataProvider2。
-```python
-def hook2(settings, dict_file, **kwargs):
-    settings.word_dict = dict_file
-    settings.input_types = [integer_value_sub_sequence(len(settings.word_dict)),
-                            integer_value_sequence(3)]
-@provider(init_hook=hook2)
-def process2(settings, file_name):
-    with open(file_name) as fdata:
-        label_list = []
-        word_slot_list = []
-        for line in fdata:
-            if (len(line)) > 1:
-                label,comment = line.strip().split('\t')
-                label = int(''.join(label.split()))
-                words = comment.split()
-                word_slot = [settings.word_dict[w] for w in words if w in settings.word_dict]
-                label_list.append(label)
-                word_slot_list.append(word_slot)
-            else:
-                yield word_slot_list, label_list
-                label_list = []
-                word_slot_list = []
-```
-### 模型中的配置
-首先，我们看一下单层序列的配置（见`sequence_layer_group.conf`）。注意：batchsize=5表示一次过5句单层序列，因此2个batch就可以完成1个pass。
-```python
-settings(batch_size=5)
-data = data_layer(name="word", size=dict_dim)
-emb = embedding_layer(input=data, size=word_dim)
-# (lstm_input + lstm) is equal to lstmemory 
-with mixed_layer(size=hidden_dim*4) as lstm_input:
-    lstm_input += full_matrix_projection(input=emb)
-lstm = lstmemory_group(input=lstm_input,
-                       size=hidden_dim,
-                       act=TanhActivation(),
-                       gate_act=SigmoidActivation(),
-                       state_act=TanhActivation(),
-                       lstm_layer_attr=ExtraLayerAttribute(error_clipping_threshold=50))
-lstm_last = last_seq(input=lstm)
-with mixed_layer(size=label_dim, 
-                 act=SoftmaxActivation(), 
-                 bias_attr=True) as output:
-    output += full_matrix_projection(input=lstm_last)
-outputs(classification_cost(input=output, label=data_layer(name="label", size=1)))
-```
-其次，我们看一下语义相同的双层序列配置（见`sequence_nest_layer_group.conf`），并对其详细分析：
- batchsize=2表示一次过2句双层序列。但从上面的数据格式可知，2句双层序列和5句单层序列的数据完全一样。
- data_layer和embedding_layer不关心数据是否是序列格式，因此两个配置在这两层上的输出是一样的。
- lstmemory:
-  - 单层序列过了一个mixed_layer和lstmemory_group。
-  - 双层序列在同样的mixed_layer和lstmemory_group外，直接加了一层group。由于这个外层group里面没有memory，表示subseq间不存在联系，即起到的作用仅仅是把双层seq拆成单层，因此双层序列过完lstmemory的输出和单层的一样。
- last_seq：
-  - 单层序列直接取了最后一个元素
-  - 双层序列首先（last_seq层）取了每个subseq的最后一个元素，将其拼接成一个新的单层序列；接着（expand_layer层）将其扩展成一个新的双层序列，其中第i个subseq中的所有向量均为输入的单层序列中的第i个向量；最后（average_layer层）取了每个subseq的平均值。
-  - 分析得出：第一个last_seq后，每个subseq的最后一个元素就等于单层序列的最后一个元素，而expand_layer和average_layer后，依然保持每个subseq最后一个元素的值不变（这两层仅是为了展示它们的用法，实际中并不需要）。因此单双层序列的输出是一样旳。
-```python
-settings(batch_size=2)
-data = data_layer(name="word", size=dict_dim)
-emb_group = embedding_layer(input=data, size=word_dim)
-# (lstm_input + lstm) is equal to lstmemory 
-def lstm_group(lstm_group_input):
-    with mixed_layer(size=hidden_dim*4) as group_input:
-      group_input += full_matrix_projection(input=lstm_group_input)
-    lstm_output = lstmemory_group(input=group_input,
-                                  name="lstm_group",
-                                  size=hidden_dim,
-                                  act=TanhActivation(),
-                                  gate_act=SigmoidActivation(),
-                                  state_act=TanhActivation(),
-                                  lstm_layer_attr=ExtraLayerAttribute(error_clipping_threshold=50))
-    return lstm_output
-lstm_nest_group = recurrent_group(input=SubsequenceInput(emb_group),
-                                  step=lstm_group,
-                                  name="lstm_nest_group")
-# hasSubseq ->(seqlastins) seq
-lstm_last = last_seq(input=lstm_nest_group, agg_level=AggregateLevel.EACH_SEQUENCE)
-# seq ->(expand) hasSubseq
-lstm_expand = expand_layer(input=lstm_last, expand_as=emb_group, expand_level=ExpandLevel.FROM_SEQUENCE)
-# hasSubseq ->(average) seq
-lstm_average = pooling_layer(input=lstm_expand,
-                             pooling_type=AvgPooling(),
-                             agg_level=AggregateLevel.EACH_SEQUENCE)
-with mixed_layer(size=label_dim, 
-                 act=SoftmaxActivation(), 
-                 bias_attr=True) as output:
-    output += full_matrix_projection(input=lstm_average)
-outputs(classification_cost(input=output, label=data_layer(name="label", size=1)))
-```
-## 示例2：双进双出，subseq间有memory
-配置：单层RNN（`sequence_rnn.conf`），双层RNN（`sequence_nest_rnn.conf`和`sequence_nest_rnn_readonly_memory.conf`），语义完全相同。
-### 读取双层序列的方法
-我们看一下单双层序列的不同数据组织形式和dataprovider（见`rnn_data_provider.py`）
-```python
-data = [
-    [[[1, 3, 2], [4, 5, 2]], 0],
-    [[[0, 2], [2, 5], [0, 1, 2]], 1],
-]
-@provider(input_types=[integer_value_sub_sequence(10),
-                       integer_value(3)])
-def process_subseq(settings, file_name):
-    for d in data:
-        yield d
-@provider(input_types=[integer_value_sequence(10),
-                       integer_value(3)])
-def process_seq(settings, file_name):
-    for d in data:
-        seq = []
-```
- 单层序列：有两句，分别为[1,3,2,4,5,2]和[0,2,2,5,0,1,2]。
- 双层序列：有两句，分别为[[1,3,2],[4,5,2]]（2个子句）和[[0,2],[2,5],[0,1,2]]（3个子句）。
- 单双层序列的label都分别是0和1
-### 模型中的配置
-我们选取单双层序列配置中的不同部分，来对比分析两者语义相同的原因。
- 单层序列：过了一个很简单的recurrent_group。每一个时间步，当前的输入y和上一个时间步的输出rnn_state做了一个全链接。
-```python
-def step(y):
-    mem = memory(name="rnn_state", size=hidden_dim)
-    return fc_layer(input=[y, mem],
-                    size=hidden_dim,
-                    act=TanhActivation(),
-                    bias_attr=True,
-                    name="rnn_state")
-out = recurrent_group(step=step, input=emb)
-```
- 双层序列，外层memory是一个元素：
-  - 内层inner_step的recurrent_group和单层序列的几乎一样。除了boot_layer=outer_mem，表示将外层的outer_mem作为内层memory的初始状态。外层outer_step中，outer_mem是一个子句的最后一个向量，即整个双层group是将前一个子句的最后一个向量，作为下一个子句memory的初始状态。
-  - 从输入数据上看，单双层序列的句子是一样的，只是双层序列将其又做了子序列划分。因此双层序列的配置中，必须将前一个子句的最后一个元素，作为boot_layer传给下一个子句的memory，才能保证和单层序列的配置中“每一个时间步都用了上一个时间步的输出结果”一致。
-```python
-def outer_step(x):
-    outer_mem = memory(name="outer_rnn_state", size=hidden_dim)
-    def inner_step(y):
-        inner_mem = memory(name="inner_rnn_state",
-                           size=hidden_dim,
-                           boot_layer=outer_mem)
-        return fc_layer(input=[y, inner_mem],
-                        size=hidden_dim,
-                        act=TanhActivation(),
-                        bias_attr=True,
-                        name="inner_rnn_state")
-    inner_rnn_output = recurrent_group(
-        step=inner_step,
-        input=x)
-    last = last_seq(input=inner_rnn_output, name="outer_rnn_state")
-    return inner_rnn_output
-out = recurrent_group(step=outer_step, input=SubsequenceInput(emb))
-```
- 双层序列，外层memory是单层序列：
-  - 由于外层每个时间步返回的是一个子句，这些子句的长度往往不等长。因此当外层有is_seq=True的memory时，内层是**无法直接使用**它的，即内层memory的boot_layer不能链接外层的这个memory。
-  - 如果内层memory想**间接使用**这个外层memory，只能通过`pooling_layer`、`last_seq`或`first_seq`这三个layer将它先变成一个元素。但这种情况下，外层memory必须有boot_layer，否则在第0个时间步时，由于外层memory没有任何seq信息，因此上述三个layer的前向会报出“**Check failed: input.sequenceStartPositions**”的错误。
-## 示例3：双进双出，输入不等长
-TBD
-## 示例4：beam_search的生成
-TBD
\ No newline at end of file
--- a/doc_cn/_sources/algorithm/rnn/rnn-tutorial.txt
+++ b/doc_cn/_sources/algorithm/rnn/rnn-tutorial.txt
-# Recurrent Group教程
-## 概述
-序列数据是自然语言处理任务面对的一种主要输入数据类型。
-一句话是由词语构成的序列，多句话进一步构成了段落。因此，段落可以看作是一个嵌套的双层的序列，这个序列的每个元素又是一个序列。
-双层序列是PaddlePaddle支持的一种非常灵活的数据组织方式，帮助我们更好地描述段落、多轮对话等更为复杂的语言数据。基于双层序列输入，我们可以设计搭建一个灵活的、层次化的RNN，分别从词语和句子级别编码输入数据，同时也能够引入更加复杂的记忆机制，更好地完成一些复杂的语言理解任务。
-在PaddlePaddle中，`recurrent_group`是一种任意复杂的RNN单元，用户只需定义RNN在一个时间步内完成的计算，PaddlePaddle负责完成信息和误差在时间序列上的传播。
-更进一步，`recurrent_group`同样可以扩展到双层序列的处理上。通过两个嵌套的`recurrent_group`分别定义子句级别和词语级别上需要完成的运算，最终实现一个层次化的复杂RNN。
-目前，在PaddlePaddle中，能够对双向序列进行处理的有`recurrent_group`和部分Layer，具体可参考文档：<a href = "hierarchical-layer.html">支持双层序列作为输入的Layer</a>。
-## 相关概念
-### 基本原理
-`recurrent_group` 是PaddlePaddle支持的一种任意复杂的RNN单元。使用者只需要关注于设计RNN在一个时间步之内完成的计算，PaddlePaddle负责完成信息和梯度在时间序列上的传播。
-PaddlePaddle中，`recurrent_group`的一个简单调用如下：
-``` python
-recurrent_group(step, input, reverse)
-```
- step：一个可调用的函数，定义一个时间步之内RNN单元完成的计算
- input：输入，必须是一个单层序列，或者一个双层序列
- reverse：是否以逆序处理输入序列
-使用`recurrent_group`的核心是设计step函数的计算逻辑。step函数内部可以自由组合PaddlePaddle支持的各种layer，完成任意的运算逻辑。`recurrent_group` 的输入（即input）会成为step函数的输入，由于step 函数只关注于RNN一个时间步之内的计算，在这里`recurrent_group`替我们完成了原始输入数据的拆分。
-### 输入
-`recurrent_group`处理的输入序列主要分为以下三种类型：
- **数据输入**：一个双层序列进入`recurrent_group`会被拆解为一个单层序列，一个单层序列进入`recurrent_group`会被拆解为非序列，然后交给step函数，这一过程对用户是完全透明的。可以有以下两种：1）通过data_layer拿到的用户输入；2）其它layer的输出。
- **只读Memory输入**：`StaticInput` 定义了一个只读的Memory，由`StaticInput`指定的输入不会被`recurrent_group`拆解，`recurrent_group` 循环展开的每个时间步总是能够引用所有输入，可以是一个非序列，或者一个单层序列。
- **序列生成任务的输入**：`GeneratedInput`只用于在序列生成任务中指定输入数据。
-### 输入示例
-序列生成任务大多遵循encoder-decoer架构，encoder和decoder可以是能够处理序列的任意神经网络单元，而RNN是最流行的选择。
-给定encoder输出和当前词，decoder每次预测产生下一个最可能的词语。在这种结构中，decoder接受两个输入：
- 要生成的目标序列：是decoder的数据输入，也是decoder循环展开的依据，`recurrent_group`会对这类输入进行拆解。
- encoder输出，可以是一个非序列，或者一个单层序列：是一个unbounded memory，decoder循环展开的每一个时间步会引用全部结果，不应该被拆解，这种类型的输入必须通过`StaticInput`指定。关于Unbounded Memory的更多讨论请参考论文 [Neural Turning Machine](https://arxiv.org/abs/1410.5401)。
-在序列生成任务中，decoder RNN总是引用上一时刻预测出的词的词向量，作为当前时刻输入。`GeneratedInput`自动完成这一过程。
-### 输出
-`step`函数必须返回一个或多个Layer的输出，这个Layer的输出会作为整个`recurrent_group` 最终的输出结果。在输出的过程中，`recurrent_group` 会将每个时间步的输出拼接，这个过程对用户也是透明的。
-### memory
-memory只能在`recurrent_group`中定义和使用。memory不能独立存在，必须指向一个PaddlePaddle定义的Layer。引用memory得到这layer上一时刻输出，因此，可以将memory理解为一个时延操作。
-可以显示地指定一个layer的输出用于初始化memory。不指定时，memory默认初始化为0。
-## 双层RNN介绍
-`recurrent_group`帮助我们完成对输入序列的拆分，对输出的合并，以及计算逻辑在序列上的循环展开。
-利用这种特性，两个嵌套的`recurrent_group`能够处理双层序列，实现词语和句子两个级别的双层RNN结构。
- 单层（word-level）RNN：每个状态（state）对应一个词（word）。
- 双层（sequence-level）RNN：一个双层RNN由多个单层RNN组成，每个单层RNN（即双层RNN的每个状态）对应一个子句（subseq）。
-为了描述方便，下文以NLP任务为例，将含有子句（subseq）的段落定义为一个双层序列，将含有词语的句子定义为一个单层序列，那么0层序列即为一个词语。
-## 双层RNN的使用
-### 训练流程的使用方法
-使用 `recurrent_group`需要遵循以下约定：
- **单进单出**：输入和输出都是单层序列。
-  - 如果有多个输入，不同输入序列含有的词语数必须严格相等。
-  - 输出一个单层序列，输出序列的词语数和输入序列一致。
-  - memory：在step函数中定义 memory指向一个layer，通过引用memory得到这个layer上一个时刻输出，形成recurrent 连接。memory的is_seq参数必须为false。如果没有定义memory，每个时间步之内的运算是独立的。
-  - boot_layer：memory的初始状态，默认初始状为0，memory的is_seq参数必须为false。
- **双进双出**：输入和输出都是双层序列。
-  - 如果有多个输入序列，不同输入含有的子句（subseq）数必须严格相等，但子句含有的词语数可以不相等。
-  - 输出一个双层序列，子句（subseq）数、子句的单词数和指定的一个输入序列一致，默认为第一个输入。
-  - memory：在step函数中定义memory，指向一个layer，通过引用memory得到这个layer上一个时刻的输出，形成recurrent连接。定义在外层`recurrent_group` step函数中的memory，能够记录上一个subseq 的状态，可以是一个单层序列（只作为read-only memory），也可以是一个词语。如果没有定义memory，那么 subseq 之间的运算是独立的。
-  - boot_layer：memory 初始状态，可以是一个单层序列（只作为read-only memory）或一个向量。默认不设置，即初始状态为0。
- **双进单出**：目前还未支持，会报错"In hierachical RNN, all out links should be from sequences now"。
-### 生成流程的使用方法
-使用`beam_search`需要遵循以下约定：
- 单层RNN：从一个word生成下一个word。
- 双层RNN：即把单层RNN生成后的subseq给拼接成一个新的双层seq。从语义上看，也不存在一个subseq直接生成下一个subseq的情况。
\ No newline at end of file
--- a/doc_cn/_sources/index.txt
+++ b/doc_cn/_sources/index.txt
@@ -16,7 +16,4 @@ PaddlePaddle文档
 算法教程
 --------
-* `Recurrent Group教程 <algorithm/rnn/rnn-tutorial.html>`_
+* `RNN配置 <../doc/algorithm/rnn/rnn.html>`_
-* `单层RNN示例 <../doc/algorithm/rnn/rnn.html>`_
-* `双层RNN示例 <algorithm/rnn/hierarchical-rnn.html>`_
-* `支持双层序列作为输入的Layer <algorithm/rnn/hierarchical-layer.html>`_
--- a/doc_cn/algorithm/rnn/hierarchical-layer.html
+++ b/doc_cn/algorithm/rnn/hierarchical-layer.html
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-  <div class="section" id="layer">
-<span id="layer"></span><h1>支持双层序列作为输入的Layer<a class="headerlink" href="#layer" title="Permalink to this headline">¶</a></h1>
-<div class="section" id="">
-<span id="id1"></span><h2>概述<a class="headerlink" href="#" title="Permalink to this headline">¶</a></h2>
-<p>在自然语言处理任务中，序列是一种常见的数据类型。一个独立的词语，可以看作是一个非序列输入，或者，我们称之为一个0层的序列；由词语构成的句子，是一个单层序列；若干个句子构成一个段落，是一个双层的序列。</p>
-<p>双层序列是一个嵌套的序列，它的每一个元素，又是一个单层的序列。这是一种非常灵活的数据组织方式，帮助我们构造一些复杂的输入信息。</p>
-<p>我们可以按照如下层次定义非序列，单层序列，以及双层序列。</p>
-<ul class="simple">
-<li>0层序列：一个独立的元素，类型可以是PaddlePaddle支持的任意输入数据类型</li>
-<li>单层序列：排成一列的多个元素，每个元素是一个0层序列，元素之间的顺序是重要的输入信息</li>
-<li>双层序列：排成一列的多个元素，每个元素是一个单层序列，称之为双层序列的一个子序列（subseq），subseq的每个元素是一个0层序列</li>
-</ul>
-<p>在 PaddlePaddle中，下面这些Layer能够接受双层序列作为输入，完成相应的计算。</p>
-</div>
-<div class="section" id="pooling-layer">
-<span id="pooling-layer"></span><h2>pooling_layer<a class="headerlink" href="#pooling-layer" title="Permalink to this headline">¶</a></h2>
-<p>pooling_layer的使用示例如下，详细见<a href = "../../../doc/ui/api/trainer_config_helpers/layers.html#pooling-layer">配置API</a>。</p>
-<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">seq_pool</span> <span class="o">=</span> <span class="n">pooling_layer</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer</span><span class="p">,</span>
-                         <span class="n">pooling_type</span><span class="o">=</span><span class="n">AvgPooling</span><span class="p">(),</span>
-                         <span class="n">agg_level</span><span class="o">=</span><span class="n">AggregateLevel</span><span class="o">.</span><span class="n">EACH_SEQUENCE</span><span class="p">)</span>
-</pre></div>
-</div>
-<ul class="simple">
-<li><code class="docutils literal"><span class="pre">pooling_type</span></code> 目前支持两种，分别是：MaxPooling()和AvgPooling()。</li>
-<li><code class="docutils literal"><span class="pre">agg_level=AggregateLevel.TIMESTEP</span></code>时（默认值）：<ul>
-<li>作用：双层序列经过运算变成一个0层序列，或单层序列经过运算变成一个0层序列</li>
-<li>输入：一个双层序列，或一个单层序列</li>
-<li>输出：一个0层序列，即整个输入序列（单层或双层）的平均值（或最大值）</li>
-</ul>
-</li>
-<li><code class="docutils literal"><span class="pre">agg_level=AggregateLevel.EACH_SEQUENCE</span></code>时：<ul>
-<li>作用：一个双层序列经过运算变成一个单层序列</li>
-<li>输入：必须是一个双层序列</li>
-<li>输出：一个单层序列，序列的每个元素是原来双层序列每个subseq元素的平均值（或最大值）</li>
-</ul>
-</li>
-</ul>
-</div>
-<div class="section" id="last-seq-first-seq">
-<span id="last-seq-first-seq"></span><h2>last_seq 和 first_seq<a class="headerlink" href="#last-seq-first-seq" title="Permalink to this headline">¶</a></h2>
-<p>last_seq的使用示例如下（first_seq类似），详细见<a href = "../../../doc/ui/api/trainer_config_helpers/layers.html#last-seq">配置API</a>。</p>
-<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">last</span> <span class="o">=</span> <span class="n">last_seq</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer</span><span class="p">,</span>
-                <span class="n">agg_level</span><span class="o">=</span><span class="n">AggregateLevel</span><span class="o">.</span><span class="n">EACH_SEQUENCE</span><span class="p">)</span>
-</pre></div>
-</div>
-<ul class="simple">
-<li><code class="docutils literal"><span class="pre">agg_level=AggregateLevel.TIMESTEP</span></code>时（默认值）：<ul>
-<li>作用：一个双层序列经过运算变成一个0层序列，或一个单层序列经过运算变成一个0层序列</li>
-<li>输入：一个双层序列或一个单层序列</li>
-<li>输出：一个0层序列，即整个输入序列（双层或者单层）最后一个，或第一个元素。</li>
-</ul>
-</li>
-<li><code class="docutils literal"><span class="pre">agg_level=AggregateLevel.EACH_SEQUENCE</span></code>时：<ul>
-<li>作用：一个双层序列经过运算变成一个单层序列</li>
-<li>输入：必须是一个双层序列</li>
-<li>输出：一个单层序列，其中每个元素是双层序列中每个subseq最后一个（或第一个）元素。</li>
-</ul>
-</li>
-</ul>
-</div>
-<div class="section" id="expand-layer">
-<span id="expand-layer"></span><h2>expand_layer<a class="headerlink" href="#expand-layer" title="Permalink to this headline">¶</a></h2>
-<p>expand_layer的使用示例如下，详细见<a href = "../../../doc/ui/api/trainer_config_helpers/layers.html#expand-layer">配置API</a>。</p>
-<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">expand</span> <span class="o">=</span> <span class="n">expand_layer</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer1</span><span class="p">,</span>
-                      <span class="n">expand_as</span><span class="o">=</span><span class="n">layer2</span><span class="p">,</span>
-                      <span class="n">expand_level</span><span class="o">=</span><span class="n">ExpandLevel</span><span class="o">.</span><span class="n">FROM_TIMESTEP</span><span class="p">)</span>
-</pre></div>
-</div>
-<ul class="simple">
-<li><code class="docutils literal"><span class="pre">expand_level=ExpandLevel.FROM_TIMESTEP</span></code>时（默认值）：<ul>
-<li>作用：一个0层序列经过运算扩展成一个单层序列，或者一个双层序列</li>
-<li>输入：layer1必须是一个0层序列，是待扩展的数据；layer2可以是一个单层序列，或者是一个双层序列，提供扩展的长度信息</li>
-<li>输出：一个单层序列，或一个双层序列，输出序列的类型（双层序列，或单层序列）和序列中含有元素的数目同 layer2一致。若输出是单层序列，单层序列的每个元素（0层序列），都是对layer1元素的拷贝；若输出是双层序列，双层序列每个subseq中每个元素（0层序列），都是对layer1元素的拷贝</li>
-</ul>
-</li>
-<li><code class="docutils literal"><span class="pre">expand_level=ExpandLevel.FROM_SEQUENCE</span></code>时：<ul>
-<li>作用：一个单层序列经过运算扩展成一个双层序列</li>
-<li>输入：layer1必须是一个单层序列，是待扩展的数据；layer2必须是一个双层序列，提供扩展的长度信息</li>
-<li>输出：一个双层序列，序列中含有元素的数目同layer2一致。要求单层序列含有元素的数目（0层序列），和双层序列含有subseq 的数目一致。单层序列第i个元素（0层序列），被扩展为一个单层序列，构成了输出双层序列的第i个subseq。</li>
-</ul>
-</li>
-</ul>
-</div>
-</div>
-          </div>
-        </div>
-      </div>
-      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
-        <div class="sphinxsidebarwrapper">
-  <h3><a href="../../index.html">Table Of Contents</a></h3>
-  <ul>
-<li><a class="reference internal" href="#">支持双层序列作为输入的Layer</a><ul>
-<li><a class="reference internal" href="#">概述</a></li>
-<li><a class="reference internal" href="#pooling-layer">pooling_layer</a></li>
-<li><a class="reference internal" href="#last-seq-first-seq">last_seq 和 first_seq</a></li>
-<li><a class="reference internal" href="#expand-layer">expand_layer</a></li>
-</ul>
-</li>
-</ul>
-  <div role="note" aria-label="source link">
-    <h3>This Page</h3>
-    <ul class="this-page-menu">
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-  <div class="section" id="rnn">
-<span id="rnn"></span><h1>双层RNN配置与示例<a class="headerlink" href="#rnn" title="Permalink to this headline">¶</a></h1>
-<p>我们在<code class="docutils literal"><span class="pre">paddle/gserver/tests/test_RecurrentGradientMachine</span></code>单测中，通过多组语义相同的单双层RNN配置，讲解如何使用双层RNN。</p>
-<div class="section" id="subseqmemory">
-<span id="subseqmemory"></span><h2>示例1：双进双出，subseq间无memory<a class="headerlink" href="#subseqmemory" title="Permalink to this headline">¶</a></h2>
-<p>配置：单层RNN（<code class="docutils literal"><span class="pre">sequence_layer_group</span></code>）和双层RNN（<code class="docutils literal"><span class="pre">sequence_nest_layer_group</span></code>），语义完全相同。</p>
-<div class="section" id="">
-<span id="id1"></span><h3>读取双层序列的方法<a class="headerlink" href="#" title="Permalink to this headline">¶</a></h3>
-<p>首先，我们看一下单双层序列的不同数据组织形式（您也可以采用别的组织形式）：</p>
-<ul class="simple">
-<li>单层序列的数据（<code class="docutils literal"><span class="pre">Sequence/tour_train_wdseg</span></code>）如下，一共有10个样本。每个样本由两部分组成，一个label（此处都为2）和一个已经分词后的句子。</li>
-</ul>
-<div class="highlight-text"><div class="highlight"><pre><span></span>2   酒店 有 很 舒适 的 床垫 子 ， 床上用品 也 应该 是 一人 一 换 ， 感觉 很 利落 对 卫生 很 放心 呀 。
-2   很 温馨 ， 也 挺 干净 的 * 地段 不错 ， 出来 就 有 全家 ， 离 地铁站 也 近 ， 交通 很方便 * 就是 都 不 给 刷牙 的 杯子 啊 ， 就 第一天 给 了 一次性杯子 *
-2   位置 方便 ， 强烈推荐 ， 十一 出去玩 的 时候 选 的 ， 对面 就是 华润万家 ， 周围 吃饭 的 也 不少 。
-2   交通便利 ， 吃 很 便利 ， 乾 浄 、 安静 ， 商务 房 有 电脑 、 上网 快 ， 价格 可以 ， 就 早餐 不 好吃 。 整体 是 不错 的 。 適 合 出差 來 住 。
-2   本来 准备 住 两 晚 ， 第 2 天 一早 居然 停电 ， 且 无 通知 ， 只有 口头 道歉 。 总体来说 性价比 尚可 ， 房间 较 新 ， 还是 推荐 .
-2   这个 酒店 去过 很多 次 了 ， 选择 的 主要原因 是 离 客户 最 便宜 相对 又 近 的 酒店
-2   挺好 的 汉庭 ， 前台 服务 很 热情 ， 卫生 很 整洁 ， 房间 安静 ， 水温 适中 ， 挺好 ！
-2   HowardJohnson 的 品质 ， 服务 相当 好 的 一 家 五星级 。 房间 不错 、 泳池 不错 、 楼层 安排 很 合理 。 还有 就是 地理位置 ， 简直 一 流 。 就 在 天一阁 、 月湖 旁边 ， 离 天一广场 也 不远 。 下次 来 宁波 还会 住 。
-2   酒店 很干净 ， 很安静 ， 很 温馨 ， 服务员 服务 好 ， 各方面 都 不错 *
-2   挺好 的 ， 就是 没 窗户 ， 不过 对 得 起 这 价格
-</pre></div>
-</div>
-<ul class="simple">
-<li>双层序列的数据（<code class="docutils literal"><span class="pre">Sequence/tour_train_wdseg.nest</span></code>）如下，一共有4个样本。样本间用空行分开，代表不同的双层序列，序列数据和上面的完全一样。每个样本的子句数分别为2,3,2,3。</li>
-</ul>
-<div class="highlight-text"><div class="highlight"><pre><span></span>2   酒店 有 很 舒适 的 床垫 子 ， 床上用品 也 应该 是 一人 一 换 ， 感觉 很 利落 对 卫生 很 放心 呀 。
-2   很 温馨 ， 也 挺 干净 的 * 地段 不错 ， 出来 就 有 全家 ， 离 地铁站 也 近 ， 交通 很方便 * 就是 都 不 给 刷牙 的 杯子 啊 ， 就 第一天 给 了 一次性杯子 *
-2   位置 方便 ， 强烈推荐 ， 十一 出去玩 的 时候 选 的 ， 对面 就是 华润万家 ， 周围 吃饭 的 也 不少 。
-2   交通便利 ， 吃 很 便利 ， 乾 浄 、 安静 ， 商务 房 有 电脑 、 上网 快 ， 价格 可以 ， 就 早餐 不 好吃 。 整体 是 不错 的 。 適 合 出差 來 住 。
-2   本来 准备 住 两 晚 ， 第 2 天 一早 居然 停电 ， 且 无 通知 ， 只有 口头 道歉 。 总体来说 性价比 尚可 ， 房间 较 新 ， 还是 推荐 .
-2   这个 酒店 去过 很多 次 了 ， 选择 的 主要原因 是 离 客户 最 便宜 相对 又 近 的 酒店
-2   挺好 的 汉庭 ， 前台 服务 很 热情 ， 卫生 很 整洁 ， 房间 安静 ， 水温 适中 ， 挺好 ！
-2   HowardJohnson 的 品质 ， 服务 相当 好 的 一 家 五星级 。 房间 不错 、 泳池 不错 、 楼层 安排 很 合理 。 还有 就是 地理位置 ， 简直 一 流 。 就 在 天一阁 、 月湖 旁边 ， 离 天一广场 也 不远 。 下次 来 宁波 还会 住 。
-2   酒店 很干净 ， 很安静 ， 很 温馨 ， 服务员 服务 好 ， 各方面 都 不错 *
-2   挺好 的 ， 就是 没 窗户 ， 不过 对 得 起 这 价格
-</pre></div>
-</div>
-<p>其次，我们看一下单双层序列的不同dataprovider（见<code class="docutils literal"><span class="pre">sequenceGen.py</span></code>）：</p>
-<ul class="simple">
-<li>单层序列的dataprovider如下：<ul>
-<li>word_slot是integer_value_sequence类型，代表单层序列。</li>
-<li>label是integer_value类型，代表一个向量。</li>
-</ul>
-</li>
-</ul>
-<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">hook</span><span class="p">(</span><span class="n">settings</span><span class="p">,</span> <span class="n">dict_file</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-    <span class="n">settings</span><span class="o">.</span><span class="n">word_dict</span> <span class="o">=</span> <span class="n">dict_file</span>
-    <span class="n">settings</span><span class="o">.</span><span class="n">input_types</span> <span class="o">=</span> <span class="p">[</span><span class="n">integer_value_sequence</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">settings</span><span class="o">.</span><span class="n">word_dict</span><span class="p">)),</span> 
-                            <span class="n">integer_value</span><span class="p">(</span><span class="mi">3</span><span class="p">)]</span>
-<span class="nd">@provider</span><span class="p">(</span><span class="n">init_hook</span><span class="o">=</span><span class="n">hook</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">process</span><span class="p">(</span><span class="n">settings</span><span class="p">,</span> <span class="n">file_name</span><span class="p">):</span>
-    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">file_name</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fdata</span><span class="p">:</span>
-        <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">fdata</span><span class="p">:</span>
-            <span class="n">label</span><span class="p">,</span> <span class="n">comment</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\t</span><span class="s1">&#39;</span><span class="p">)</span>
-            <span class="n">label</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="s1">&#39;&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">label</span><span class="o">.</span><span class="n">split</span><span class="p">()))</span>
-            <span class="n">words</span> <span class="o">=</span> <span class="n">comment</span><span class="o">.</span><span class="n">split</span><span class="p">()</span>
-            <span class="n">word_slot</span> <span class="o">=</span> <span class="p">[</span><span class="n">settings</span><span class="o">.</span><span class="n">word_dict</span><span class="p">[</span><span class="n">w</span><span class="p">]</span> <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">words</span> <span class="k">if</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">settings</span><span class="o">.</span><span class="n">word_dict</span><span class="p">]</span>
-            <span class="k">yield</span> <span class="n">word_slot</span><span class="p">,</span> <span class="n">label</span>
-</pre></div>
-</div>
-<ul class="simple">
-<li>双层序列的dataprovider如下：<ul>
-<li>word_slot是integer_value_sub_sequence类型，代表双层序列。</li>
-<li>label是integer_value_sequence类型，代表单层序列，即一个子句一个label。注意：也可以为integer_value类型，代表一个向量，即一个句子一个label。通常根据任务需求进行不同设置。</li>
-<li>关于dataprovider中input_types的详细用法，参见PyDataProvider2。</li>
-</ul>
-</li>
-</ul>
-<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">hook2</span><span class="p">(</span><span class="n">settings</span><span class="p">,</span> <span class="n">dict_file</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-    <span class="n">settings</span><span class="o">.</span><span class="n">word_dict</span> <span class="o">=</span> <span class="n">dict_file</span>
-    <span class="n">settings</span><span class="o">.</span><span class="n">input_types</span> <span class="o">=</span> <span class="p">[</span><span class="n">integer_value_sub_sequence</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">settings</span><span class="o">.</span><span class="n">word_dict</span><span class="p">)),</span>
-                            <span class="n">integer_value_sequence</span><span class="p">(</span><span class="mi">3</span><span class="p">)]</span>
-<span class="nd">@provider</span><span class="p">(</span><span class="n">init_hook</span><span class="o">=</span><span class="n">hook2</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">process2</span><span class="p">(</span><span class="n">settings</span><span class="p">,</span> <span class="n">file_name</span><span class="p">):</span>
-    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">file_name</span><span class="p">)</span> <span class="k">as</span> <span class="n">fdata</span><span class="p">:</span>
-        <span class="n">label_list</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="n">word_slot_list</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">fdata</span><span class="p">:</span>
-            <span class="k">if</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">line</span><span class="p">))</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                <span class="n">label</span><span class="p">,</span><span class="n">comment</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\t</span><span class="s1">&#39;</span><span class="p">)</span>
-                <span class="n">label</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="s1">&#39;&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">label</span><span class="o">.</span><span class="n">split</span><span class="p">()))</span>
-                <span class="n">words</span> <span class="o">=</span> <span class="n">comment</span><span class="o">.</span><span class="n">split</span><span class="p">()</span>
-                <span class="n">word_slot</span> <span class="o">=</span> <span class="p">[</span><span class="n">settings</span><span class="o">.</span><span class="n">word_dict</span><span class="p">[</span><span class="n">w</span><span class="p">]</span> <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">words</span> <span class="k">if</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">settings</span><span class="o">.</span><span class="n">word_dict</span><span class="p">]</span>
-                <span class="n">label_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">label</span><span class="p">)</span>
-                <span class="n">word_slot_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">word_slot</span><span class="p">)</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="k">yield</span> <span class="n">word_slot_list</span><span class="p">,</span> <span class="n">label_list</span>
-                <span class="n">label_list</span> <span class="o">=</span> <span class="p">[]</span>
-                <span class="n">word_slot_list</span> <span class="o">=</span> <span class="p">[]</span>
-</pre></div>
-</div>
-</div>
-<div class="section" id="">
-<span id="id2"></span><h3>模型中的配置<a class="headerlink" href="#" title="Permalink to this headline">¶</a></h3>
-<p>首先，我们看一下单层序列的配置（见<code class="docutils literal"><span class="pre">sequence_layer_group.conf</span></code>）。注意：batchsize=5表示一次过5句单层序列，因此2个batch就可以完成1个pass。</p>
-<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">settings</span><span class="p">(</span><span class="n">batch_size</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
-<span class="n">data</span> <span class="o">=</span> <span class="n">data_layer</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;word&quot;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">dict_dim</span><span class="p">)</span>
-<span class="n">emb</span> <span class="o">=</span> <span class="n">embedding_layer</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">data</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">word_dim</span><span class="p">)</span>
-<span class="c1"># (lstm_input + lstm) is equal to lstmemory </span>
-<span class="k">with</span> <span class="n">mixed_layer</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="n">hidden_dim</span><span class="o">*</span><span class="mi">4</span><span class="p">)</span> <span class="k">as</span> <span class="n">lstm_input</span><span class="p">:</span>
-    <span class="n">lstm_input</span> <span class="o">+=</span> <span class="n">full_matrix_projection</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">emb</span><span class="p">)</span>
-<span class="n">lstm</span> <span class="o">=</span> <span class="n">lstmemory_group</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">lstm_input</span><span class="p">,</span>
-                       <span class="n">size</span><span class="o">=</span><span class="n">hidden_dim</span><span class="p">,</span>
-                       <span class="n">act</span><span class="o">=</span><span class="n">TanhActivation</span><span class="p">(),</span>
-                       <span class="n">gate_act</span><span class="o">=</span><span class="n">SigmoidActivation</span><span class="p">(),</span>
-                       <span class="n">state_act</span><span class="o">=</span><span class="n">TanhActivation</span><span class="p">(),</span>
-                       <span class="n">lstm_layer_attr</span><span class="o">=</span><span class="n">ExtraLayerAttribute</span><span class="p">(</span><span class="n">error_clipping_threshold</span><span class="o">=</span><span class="mi">50</span><span class="p">))</span>
-<span class="n">lstm_last</span> <span class="o">=</span> <span class="n">last_seq</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">lstm</span><span class="p">)</span>
-<span class="k">with</span> <span class="n">mixed_layer</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="n">label_dim</span><span class="p">,</span> 
-                 <span class="n">act</span><span class="o">=</span><span class="n">SoftmaxActivation</span><span class="p">(),</span> 
-                 <span class="n">bias_attr</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span> <span class="k">as</span> <span class="n">output</span><span class="p">:</span>
-    <span class="n">output</span> <span class="o">+=</span> <span class="n">full_matrix_projection</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">lstm_last</span><span class="p">)</span>
-<span class="n">outputs</span><span class="p">(</span><span class="n">classification_cost</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">output</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">data_layer</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;label&quot;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">1</span><span class="p">)))</span>
-</pre></div>
-</div>
-<p>其次，我们看一下语义相同的双层序列配置（见<code class="docutils literal"><span class="pre">sequence_nest_layer_group.conf</span></code>），并对其详细分析：</p>
-<ul class="simple">
-<li>batchsize=2表示一次过2句双层序列。但从上面的数据格式可知，2句双层序列和5句单层序列的数据完全一样。</li>
-<li>data_layer和embedding_layer不关心数据是否是序列格式，因此两个配置在这两层上的输出是一样的。</li>
-<li>lstmemory:<ul>
-<li>单层序列过了一个mixed_layer和lstmemory_group。</li>
-<li>双层序列在同样的mixed_layer和lstmemory_group外，直接加了一层group。由于这个外层group里面没有memory，表示subseq间不存在联系，即起到的作用仅仅是把双层seq拆成单层，因此双层序列过完lstmemory的输出和单层的一样。</li>
-</ul>
-</li>
-<li>last_seq：<ul>
-<li>单层序列直接取了最后一个元素</li>
-<li>双层序列首先（last_seq层）取了每个subseq的最后一个元素，将其拼接成一个新的单层序列；接着（expand_layer层）将其扩展成一个新的双层序列，其中第i个subseq中的所有向量均为输入的单层序列中的第i个向量；最后（average_layer层）取了每个subseq的平均值。</li>
-<li>分析得出：第一个last_seq后，每个subseq的最后一个元素就等于单层序列的最后一个元素，而expand_layer和average_layer后，依然保持每个subseq最后一个元素的值不变（这两层仅是为了展示它们的用法，实际中并不需要）。因此单双层序列的输出是一样旳。</li>
-</ul>
-</li>
-</ul>
-<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">settings</span><span class="p">(</span><span class="n">batch_size</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
-<span class="n">data</span> <span class="o">=</span> <span class="n">data_layer</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;word&quot;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">dict_dim</span><span class="p">)</span>
-<span class="n">emb_group</span> <span class="o">=</span> <span class="n">embedding_layer</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">data</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">word_dim</span><span class="p">)</span>
-<span class="c1"># (lstm_input + lstm) is equal to lstmemory </span>
-<span class="k">def</span> <span class="nf">lstm_group</span><span class="p">(</span><span class="n">lstm_group_input</span><span class="p">):</span>
-    <span class="k">with</span> <span class="n">mixed_layer</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="n">hidden_dim</span><span class="o">*</span><span class="mi">4</span><span class="p">)</span> <span class="k">as</span> <span class="n">group_input</span><span class="p">:</span>
-      <span class="n">group_input</span> <span class="o">+=</span> <span class="n">full_matrix_projection</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">lstm_group_input</span><span class="p">)</span>
-    <span class="n">lstm_output</span> <span class="o">=</span> <span class="n">lstmemory_group</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">group_input</span><span class="p">,</span>
-                                  <span class="n">name</span><span class="o">=</span><span class="s2">&quot;lstm_group&quot;</span><span class="p">,</span>
-                                  <span class="n">size</span><span class="o">=</span><span class="n">hidden_dim</span><span class="p">,</span>
-                                  <span class="n">act</span><span class="o">=</span><span class="n">TanhActivation</span><span class="p">(),</span>
-                                  <span class="n">gate_act</span><span class="o">=</span><span class="n">SigmoidActivation</span><span class="p">(),</span>
-                                  <span class="n">state_act</span><span class="o">=</span><span class="n">TanhActivation</span><span class="p">(),</span>
-                                  <span class="n">lstm_layer_attr</span><span class="o">=</span><span class="n">ExtraLayerAttribute</span><span class="p">(</span><span class="n">error_clipping_threshold</span><span class="o">=</span><span class="mi">50</span><span class="p">))</span>
-    <span class="k">return</span> <span class="n">lstm_output</span>
-<span class="n">lstm_nest_group</span> <span class="o">=</span> <span class="n">recurrent_group</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">SubsequenceInput</span><span class="p">(</span><span class="n">emb_group</span><span class="p">),</span>
-                                  <span class="n">step</span><span class="o">=</span><span class="n">lstm_group</span><span class="p">,</span>
-                                  <span class="n">name</span><span class="o">=</span><span class="s2">&quot;lstm_nest_group&quot;</span><span class="p">)</span>
-<span class="c1"># hasSubseq -&gt;(seqlastins) seq</span>
-<span class="n">lstm_last</span> <span class="o">=</span> <span class="n">last_seq</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">lstm_nest_group</span><span class="p">,</span> <span class="n">agg_level</span><span class="o">=</span><span class="n">AggregateLevel</span><span class="o">.</span><span class="n">EACH_SEQUENCE</span><span class="p">)</span>
-<span class="c1"># seq -&gt;(expand) hasSubseq</span>
-<span class="n">lstm_expand</span> <span class="o">=</span> <span class="n">expand_layer</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">lstm_last</span><span class="p">,</span> <span class="n">expand_as</span><span class="o">=</span><span class="n">emb_group</span><span class="p">,</span> <span class="n">expand_level</span><span class="o">=</span><span class="n">ExpandLevel</span><span class="o">.</span><span class="n">FROM_SEQUENCE</span><span class="p">)</span>
-<span class="c1"># hasSubseq -&gt;(average) seq</span>
-<span class="n">lstm_average</span> <span class="o">=</span> <span class="n">pooling_layer</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">lstm_expand</span><span class="p">,</span>
-                             <span class="n">pooling_type</span><span class="o">=</span><span class="n">AvgPooling</span><span class="p">(),</span>
-                             <span class="n">agg_level</span><span class="o">=</span><span class="n">AggregateLevel</span><span class="o">.</span><span class="n">EACH_SEQUENCE</span><span class="p">)</span>
-<span class="k">with</span> <span class="n">mixed_layer</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="n">label_dim</span><span class="p">,</span> 
-                 <span class="n">act</span><span class="o">=</span><span class="n">SoftmaxActivation</span><span class="p">(),</span> 
-                 <span class="n">bias_attr</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span> <span class="k">as</span> <span class="n">output</span><span class="p">:</span>
-    <span class="n">output</span> <span class="o">+=</span> <span class="n">full_matrix_projection</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">lstm_average</span><span class="p">)</span>
-<span class="n">outputs</span><span class="p">(</span><span class="n">classification_cost</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">output</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">data_layer</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;label&quot;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">1</span><span class="p">)))</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="section" id="subseqmemory">
-<span id="id3"></span><h2>示例2：双进双出，subseq间有memory<a class="headerlink" href="#subseqmemory" title="Permalink to this headline">¶</a></h2>
-<p>配置：单层RNN（<code class="docutils literal"><span class="pre">sequence_rnn.conf</span></code>），双层RNN（<code class="docutils literal"><span class="pre">sequence_nest_rnn.conf</span></code>和<code class="docutils literal"><span class="pre">sequence_nest_rnn_readonly_memory.conf</span></code>），语义完全相同。</p>
-<div class="section" id="">
-<span id="id4"></span><h3>读取双层序列的方法<a class="headerlink" href="#" title="Permalink to this headline">¶</a></h3>
-<p>我们看一下单双层序列的不同数据组织形式和dataprovider（见<code class="docutils literal"><span class="pre">rnn_data_provider.py</span></code>）</p>
-<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="p">[[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">2</span><span class="p">]],</span> <span class="mi">0</span><span class="p">],</span>
-    <span class="p">[[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]],</span> <span class="mi">1</span><span class="p">],</span>
-<span class="p">]</span>
-<span class="nd">@provider</span><span class="p">(</span><span class="n">input_types</span><span class="o">=</span><span class="p">[</span><span class="n">integer_value_sub_sequence</span><span class="p">(</span><span class="mi">10</span><span class="p">),</span>
-                       <span class="n">integer_value</span><span class="p">(</span><span class="mi">3</span><span class="p">)])</span>
-<span class="k">def</span> <span class="nf">process_subseq</span><span class="p">(</span><span class="n">settings</span><span class="p">,</span> <span class="n">file_name</span><span class="p">):</span>
-    <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">data</span><span class="p">:</span>
-        <span class="k">yield</span> <span class="n">d</span>
-<span class="nd">@provider</span><span class="p">(</span><span class="n">input_types</span><span class="o">=</span><span class="p">[</span><span class="n">integer_value_sequence</span><span class="p">(</span><span class="mi">10</span><span class="p">),</span>
-                       <span class="n">integer_value</span><span class="p">(</span><span class="mi">3</span><span class="p">)])</span>
-<span class="k">def</span> <span class="nf">process_seq</span><span class="p">(</span><span class="n">settings</span><span class="p">,</span> <span class="n">file_name</span><span class="p">):</span>
-    <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">data</span><span class="p">:</span>
-        <span class="n">seq</span> <span class="o">=</span> <span class="p">[]</span>
-</pre></div>
-</div>
-<ul class="simple">
-<li>单层序列：有两句，分别为[1,3,2,4,5,2]和[0,2,2,5,0,1,2]。</li>
-<li>双层序列：有两句，分别为[[1,3,2],[4,5,2]]（2个子句）和[[0,2],[2,5],[0,1,2]]（3个子句）。</li>
-<li>单双层序列的label都分别是0和1</li>
-</ul>
-</div>
-<div class="section" id="">
-<span id="id5"></span><h3>模型中的配置<a class="headerlink" href="#" title="Permalink to this headline">¶</a></h3>
-<p>我们选取单双层序列配置中的不同部分，来对比分析两者语义相同的原因。</p>
-<ul class="simple">
-<li>单层序列：过了一个很简单的recurrent_group。每一个时间步，当前的输入y和上一个时间步的输出rnn_state做了一个全链接。</li>
-</ul>
-<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">step</span><span class="p">(</span><span class="n">y</span><span class="p">):</span>
-    <span class="n">mem</span> <span class="o">=</span> <span class="n">memory</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;rnn_state&quot;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">hidden_dim</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">fc_layer</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="p">[</span><span class="n">y</span><span class="p">,</span> <span class="n">mem</span><span class="p">],</span>
-                    <span class="n">size</span><span class="o">=</span><span class="n">hidden_dim</span><span class="p">,</span>
-                    <span class="n">act</span><span class="o">=</span><span class="n">TanhActivation</span><span class="p">(),</span>
-                    <span class="n">bias_attr</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span>
-                    <span class="n">name</span><span class="o">=</span><span class="s2">&quot;rnn_state&quot;</span><span class="p">)</span>
-<span class="n">out</span> <span class="o">=</span> <span class="n">recurrent_group</span><span class="p">(</span><span class="n">step</span><span class="o">=</span><span class="n">step</span><span class="p">,</span> <span class="nb">input</span><span class="o">=</span><span class="n">emb</span><span class="p">)</span>
-</pre></div>
-</div>
-<ul class="simple">
-<li>双层序列，外层memory是一个元素：<ul>
-<li>内层inner_step的recurrent_group和单层序列的几乎一样。除了boot_layer=outer_mem，表示将外层的outer_mem作为内层memory的初始状态。外层outer_step中，outer_mem是一个子句的最后一个向量，即整个双层group是将前一个子句的最后一个向量，作为下一个子句memory的初始状态。</li>
-<li>从输入数据上看，单双层序列的句子是一样的，只是双层序列将其又做了子序列划分。因此双层序列的配置中，必须将前一个子句的最后一个元素，作为boot_layer传给下一个子句的memory，才能保证和单层序列的配置中“每一个时间步都用了上一个时间步的输出结果”一致。</li>
-</ul>
-</li>
-</ul>
-<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">outer_step</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
-    <span class="n">outer_mem</span> <span class="o">=</span> <span class="n">memory</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;outer_rnn_state&quot;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">hidden_dim</span><span class="p">)</span>
-    <span class="k">def</span> <span class="nf">inner_step</span><span class="p">(</span><span class="n">y</span><span class="p">):</span>
-        <span class="n">inner_mem</span> <span class="o">=</span> <span class="n">memory</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;inner_rnn_state&quot;</span><span class="p">,</span>
-                           <span class="n">size</span><span class="o">=</span><span class="n">hidden_dim</span><span class="p">,</span>
-                           <span class="n">boot_layer</span><span class="o">=</span><span class="n">outer_mem</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">fc_layer</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="p">[</span><span class="n">y</span><span class="p">,</span> <span class="n">inner_mem</span><span class="p">],</span>
-                        <span class="n">size</span><span class="o">=</span><span class="n">hidden_dim</span><span class="p">,</span>
-                        <span class="n">act</span><span class="o">=</span><span class="n">TanhActivation</span><span class="p">(),</span>
-                        <span class="n">bias_attr</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span>
-                        <span class="n">name</span><span class="o">=</span><span class="s2">&quot;inner_rnn_state&quot;</span><span class="p">)</span>
-    <span class="n">inner_rnn_output</span> <span class="o">=</span> <span class="n">recurrent_group</span><span class="p">(</span>
-        <span class="n">step</span><span class="o">=</span><span class="n">inner_step</span><span class="p">,</span>
-        <span class="nb">input</span><span class="o">=</span><span class="n">x</span><span class="p">)</span>
-    <span class="n">last</span> <span class="o">=</span> <span class="n">last_seq</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">inner_rnn_output</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;outer_rnn_state&quot;</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">inner_rnn_output</span>
-<span class="n">out</span> <span class="o">=</span> <span class="n">recurrent_group</span><span class="p">(</span><span class="n">step</span><span class="o">=</span><span class="n">outer_step</span><span class="p">,</span> <span class="nb">input</span><span class="o">=</span><span class="n">SubsequenceInput</span><span class="p">(</span><span class="n">emb</span><span class="p">))</span>
-</pre></div>
-</div>
-<ul class="simple">
-<li>双层序列，外层memory是单层序列：<ul>
-<li>由于外层每个时间步返回的是一个子句，这些子句的长度往往不等长。因此当外层有is_seq=True的memory时，内层是<strong>无法直接使用</strong>它的，即内层memory的boot_layer不能链接外层的这个memory。</li>
-<li>如果内层memory想<strong>间接使用</strong>这个外层memory，只能通过<code class="docutils literal"><span class="pre">pooling_layer</span></code>、<code class="docutils literal"><span class="pre">last_seq</span></code>或<code class="docutils literal"><span class="pre">first_seq</span></code>这三个layer将它先变成一个元素。但这种情况下，外层memory必须有boot_layer，否则在第0个时间步时，由于外层memory没有任何seq信息，因此上述三个layer的前向会报出“<strong>Check failed: input.sequenceStartPositions</strong>”的错误。</li>
-</ul>
-</li>
-</ul>
-</div>
-</div>
-<div class="section" id="">
-<span id="id6"></span><h2>示例3：双进双出，输入不等长<a class="headerlink" href="#" title="Permalink to this headline">¶</a></h2>
-<p>TBD</p>
-</div>
-<div class="section" id="beam-search">
-<span id="beam-search"></span><h2>示例4：beam_search的生成<a class="headerlink" href="#beam-search" title="Permalink to this headline">¶</a></h2>
-<p>TBD</p>
-</div>
-</div>
-          </div>
-        </div>
-      </div>
-      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
-        <div class="sphinxsidebarwrapper">
-  <h3><a href="../../index.html">Table Of Contents</a></h3>
-  <ul>
-<li><a class="reference internal" href="#">双层RNN配置与示例</a><ul>
-<li><a class="reference internal" href="#subseqmemory">示例1：双进双出，subseq间无memory</a><ul>
-<li><a class="reference internal" href="#">读取双层序列的方法</a></li>
-<li><a class="reference internal" href="#">模型中的配置</a></li>
-</ul>
-</li>
-<li><a class="reference internal" href="#subseqmemory">示例2：双进双出，subseq间有memory</a><ul>
-<li><a class="reference internal" href="#">读取双层序列的方法</a></li>
-<li><a class="reference internal" href="#">模型中的配置</a></li>
-</ul>
-</li>
-<li><a class="reference internal" href="#">示例3：双进双出，输入不等长</a></li>
-<li><a class="reference internal" href="#beam-search">示例4：beam_search的生成</a></li>
-</ul>
-</li>
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-  <div class="section" id="recurrent-group">
-<span id="recurrent-group"></span><h1>Recurrent Group教程<a class="headerlink" href="#recurrent-group" title="Permalink to this headline">¶</a></h1>
-<div class="section" id="">
-<span id="id1"></span><h2>概述<a class="headerlink" href="#" title="Permalink to this headline">¶</a></h2>
-<p>序列数据是自然语言处理任务面对的一种主要输入数据类型。</p>
-<p>一句话是由词语构成的序列，多句话进一步构成了段落。因此，段落可以看作是一个嵌套的双层的序列，这个序列的每个元素又是一个序列。</p>
-<p>双层序列是PaddlePaddle支持的一种非常灵活的数据组织方式，帮助我们更好地描述段落、多轮对话等更为复杂的语言数据。基于双层序列输入，我们可以设计搭建一个灵活的、层次化的RNN，分别从词语和句子级别编码输入数据，同时也能够引入更加复杂的记忆机制，更好地完成一些复杂的语言理解任务。</p>
-<p>在PaddlePaddle中，<code class="docutils literal"><span class="pre">recurrent_group</span></code>是一种任意复杂的RNN单元，用户只需定义RNN在一个时间步内完成的计算，PaddlePaddle负责完成信息和误差在时间序列上的传播。</p>
-<p>更进一步，<code class="docutils literal"><span class="pre">recurrent_group</span></code>同样可以扩展到双层序列的处理上。通过两个嵌套的<code class="docutils literal"><span class="pre">recurrent_group</span></code>分别定义子句级别和词语级别上需要完成的运算，最终实现一个层次化的复杂RNN。</p>
-<p>目前，在PaddlePaddle中，能够对双向序列进行处理的有<code class="docutils literal"><span class="pre">recurrent_group</span></code>和部分Layer，具体可参考文档：<a href = "hierarchical-layer.html">支持双层序列作为输入的Layer</a>。</p>
-</div>
-<div class="section" id="">
-<span id="id2"></span><h2>相关概念<a class="headerlink" href="#" title="Permalink to this headline">¶</a></h2>
-<div class="section" id="">
-<span id="id3"></span><h3>基本原理<a class="headerlink" href="#" title="Permalink to this headline">¶</a></h3>
-<p><code class="docutils literal"><span class="pre">recurrent_group</span></code> 是PaddlePaddle支持的一种任意复杂的RNN单元。使用者只需要关注于设计RNN在一个时间步之内完成的计算，PaddlePaddle负责完成信息和梯度在时间序列上的传播。</p>
-<p>PaddlePaddle中，<code class="docutils literal"><span class="pre">recurrent_group</span></code>的一个简单调用如下：</p>
-<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">recurrent_group</span><span class="p">(</span><span class="n">step</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">reverse</span><span class="p">)</span>
-</pre></div>
-</div>
-<ul class="simple">
-<li>step：一个可调用的函数，定义一个时间步之内RNN单元完成的计算</li>
-<li>input：输入，必须是一个单层序列，或者一个双层序列</li>
-<li>reverse：是否以逆序处理输入序列</li>
-</ul>
-<p>使用<code class="docutils literal"><span class="pre">recurrent_group</span></code>的核心是设计step函数的计算逻辑。step函数内部可以自由组合PaddlePaddle支持的各种layer，完成任意的运算逻辑。<code class="docutils literal"><span class="pre">recurrent_group</span></code> 的输入（即input）会成为step函数的输入，由于step 函数只关注于RNN一个时间步之内的计算，在这里<code class="docutils literal"><span class="pre">recurrent_group</span></code>替我们完成了原始输入数据的拆分。</p>
-</div>
-<div class="section" id="">
-<span id="id4"></span><h3>输入<a class="headerlink" href="#" title="Permalink to this headline">¶</a></h3>
-<p><code class="docutils literal"><span class="pre">recurrent_group</span></code>处理的输入序列主要分为以下三种类型：</p>
-<ul class="simple">
-<li><strong>数据输入</strong>：一个双层序列进入<code class="docutils literal"><span class="pre">recurrent_group</span></code>会被拆解为一个单层序列，一个单层序列进入<code class="docutils literal"><span class="pre">recurrent_group</span></code>会被拆解为非序列，然后交给step函数，这一过程对用户是完全透明的。可以有以下两种：1）通过data_layer拿到的用户输入；2）其它layer的输出。</li>
-<li><strong>只读Memory输入</strong>：<code class="docutils literal"><span class="pre">StaticInput</span></code> 定义了一个只读的Memory，由<code class="docutils literal"><span class="pre">StaticInput</span></code>指定的输入不会被<code class="docutils literal"><span class="pre">recurrent_group</span></code>拆解，<code class="docutils literal"><span class="pre">recurrent_group</span></code> 循环展开的每个时间步总是能够引用所有输入，可以是一个非序列，或者一个单层序列。</li>
-<li><strong>序列生成任务的输入</strong>：<code class="docutils literal"><span class="pre">GeneratedInput</span></code>只用于在序列生成任务中指定输入数据。</li>
-</ul>
-</div>
-<div class="section" id="">
-<span id="id5"></span><h3>输入示例<a class="headerlink" href="#" title="Permalink to this headline">¶</a></h3>
-<p>序列生成任务大多遵循encoder-decoer架构，encoder和decoder可以是能够处理序列的任意神经网络单元，而RNN是最流行的选择。</p>
-<p>给定encoder输出和当前词，decoder每次预测产生下一个最可能的词语。在这种结构中，decoder接受两个输入：</p>
-<ul class="simple">
-<li>要生成的目标序列：是decoder的数据输入，也是decoder循环展开的依据，<code class="docutils literal"><span class="pre">recurrent_group</span></code>会对这类输入进行拆解。</li>
-<li>encoder输出，可以是一个非序列，或者一个单层序列：是一个unbounded memory，decoder循环展开的每一个时间步会引用全部结果，不应该被拆解，这种类型的输入必须通过<code class="docutils literal"><span class="pre">StaticInput</span></code>指定。关于Unbounded Memory的更多讨论请参考论文 <a class="reference external" href="https://arxiv.org/abs/1410.5401">Neural Turning Machine</a>。</li>
-</ul>
-<p>在序列生成任务中，decoder RNN总是引用上一时刻预测出的词的词向量，作为当前时刻输入。<code class="docutils literal"><span class="pre">GeneratedInput</span></code>自动完成这一过程。</p>
-</div>
-<div class="section" id="">
-<span id="id6"></span><h3>输出<a class="headerlink" href="#" title="Permalink to this headline">¶</a></h3>
-<p><code class="docutils literal"><span class="pre">step</span></code>函数必须返回一个或多个Layer的输出，这个Layer的输出会作为整个<code class="docutils literal"><span class="pre">recurrent_group</span></code> 最终的输出结果。在输出的过程中，<code class="docutils literal"><span class="pre">recurrent_group</span></code> 会将每个时间步的输出拼接，这个过程对用户也是透明的。</p>
-</div>
-<div class="section" id="memory">
-<span id="memory"></span><h3>memory<a class="headerlink" href="#memory" title="Permalink to this headline">¶</a></h3>
-<p>memory只能在<code class="docutils literal"><span class="pre">recurrent_group</span></code>中定义和使用。memory不能独立存在，必须指向一个PaddlePaddle定义的Layer。引用memory得到这layer上一时刻输出，因此，可以将memory理解为一个时延操作。</p>
-<p>可以显示地指定一个layer的输出用于初始化memory。不指定时，memory默认初始化为0。</p>
-</div>
-</div>
-<div class="section" id="rnn">
-<span id="rnn"></span><h2>双层RNN介绍<a class="headerlink" href="#rnn" title="Permalink to this headline">¶</a></h2>
-<p><code class="docutils literal"><span class="pre">recurrent_group</span></code>帮助我们完成对输入序列的拆分，对输出的合并，以及计算逻辑在序列上的循环展开。</p>
-<p>利用这种特性，两个嵌套的<code class="docutils literal"><span class="pre">recurrent_group</span></code>能够处理双层序列，实现词语和句子两个级别的双层RNN结构。</p>
-<ul class="simple">
-<li>单层（word-level）RNN：每个状态（state）对应一个词（word）。</li>
-<li>双层（sequence-level）RNN：一个双层RNN由多个单层RNN组成，每个单层RNN（即双层RNN的每个状态）对应一个子句（subseq）。</li>
-</ul>
-<p>为了描述方便，下文以NLP任务为例，将含有子句（subseq）的段落定义为一个双层序列，将含有词语的句子定义为一个单层序列，那么0层序列即为一个词语。</p>
-</div>
-<div class="section" id="rnn">
-<span id="id7"></span><h2>双层RNN的使用<a class="headerlink" href="#rnn" title="Permalink to this headline">¶</a></h2>
-<div class="section" id="">
-<span id="id8"></span><h3>训练流程的使用方法<a class="headerlink" href="#" title="Permalink to this headline">¶</a></h3>
-<p>使用 <code class="docutils literal"><span class="pre">recurrent_group</span></code>需要遵循以下约定：</p>
-<ul class="simple">
-<li><strong>单进单出</strong>：输入和输出都是单层序列。<ul>
-<li>如果有多个输入，不同输入序列含有的词语数必须严格相等。</li>
-<li>输出一个单层序列，输出序列的词语数和输入序列一致。</li>
-<li>memory：在step函数中定义 memory指向一个layer，通过引用memory得到这个layer上一个时刻输出，形成recurrent 连接。memory的is_seq参数必须为false。如果没有定义memory，每个时间步之内的运算是独立的。</li>
-<li>boot_layer：memory的初始状态，默认初始状为0，memory的is_seq参数必须为false。</li>
-</ul>
-</li>
-<li><strong>双进双出</strong>：输入和输出都是双层序列。<ul>
-<li>如果有多个输入序列，不同输入含有的子句（subseq）数必须严格相等，但子句含有的词语数可以不相等。</li>
-<li>输出一个双层序列，子句（subseq）数、子句的单词数和指定的一个输入序列一致，默认为第一个输入。</li>
-<li>memory：在step函数中定义memory，指向一个layer，通过引用memory得到这个layer上一个时刻的输出，形成recurrent连接。定义在外层<code class="docutils literal"><span class="pre">recurrent_group</span></code> step函数中的memory，能够记录上一个subseq 的状态，可以是一个单层序列（只作为read-only memory），也可以是一个词语。如果没有定义memory，那么 subseq 之间的运算是独立的。</li>
-<li>boot_layer：memory 初始状态，可以是一个单层序列（只作为read-only memory）或一个向量。默认不设置，即初始状态为0。</li>
-</ul>
-</li>
-<li><strong>双进单出</strong>：目前还未支持，会报错&#8221;In hierachical RNN, all out links should be from sequences now&#8221;。</li>
-</ul>
-</div>
-<div class="section" id="">
-<span id="id9"></span><h3>生成流程的使用方法<a class="headerlink" href="#" title="Permalink to this headline">¶</a></h3>
-<p>使用<code class="docutils literal"><span class="pre">beam_search</span></code>需要遵循以下约定：</p>
-<ul class="simple">
-<li>单层RNN：从一个word生成下一个word。</li>
-<li>双层RNN：即把单层RNN生成后的subseq给拼接成一个新的双层seq。从语义上看，也不存在一个subseq直接生成下一个subseq的情况。</li>
-</ul>
-</div>
-</div>
-</div>
-          </div>
-        </div>
-      </div>
-      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
-        <div class="sphinxsidebarwrapper">
-  <h3><a href="../../index.html">Table Of Contents</a></h3>
-  <ul>
-<li><a class="reference internal" href="#">Recurrent Group教程</a><ul>
-<li><a class="reference internal" href="#">概述</a></li>
-<li><a class="reference internal" href="#">相关概念</a><ul>
-<li><a class="reference internal" href="#">基本原理</a></li>
-<li><a class="reference internal" href="#">输入</a></li>
-<li><a class="reference internal" href="#">输入示例</a></li>
-<li><a class="reference internal" href="#">输出</a></li>
-<li><a class="reference internal" href="#memory">memory</a></li>
-</ul>
-</li>
-<li><a class="reference internal" href="#rnn">双层RNN介绍</a></li>
-<li><a class="reference internal" href="#rnn">双层RNN的使用</a><ul>
-<li><a class="reference internal" href="#">训练流程的使用方法</a></li>
-<li><a class="reference internal" href="#">生成流程的使用方法</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-  <div role="note" aria-label="source link">
-    <h3>This Page</h3>
-    <ul class="this-page-menu">
-      <li><a href="../../_sources/algorithm/rnn/rnn-tutorial.txt"
-            rel="nofollow">Show Source</a></li>
-    </ul>
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-  <h3>Quick search</h3>
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\ No newline at end of file
--- a/doc_cn/index.html
+++ b/doc_cn/index.html
@@ -78,10 +78,7 @@ var _hmt = _hmt || [];
 <div class="section" id="id9">
 <h2>算法教程<a class="headerlink" href="#id9" title="Permalink to this headline">¶</a></h2>
 <ul class="simple">
-<li><a class="reference external" href="algorithm/rnn/rnn-tutorial.html">Recurrent Group教程</a></li>
+<li><a class="reference external" href="../doc/algorithm/rnn/rnn.html">RNN配置</a></li>
-<li><a class="reference external" href="../doc/algorithm/rnn/rnn.html">单层RNN示例</a></li>
-<li><a class="reference external" href="algorithm/rnn/hierarchical-rnn.html">双层RNN示例</a></li>
-<li><a class="reference external" href="algorithm/rnn/hierarchical-layer.html">支持双层序列作为输入的Layer</a></li>
 </ul>
 </div>
 </div>

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