Recurrent Layer Group
+
+
memory
+
+
+paddle.trainer_config_helpers.layers.memory( name , size , is_seq=False , boot_layer=None , boot_bias=None , boot_bias_active_type=None , boot_with_const_id=None )
+The memory layers is a layer cross each time step. Reference this output
+as previous time step layer name
+The default memory is zero in first time step, previous time step’s
+output in the rest time steps.
+If boot_bias, the first time step value is this bias and
+with activation.
+If boot_with_const_id, then the first time stop is a IndexSlot, the
+Arguments.ids()[0] is this cost_id
+If boot_layer is not null, the memory is just the boot_layer’s output.
+Set is_seq
+The same name layer in recurrent group will set memory on each time
+step.
+
+
+Parameters:
+name (basestring ) – memory’s name.size (int ) – size of memory.is_seq (bool ) – is sequence for boot_layerboot_layer (LayerOutput|None ) – boot layer of memory.boot_bias (ParameterAttribute|None ) – boot layer’s biasboot_bias_active_type (BaseActivation ) – boot layer’s active type.boot_with_const_id (int ) – boot layer’s id.
+Returns: LayerOutput object which is a memory.
+Return type: LayerOutput
+
+
+
+
+
recurrent_group
@@ -2647,6 +2690,7 @@ It is used by recurrent layer group.
Recurrent Layer Group
+memory recurrent_group beam_search get_output_layer
Recurrent Layer Group
+memory recurrent_group beam_search get_output_layer
+
diff --git a/doc_cn/_sources/algorithm/rnn/hierarchical-layer.txt b/doc_cn/_sources/algorithm/rnn/hierarchical-layer.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5282bbbcb82d00f5aed7b784d2bd44f9ec33fa42
--- /dev/null
+++ b/doc_cn/_sources/algorithm/rnn/hierarchical-layer.txt
@@ -0,0 +1,66 @@
+# 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，详细见配置API 。
+```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），详细见配置API 。
+```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，详细见配置API 。
+```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
diff --git a/doc_cn/_sources/algorithm/rnn/hierarchical-rnn.txt b/doc_cn/_sources/algorithm/rnn/hierarchical-rnn.txt
new file mode 100644
index 0000000000000000000000000000000000000000..979fe13e2ecbdef908b127a44a4e20542fdf2deb
--- /dev/null
+++ b/doc_cn/_sources/algorithm/rnn/hierarchical-rnn.txt
@@ -0,0 +1,267 @@
+# 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
diff --git a/doc_cn/_sources/algorithm/rnn/rnn-tutorial.txt b/doc_cn/_sources/algorithm/rnn/rnn-tutorial.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7a553054c80392946ba5b16cc31bcaea18cfc977
--- /dev/null
+++ b/doc_cn/_sources/algorithm/rnn/rnn-tutorial.txt
@@ -0,0 +1,96 @@
+# 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：支持双层序列作为输入的Layer 。
+
+## 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
diff --git a/doc_cn/_sources/faq/index.txt b/doc_cn/_sources/faq/index.txt
new file mode 100644
index 0000000000000000000000000000000000000000..283607957ce63099a61d220478728654e993fe9a
--- /dev/null
+++ b/doc_cn/_sources/faq/index.txt
@@ -0,0 +1,169 @@
+####################
+PaddlePaddle常见问题
+####################
+
+.. contents::
+
+1. 如何减少PaddlePaddle的内存占用
+---------------------------------
+
+神经网络的训练本身是一个非常消耗内存和显存的工作。经常会消耗数十G的内存和数G的显存。
+PaddlePaddle的内存占用主要分为如下几个方面\:
+
+* DataProvider缓冲池内存 (只针对内存)
+* 神经元激活内存 （针对内存和显存）
+* 参数内存 (针对内存和显存)
+* 其他内存杂项
+
+这其中，其他内存杂项是指PaddlePaddle本身所用的一些内存，包括字符串分配，临时变量等等，
+这些内存就不考虑如何缩减了。
+
+其他的内存的减少方法依次为
+
+
+减少DataProvider缓冲池内存
+++++++++++++++++++++++++++
+
+PyDataProvider使用的是异步加载，同时在内存里直接随即选取数据来做Shuffle。即
+
+.. graphviz::
+
+ digraph {
+ rankdir=LR;
+ 数据文件 -> 内存池 -> PaddlePaddle训练
+ }
+
+所以，减小这个内存池即可减小内存占用，同时也可以加速开始训练前数据载入的过程。但是，这
+个内存池实际上决定了shuffle的粒度。所以，如果将这个内存池减小，又要保证数据是随机的，
+那么最好将数据文件在每次读取之前做一次shuffle。可能的代码为
+
+.. literalinclude:: reduce_min_pool_size.py
+
+这样做可以极大的减少内存占用，并且可能会加速训练过程。 详细文档参考 `这里
+<../ui/data_provider/pydataprovider2.html#provider>`_ 。
+
+神经元激活内存
+++++++++++++++
+
+神经网络在训练的时候，会对每一个激活暂存一些数据，包括激活，參差等等。
+在反向传递的时候，这些数据会被用来更新参数。这些数据使用的内存主要和两个参数有关系，
+一是batch size，另一个是每条序列(Sequence)长度。所以，其实也是和每个mini-batch中包含
+的时间步信息成正比。
+
+所以，做法可以有两种。他们是
+
+* 减小batch size。 即在网络配置中 :code:`settings(batch_size=1000)` 设置成一个小一些的值。但是batch size本身是神经网络的超参数，减小batch size可能会对训练结果产生影响。
+* 减小序列的长度，或者直接扔掉非常长的序列。比如，一个数据集大部分序列长度是100-200,
+ 但是突然有一个10000长的序列，就很容易导致内存超限。特别是在LSTM等RNN中。
+
+参数内存
+++++++++
+
+PaddlePaddle支持非常多的优化算法(Optimizer)，不同的优化算法需要使用不同大小的内存。
+例如如果使用 :code:`adadelta` 算法，则需要使用参数规模大约5倍的内存。 如果参数保存下来的
+文件为 :code:`100M`， 那么该优化算法至少需要 :code:`500M` 的内存。
+
+可以考虑使用一些优化算法，例如 :code:`momentum`。
+
+2. 如何加速PaddlePaddle的训练速度
+---------------------------------
+
+PaddlePaddle是神经网络训练平台，加速PaddlePaddle训练有如下几个方面\：
+
+* 减少数据载入的耗时
+* 加速训练速度
+* 利用更多的计算资源
+
+减少数据载入的耗时
+++++++++++++++++++
+
+使用 :code:`pydataprovider`时，可以减少缓存池的大小，同时设置内存缓存功能，即可以极大的加速数据载入流程。
+:code:`DataProvider` 缓存池的减小，和之前减小通过减小缓存池来减小内存占用的原理一致。
+
+.. literalinclude:: reduce_min_pool_size.py
+
+同时 :code:`@provider` 接口有一个 :code:`cache` 参数来控制缓存方法，将其设置成 :code:`CacheType.CACHE_PASS_IN_MEM` 的话，会将第一个 :code:`pass` (过完所有训练数据即为一个pass)生成的数据缓存在内存里，在之后的 :code:`pass` 中，不会再从 :code:`python` 端读取数据，而是直接从内存的缓存里读取数据。这也会极大减少数据读入的耗时。
+
+
+加速训练速度
+++++++++++++
+
+PaddlePaddle支持Sparse的训练，sparse训练需要训练特征是 :code:`sparse_binary_vector` 、 :code:`sparse_vector` 、或者 :code:`integer_value` 的任一一种。同时，与这个训练数据交互的Layer，需要将其Parameter设置成 sparse 更新模式，即设置 :code:`sparse_update=True`
+
+这里使用简单的 :code:`word2vec` 训练语言模型距离，具体使用方法为\:
+
+使用一个词前两个词和后两个词，来预测这个中间的词。这个任务的DataProvider为\:
+
+.. literalinclude:: word2vec_dataprovider.py
+
+这个任务的配置为\:
+
+.. literalinclude:: word2vec_config.py
+
+更多关于sparse训练的内容请参考 `sparse训练的文档 `_
+
+利用更多的计算资源
+++++++++++++++++++
+
+利用更多的计算资源可以分为一下几个方式来进行\:
+
+* 单机CPU训练
+ * 使用多线程训练。设置命令行参数 :code:`trainer_count`，即可以设置参与训练的线程数量。使用方法为 :code:`paddle train --trainer_count=4`
+* 单机GPU训练
+ * 使用显卡训练。设置命令行参数 :code:`use_gpu`。 使用方法为 :code:`paddle train --use_gpu=true`
+ * 使用多块显卡训练。设置命令行参数 :code:`use_gpu` 和 :code:`trainer_count`。使用 :code:`--use_gpu=True` 开启GPU训练，使用 :code:`trainer_count` 指定显卡数量。使用方法为 :code:`paddle train --use_gpu=true --trainer_count=4`
+* 多机训练
+ * 使用多机训练的方法也比较简单，需要先在每个节点启动 :code:`paddle pserver`，在使用 :code:`paddle train --pservers=192.168.100.1,192.168.100.2` 来指定每个pserver的ip地址
+ * 具体的多机训练方法参考 `多机训练 `_ 文档。
+
+
+3. 遇到“非法指令”或者是“illegal instruction”
+--------------------------------------------
+
+paddle在进行计算的时候为了提升计算性能，使用了avx指令。部分老的cpu型号无法支持这样的指令。通常来说执行下grep avx /proc/cpuinfo看看是否有输出即可知道是否支持。（另：用此方法部分虚拟机可能检测到支持avx指令但是实际运行会挂掉，请当成是不支持，看下面的解决方案）
+
+解决办法是\:
+
+* 使用 NO_AVX的 `安装包 <../build_and_install/index.html>`_ 或者 `Docker image <../build_and_install/install/docker_install.html>`_
+* 或者，使用 :code:`-DWITH_AVX=OFF` 重新编译PaddlePaddle。
+
+
+4. 如何选择SGD算法的学习率
+--------------------------
+
+在采用sgd/async_sgd进行训练时，一个重要的问题是选择正确的learning_rate。如果learning_rate太大，那么训练有可能不收敛，如果learning_rate太小，那么收敛可能很慢，导致训练时间过长。
+
+通常做法是从一个比较大的learning_rate开始试，如果不收敛，那减少学习率10倍继续试验，直到训练收敛为止。那么如何判断训练不收敛呢？可以估计出如果模型采用不变的输出最小的cost0是多少。
+
+如果训练过程的的cost明显高于这个常数输出的cost，那么我们可以判断为训练不收敛。举一个例子，假如我们是三分类问题，采用multi-class-cross-entropy作为cost，数据中0,1,2三类的比例为 :code:`0.2, 0.5, 0.3` , 那么常数输出所能达到的最小cost是 :code:`-(0.2*log(0.2)+0.5*log(0.5)+0.3*log(0.3))=1.03` 。如果训练一个pass（或者更早）后，cost还大于这个数，那么可以认为训练不收敛，应该降低学习率。
+
+
+5. 如何初始化参数
+-----------------
+
+默认情况下，PaddlePaddle使用均值0，标准差为 :math:`\frac{1}{\sqrt{d}}` 来初始化参数。其中 :math:`d` 为参数矩阵的宽度。这种初始化方式在一般情况下不会产生很差的结果。如果用户想要自定义初始化方式，PaddlePaddle目前提供两种参数初始化的方式\:
+
+* 高斯分布。将 :code:`param_attr` 设置成 :code:`param_attr=ParamAttr(initial_mean=0.0, initial_std=1.0)`
+* 均匀分布。将 :code:`param_attr` 设置成 :code:`param_attr=ParamAttr(initial_max=1.0, initial_min=-1.0)`
+
+比如设置一个全连接层的参数初始化方式和bias初始化方式，可以使用如下代码。
+
+.. code-block:: python
+
+ hidden = fc_layer(input=ipt, param_attr=ParamAttr(initial_max=1.0, initial_min=-1.0),
+ bias_attr=ParamAttr(initial_mean=1.0, initial_std=0.0))
+
+上述代码将bias全部初始化为1.0, 同时将参数初始化为 :code:`[1.0, -1.0]` 的均匀分布。
+
+6. 如何共享参数
+---------------
+
+PaddlePaddle的参数使用名字 :code:`name` 作为参数的ID，相同名字的参数，会共享参数。设置参数的名字，可以使用 :code:`ParamAttr(name="YOUR_PARAM_NAME")` 来设置。更方便的设置方式，是想要共享的参数使用同样的 :code:`ParamAttr` 对象。
+
+简单的全连接网络，参数共享的配置示例为\:
+
+.. literalinclude:: ../../python/paddle/trainer_config_helpers/tests/configs/shared_fc.py
+
+这里 :code:`hidden_a` 和 :code:`hidden_b` 使用了同样的parameter和bias。并且softmax层的两个输入也使用了同样的参数 :code:`softmax_param`。
+
+
diff --git a/doc_cn/_sources/index.txt b/doc_cn/_sources/index.txt
index 6cf5588b5b34f5e80ea4c70cc364d4c6c42cce3d..d2d50fbdb47f27ad5ad8d22215a9f0993145430f 100644
--- a/doc_cn/_sources/index.txt
+++ b/doc_cn/_sources/index.txt
@@ -3,6 +3,7 @@ PaddlePaddle文档
使用指南
--------
+
* `快速入门 `_
* `编译与安装 `_
* `用户接口 `_
@@ -16,4 +17,13 @@ PaddlePaddle文档
算法教程
--------
-* `RNN配置 <../doc/algorithm/rnn/rnn.html>`_
+
+* `Recurrent Group教程 `_
+* `单层RNN示例 <../doc/algorithm/rnn/rnn.html>`_
+* `双层RNN示例 `_
+* `支持双层序列作为输入的Layer `_
+
+常见问题
+--------
+
+* `常见问题 `_
diff --git a/doc_cn/algorithm/rnn/hierarchical-layer.html b/doc_cn/algorithm/rnn/hierarchical-layer.html
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+ 支持双层序列作为输入的Layer — PaddlePaddle documentation
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支持双层序列作为输入的Layer
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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。
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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。
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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。
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+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
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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。
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+
pooling_layer
+
pooling_layer的使用示例如下，详细见配置API 。
+
seq_pool = pooling_layer ( input = layer ,
+ pooling_type = AvgPooling (),
+ agg_level = AggregateLevel . EACH_SEQUENCE )
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+
+
+pooling_type agg_level=AggregateLevel.TIMESTEP
+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
+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）
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+
+
+
last_seq 和 first_seq
+
last_seq的使用示例如下（first_seq类似），详细见配置API 。
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last = last_seq ( input = layer ,
+ agg_level = AggregateLevel . EACH_SEQUENCE )
+
+
+
+agg_level=AggregateLevel.TIMESTEP
+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
+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。
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+
+
expand_layer
+
expand_layer的使用示例如下，详细见配置API 。
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expand = expand_layer ( input = layer1 ,
+ expand_as = layer2 ,
+ expand_level = ExpandLevel . FROM_TIMESTEP )
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+
+
+expand_level=ExpandLevel.FROM_TIMESTEP
+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
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+expand_level=ExpandLevel.FROM_SEQUENCE
+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。
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+
双层RNN配置与示例 — PaddlePaddle documentation
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+
双层RNN配置与示例
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我们在paddle/gserver/tests/test_RecurrentGradientMachine
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示例1：双进双出，subseq间无memory
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配置：单层RNN（sequence_layer_group sequence_nest_layer_group
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+
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）：
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+单层序列的数据（Sequence/tour_train_wdseg
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+双层序列的数据（Sequence/tour_train_wdseg.nest
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其次，我们看一下单双层序列的不同dataprovider（见sequenceGen.py
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+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。
+
+
+
+
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。
+
+
+
+
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
+
settings ( batch_size = 5 )
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+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
+
+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。
+
+
+
+
settings ( batch_size = 2 )
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+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 sequence_nest_rnn.conf sequence_nest_rnn_readonly_memory.conf
+
+
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
+
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。
+
+
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。
+
+
+
+
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 first_seq Check failed: input.sequenceStartPositions ”的错误。
+
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+
示例4：beam_search的生成
+
TBD
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+
Recurrent Group教程 — PaddlePaddle documentation
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+
Recurrent Group教程
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+
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
+
更进一步，recurrent_group recurrent_group
+
目前，在PaddlePaddle中，能够对双向序列进行处理的有recurrent_group 支持双层序列作为输入的Layer 。
+
+
+
相关概念
+
+
基本原理
+
recurrent_group
+
PaddlePaddle中，recurrent_group
+
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 recurrent_group recurrent_group
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+
+
输入
+
recurrent_group
+
+数据输入 ：一个双层序列进入recurrent_group recurrent_group 只读Memory输入 ：StaticInput StaticInput recurrent_group recurrent_group 序列生成任务的输入 ：GeneratedInput
+
+
+
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
+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 Neural Turning Machine 。
+
+
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
+
+
+
输出
+
step recurrent_group recurrent_group
+
+
+
memory
+
memory只能在recurrent_group
+
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
+
利用这种特性，两个嵌套的recurrent_group
+
+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
+
+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
+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
+
+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。
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+
PaddlePaddle常见问题 — PaddlePaddle documentation
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+
+
+
神经网络的训练本身是一个非常消耗内存和显存的工作。经常会消耗数十G的内存和数G的显存。
+PaddlePaddle的内存占用主要分为如下几个方面:
+
+DataProvider缓冲池内存 (只针对内存)
+神经元激活内存 （针对内存和显存）
+参数内存 (针对内存和显存)
+其他内存杂项
+
+
这其中，其他内存杂项是指PaddlePaddle本身所用的一些内存，包括字符串分配，临时变量等等，
+这些内存就不考虑如何缩减了。
+
其他的内存的减少方法依次为
+
+
+
PyDataProvider使用的是异步加载，同时在内存里直接随即选取数据来做Shuffle。即
+
+
所以，减小这个内存池即可减小内存占用，同时也可以加速开始训练前数据载入的过程。但是，这
+个内存池实际上决定了shuffle的粒度。所以，如果将这个内存池减小，又要保证数据是随机的，
+那么最好将数据文件在每次读取之前做一次shuffle。可能的代码为
+
@provider ( min_pool_size = 0 , ... )
+def process ( settings , filename ):
+ os . system ( 'shuf %s > %s .shuf' % ( filename , filename )) # shuffle before.
+ with open ( ' %s .shuf' % filename , 'r' ) as f :
+ for line in f :
+ yield get_sample_from_line ( line )
+
+
+
这样做可以极大的减少内存占用，并且可能会加速训练过程。 详细文档参考 这里 。
+
+
+
+
神经网络在训练的时候，会对每一个激活暂存一些数据，包括激活，參差等等。
+在反向传递的时候，这些数据会被用来更新参数。这些数据使用的内存主要和两个参数有关系，
+一是batch size，另一个是每条序列(Sequence)长度。所以，其实也是和每个mini-batch中包含
+的时间步信息成正比。
+
所以，做法可以有两种。他们是
+
+减小batch size。 即在网络配置中 settings(batch_size=1000)
+减小序列的长度，或者直接扔掉非常长的序列。比如，一个数据集大部分序列长度是100-200,
+但是突然有一个10000长的序列，就很容易导致内存超限。特别是在LSTM等RNN中。
+
+
+
+
+
PaddlePaddle支持非常多的优化算法(Optimizer)，不同的优化算法需要使用不同大小的内存。
+例如如果使用 adadelta 100M 500M
+
可以考虑使用一些优化算法，例如 momentum
+
+
+
+
+
PaddlePaddle是神经网络训练平台，加速PaddlePaddle训练有如下几个方面：
+
+减少数据载入的耗时
+加速训练速度
+利用更多的计算资源
+
+
+
+
使用 pydataprovider`时，可以减少缓存池的大小，同时设置内存缓存功能，即可以极大的加速数据载入流程。
+:code:`DataProvider
+
@provider ( min_pool_size = 0 , ... )
+def process ( settings , filename ):
+ os . system ( 'shuf %s > %s .shuf' % ( filename , filename )) # shuffle before.
+ with open ( ' %s .shuf' % filename , 'r' ) as f :
+ for line in f :
+ yield get_sample_from_line ( line )
+
+
+
同时 @provider cache CacheType.CACHE_PASS_IN_MEM pass pass python
+
+
+
+
PaddlePaddle支持Sparse的训练，sparse训练需要训练特征是 sparse_binary_vector sparse_vector integer_value sparse_update=True
+
这里使用简单的 word2vec
+
使用一个词前两个词和后两个词，来预测这个中间的词。这个任务的DataProvider为:
+
DICT_DIM = 3000
+@provider ( input_types = [ integer_sequence ( DICT_DIM ), integer_value ( DICT_DIM )])
+def process ( settings , filename ):
+ with open ( filename ) as f :
+ # yield word ids to predict inner word id
+ # such as [28, 29, 10, 4], 4
+ # It means the sentance is 28, 29, 4, 10, 4.
+ yield read_next_from_file ( f )
+
+
+
这个任务的配置为:
+
... # the settings and define data provider is omitted.
+DICT_DIM = 3000 # dictionary dimension.
+word_ids = data_layer ( 'word_ids' , size = DICT_DIM )
+
+emb = embedding_layer ( input = word_ids , size = 256 , param_attr = ParamAttr ( sparse_update = True ))
+emb_sum = pooling_layer ( input = emb , pooling_type = SumPooling ())
+predict = fc_layer ( input = emb_sum , size = DICT_DIM , act = Softmax ())
+outputs ( classification_cost ( input = predict , label = data_layer ( 'label' , size = DICT_DIM )))
+
+
+
更多关于sparse训练的内容请参考 sparse训练的文档
+
+
+
+
利用更多的计算资源可以分为一下几个方式来进行:
+
+单机CPU训练
+* 使用多线程训练。设置命令行参数 trainer_count paddle train --trainer_count=4
+单机GPU训练
+* 使用显卡训练。设置命令行参数 use_gpu paddle train --use_gpu=true use_gpu trainer_count --use_gpu=True trainer_count paddle train --use_gpu=true --trainer_count=4
+多机训练
+* 使用多机训练的方法也比较简单，需要先在每个节点启动 paddle pserver paddle train --pservers=192.168.100.1,192.168.100.2 多机训练 文档。
+
+
+
+
+
+
paddle在进行计算的时候为了提升计算性能，使用了avx指令。部分老的cpu型号无法支持这样的指令。通常来说执行下grep avx /proc/cpuinfo看看是否有输出即可知道是否支持。（另：用此方法部分虚拟机可能检测到支持avx指令但是实际运行会挂掉，请当成是不支持，看下面的解决方案）
+
解决办法是:
+
+
+
+
+
在采用sgd/async_sgd进行训练时，一个重要的问题是选择正确的learning_rate。如果learning_rate太大，那么训练有可能不收敛，如果learning_rate太小，那么收敛可能很慢，导致训练时间过长。
+
通常做法是从一个比较大的learning_rate开始试，如果不收敛，那减少学习率10倍继续试验，直到训练收敛为止。那么如何判断训练不收敛呢？可以估计出如果模型采用不变的输出最小的cost0是多少。
+
如果训练过程的的cost明显高于这个常数输出的cost，那么我们可以判断为训练不收敛。举一个例子，假如我们是三分类问题，采用multi-class-cross-entropy作为cost，数据中0,1,2三类的比例为 0.2, 0.5, 0.3 -(0.2*log(0.2)+0.5*log(0.5)+0.3*log(0.3))=1.03
+
+
+
+
默认情况下，PaddlePaddle使用均值0，标准差为 \(\frac{1}{\sqrt{d}}\) 来初始化参数。其中 \(d\) 为参数矩阵的宽度。这种初始化方式在一般情况下不会产生很差的结果。如果用户想要自定义初始化方式，PaddlePaddle目前提供两种参数初始化的方式:
+
+高斯分布。将 param_attr param_attr=ParamAttr(initial_mean=0.0, initial_std=1.0)
+均匀分布。将 param_attr param_attr=ParamAttr(initial_max=1.0, initial_min=-1.0)
+
+
比如设置一个全连接层的参数初始化方式和bias初始化方式，可以使用如下代码。
+
hidden = fc_layer ( input = ipt , param_attr = ParamAttr ( initial_max = 1.0 , initial_min =- 1.0 ),
+ bias_attr = ParamAttr ( initial_mean = 1.0 , initial_std = 0.0 ))
+
+
+
上述代码将bias全部初始化为1.0, 同时将参数初始化为 [1.0, -1.0]
+
+
+
+
PaddlePaddle的参数使用名字 name ParamAttr(name="YOUR_PARAM_NAME") ParamAttr
+
简单的全连接网络，参数共享的配置示例为:
+
from paddle.trainer_config_helpers import *
+
+settings (
+ learning_rate = 1 e - 4 ,
+ batch_size = 1000
+)
+
+a = data_layer ( name = 'feature_a' , size = 200 )
+b = data_layer ( name = 'feature_b' , size = 200 )
+
+fc_param = ParamAttr ( name = 'fc_param' , initial_max = 1.0 , initial_min =- 1.0 )
+bias_param = ParamAttr ( name = 'bias_param' , initial_mean = 0.0 , initial_std = 0.0 )
+
+softmax_param = ParamAttr ( name = 'softmax_param' , initial_max = 1.0 , initial_min =- 1.0 )
+
+hidden_a = fc_layer ( input = a , size = 200 , param_attr = fc_param , bias_attr = bias_param )
+hidden_b = fc_layer ( input = b , size = 200 , param_attr = fc_param , bias_attr = bias_param )
+
+predict = fc_layer ( input = [ hidden_a , hidden_b ], param_attr = [ softmax_param , softmax_param ],
+ bias_attr = False , size = 10 , act = SoftmaxActivation ())
+
+outputs ( classification_cost ( input = predict , label = data_layer ( name = 'label' , size = 10 )))
+
+
+
这里 hidden_a hidden_b softmax_param
+
+
+
+
+
+
+
+
+
+
@@ -95,6 +104,7 @@ var _hmt = _hmt || [];
使用指南 开发指南 算法教程
+
常见问题
diff --git a/doc_cn/objects.inv b/doc_cn/objects.inv
index 7969c18370e0d15b69996dcf3db0c407e6c28fe7..2cfa1bb72d49420b7b628bffcdc4927c1e4e4dd2 100644
Binary files a/doc_cn/objects.inv and b/doc_cn/objects.inv differ
diff --git a/doc_cn/searchindex.js b/doc_cn/searchindex.js
index 106bae35fd972cecfdd7b6f379d3eb3e4ab3fbcb..001f1053657aefaa6567c7c7d4a7aa439c68c20a 100644
--- a/doc_cn/searchindex.js
+++ b/doc_cn/searchindex.js
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