From ac96359092fab048b4acc2379b97fdf7a5e650b2 Mon Sep 17 00:00:00 2001 From: Travis CI Date: Mon, 17 Oct 2016 05:41:34 +0000 Subject: [PATCH] Deploy to GitHub Pages: e4952ca6cea9130258086b4b7f2dff7a8bf5f566 --- .../ui/api/trainer_config_helpers/layers.txt | 6 + doc/genindex.html | 16 +- doc/objects.inv | Bin 155870 -> 155825 bytes doc/searchindex.js | 2 +- doc/source/cuda/rnn/rnn.html | 4 +- doc/source/gserver/layers/layer.html | 17 +- doc/source/math/matrix/matrix.html | 12 +- doc/ui/api/trainer_config_helpers/layers.html | 44 ++ .../trainer_config_helpers/layers_index.html | 1 + doc_cn/.buildinfo | 2 +- ...e6aad37f57c60f4b971dde0ef44ce27179cf9a.png | Bin 0 -> 11618 bytes ...d37f57c60f4b971dde0ef44ce27179cf9a.png.map | 2 + .../algorithm/rnn/hierarchical-layer.txt | 66 +++ .../algorithm/rnn/hierarchical-rnn.txt | 267 ++++++++++++ .../_sources/algorithm/rnn/rnn-tutorial.txt | 96 +++++ doc_cn/_sources/faq/index.txt | 169 ++++++++ doc_cn/_sources/index.txt | 12 +- doc_cn/algorithm/rnn/hierarchical-layer.html | 196 +++++++++ doc_cn/algorithm/rnn/hierarchical-rnn.html | 404 ++++++++++++++++++ doc_cn/algorithm/rnn/rnn-tutorial.html | 222 ++++++++++ doc_cn/faq/index.html | 329 ++++++++++++++ doc_cn/index.html | 12 +- doc_cn/objects.inv | Bin 851 -> 986 bytes doc_cn/searchindex.js | 2 +- 24 files changed, 1842 insertions(+), 39 deletions(-) create mode 100644 doc_cn/_images/graphviz-9be6aad37f57c60f4b971dde0ef44ce27179cf9a.png create mode 100644 doc_cn/_images/graphviz-9be6aad37f57c60f4b971dde0ef44ce27179cf9a.png.map create mode 100644 doc_cn/_sources/algorithm/rnn/hierarchical-layer.txt create mode 100644 doc_cn/_sources/algorithm/rnn/hierarchical-rnn.txt create mode 100644 doc_cn/_sources/algorithm/rnn/rnn-tutorial.txt create mode 100644 doc_cn/_sources/faq/index.txt create mode 100644 doc_cn/algorithm/rnn/hierarchical-layer.html create mode 100644 doc_cn/algorithm/rnn/hierarchical-rnn.html create mode 100644 doc_cn/algorithm/rnn/rnn-tutorial.html create mode 100644 doc_cn/faq/index.html diff --git a/doc/_sources/ui/api/trainer_config_helpers/layers.txt b/doc/_sources/ui/api/trainer_config_helpers/layers.txt index 5271262d20..55f5623b0f 100644 --- a/doc/_sources/ui/api/trainer_config_helpers/layers.txt +++ b/doc/_sources/ui/api/trainer_config_helpers/layers.txt @@ -130,6 +130,12 @@ gru_step_layer Recurrent Layer Group ===================== +memory +------ +.. automodule:: paddle.trainer_config_helpers.layers + :members: memory + :noindex: + recurrent_group --------------- .. automodule:: paddle.trainer_config_helpers.layers diff --git a/doc/genindex.html b/doc/genindex.html index 5eb89b5c23..be28cc77b4 100644 --- a/doc/genindex.html +++ b/doc/genindex.html @@ -1823,7 +1823,7 @@ var _hmt = _hmt || []; -
hl_sequence2batch_copy (C++ function) +
hl_sequence2batch_copy (C++ function)
@@ -5247,7 +5247,7 @@ var _hmt = _hmt || []; -
paddle::CpuMatrix::copyByRowIndex (C++ function) +
paddle::CpuMatrix::copyByRowIndex (C++ function)
@@ -6955,10 +6955,6 @@ var _hmt = _hmt || []; -
paddle::ExpandLayer::cpuExpandStartsPos_ (C++ member) -
- -
paddle::ExpandLayer::ExpandLayer (C++ function)
@@ -7359,7 +7355,7 @@ var _hmt = _hmt || []; -
paddle::GpuMatrix::copyByRowIndex (C++ function) +
paddle::GpuMatrix::copyByRowIndex (C++ function)
@@ -9339,7 +9335,7 @@ var _hmt = _hmt || []; -
paddle::Matrix::copyByRowIndex (C++ function) +
paddle::Matrix::copyByRowIndex (C++ function)
@@ -14465,10 +14461,6 @@ var _hmt = _hmt || []; -
paddle::SequenceScatterAgentLayer::cpuInputStartPos_ (C++ member) -
- -
paddle::SequenceScatterAgentLayer::forward (C++ function)
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zZ7$DG$JLU+=^-iiTz+kXmp|a+Le7uQZk*nod#^6$2jNP`1MS~jbB(HT@ekK_Vln&s z7kBQz;
@@ -2551,8 +2551,8 @@ where bit(i, j) = ((codes(i) + numClasses) & 2^j) ? 1 : 0
-
-virtual void copyByRowIndex(Matrix &b, IVector &rowIndex)
+
+virtual void copyByRowIndex(Matrix &b, const IVector &rowIndex)
@@ -3111,8 +3111,8 @@ where bit(i, j) = ((codes(i) + numClasses) & 2^j) ? 1 : 0
-
-virtual void copyByRowIndex(Matrix &b, IVector &rowIndex)
+
+virtual void copyByRowIndex(Matrix &b, const IVector &rowIndex)
diff --git a/doc/ui/api/trainer_config_helpers/layers.html b/doc/ui/api/trainer_config_helpers/layers.html index a11f93cb71..d61c8d0a00 100644 --- a/doc/ui/api/trainer_config_helpers/layers.html +++ b/doc/ui/api/trainer_config_helpers/layers.html @@ -901,6 +901,49 @@ will get a warning.

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 ‘s output.

+

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 is true boot layer is sequence.

+

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_layer
    • +
  • boot_layer (LayerOutput|None) – boot layer of memory.
    • +
  • boot_bias (ParameterAttribute|None) – boot layer’s bias
    • +
  • boot_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
      • diff --git a/doc/ui/api/trainer_config_helpers/layers_index.html b/doc/ui/api/trainer_config_helpers/layers_index.html index 0740512a8b..4b825430a0 100644 --- a/doc/ui/api/trainer_config_helpers/layers_index.html +++ b/doc/ui/api/trainer_config_helpers/layers_index.html @@ -114,6 +114,7 @@ var _hmt = _hmt || [];
  • Recurrent Layer Group
      +
    • memory
    • recurrent_group
    • beam_search
    • get_output_layer
      • diff --git a/doc_cn/.buildinfo b/doc_cn/.buildinfo index fc2ad92e9f..273d212ecc 100644 --- a/doc_cn/.buildinfo +++ b/doc_cn/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. 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a/doc_cn/_sources/algorithm/rnn/hierarchical-layer.txt b/doc_cn/_sources/algorithm/rnn/hierarchical-layer.txt new file mode 100644 index 0000000000..5282bbbcb8 --- /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 0000000000..979fe13e2e --- /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 0000000000..7a553054c8 --- /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 0000000000..283607957c --- /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 6cf5588b5b..d2d50fbdb4 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 new file mode 100644 index 0000000000..1799f0a800 --- /dev/null +++ b/doc_cn/algorithm/rnn/hierarchical-layer.html @@ -0,0 +1,196 @@ + + + + + + + + + + 支持双层序列作为输入的Layer — PaddlePaddle documentation + + + + + + + + + + + + + + + + + + +
      +
      +
      +
      + +
      +

      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。

      +
      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。

      +
      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。

      +
      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/algorithm/rnn/hierarchical-rnn.html b/doc_cn/algorithm/rnn/hierarchical-rnn.html new file mode 100644 index 0000000000..2bf7294fd6 --- /dev/null +++ b/doc_cn/algorithm/rnn/hierarchical-rnn.html @@ -0,0 +1,404 @@ + + + + + + + + + + 双层RNN配置与示例 — PaddlePaddle documentation + + + + + + + + + + + + + + + + + + +
      +
      +
      +
      + +
      +

      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。
        • +
      +
      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。
        • +
      +
      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。
          • +
        +
        • +
      +
      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）。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。

      +
      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。
          • +
        +
        • +
      +
      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）

      +
      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

      +
      + +
      + + +
      +
      +
      + +
      +
      + + + + \ No newline at end of file diff --git a/doc_cn/algorithm/rnn/rnn-tutorial.html b/doc_cn/algorithm/rnn/rnn-tutorial.html new file mode 100644 index 0000000000..ed86662ecc --- /dev/null +++ b/doc_cn/algorithm/rnn/rnn-tutorial.html @@ -0,0 +1,222 @@ + + + + + + + + + + Recurrent Group教程 — PaddlePaddle documentation + + + + + + + + + + + + + + + + + + +
      +
      +
      +
      + +
      +

      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：

      +
      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。
        • +
      +

      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/faq/index.html b/doc_cn/faq/index.html new file mode 100644 index 0000000000..6b05ec70af --- /dev/null +++ b/doc_cn/faq/index.html @@ -0,0 +1,329 @@ + + + + + + + + + + PaddlePaddle常见问题 — PaddlePaddle documentation + + + + + + + + + + + + + + + + + + +
      +
      +
      +
      + +
      +

      PaddlePaddle常见问题

      + +
      +

      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

      +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

      +
      @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 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 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 100M， 那么该优化算法至少需要 500M 的内存。

      +

      可以考虑使用一些优化算法，例如 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 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。

      +
      @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 接口有一个 cache 参数来控制缓存方法，将其设置成 CacheType.CACHE_PASS_IN_MEM 的话，会将第一个 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 pass 中，不会再从 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 sparse_binary_vector 、 sparse_vector 、或者 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 sparse_update=True

      +

      这里使用简单的 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:

      +
      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

      +

      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，即可以设置参与训练的线程数量。使用方法为 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。 使用方法为 paddle train --use_gpu=true +* 使用多块显卡训练。设置命令行参数 use_gpu 和 trainer_count。使用 --use_gpu=True 开启GPU训练，使用 trainer_count 指定显卡数量。使用方法为 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，在使用 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 或者 Docker image
        • +
      • 或者，使用 -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 0.2, 0.5, 0.3 , 那么常数输出所能达到的最小cost是 -(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 \(\frac{1}{\sqrt{d}}\) 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 设置成 param_attr=ParamAttr(initial_mean=0.0, initial_std=1.0)
        • +
      • 均匀分布。将 param_attr 设置成 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] 的均匀分布。

      +
      +
      +

      6. 如何共享参数

      +

      PaddlePaddle的参数使用名字 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 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 ParamAttr 对象。

      +

      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=1e-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 和 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 softmax_param。

      +
      +
      + + +
      +
      +
      + +
      +
      + + + + \ No newline at end of file diff --git a/doc_cn/index.html b/doc_cn/index.html index db944cacf5..768568571c 100644 --- a/doc_cn/index.html +++ b/doc_cn/index.html @@ -78,7 +78,16 @@ var _hmt = _hmt || []; +
      +

      常见问题

      +
    • @@ -95,6 +104,7 @@ var _hmt = _hmt || [];
  • 使用指南
  • 开发指南
  • 算法教程
    • +
  • 常见问题
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\ No newline at end of file -- GitLab
    -
    -void hl_sequence2batch_copy(real *batch, real *sequence, int *batchIndex, int seqWidth, int batchCount, bool seq2batch)
    +
    +void hl_sequence2batch_copy(real *batch, real *sequence, const int *batchIndex, int seqWidth, int batchCount, bool seq2batch)

    Memory copy from sequence to batch.

    if seq2batch == true

    copy from sequence to batch: batch[i] = sequence[batchIndex[i]].

    diff --git a/doc/source/gserver/layers/layer.html b/doc/source/gserver/layers/layer.html index c077aeddd2..31a6707bd4 100644 --- a/doc/source/gserver/layers/layer.html +++ b/doc/source/gserver/layers/layer.html @@ -4623,14 +4623,9 @@ The config file api if gru_step_layer.

    Protected Attributes

    -
    -
    -IVectorPtr cpuInputStartPos_
    -
    -
    -IVectorPtr inputStartPos_
    +ICpuGpuVectorPtr inputStartPos_
    @@ -5747,16 +5742,10 @@ sequence is one) to sequence data.”

    store the ExpandLevel

    -
    -
    -IVectorPtr cpuExpandStartsPos_
    -

    expanded sequenceStartPositions or subSequenceStartPositions of input[1]

    -
    -
    -IVectorPtr expandStartsPos_
    -

    point to cpuExpandStartsPos_ when useGpu_ is false, copy from cpuExpandStartsPos_ when useGpu_ is true

    +ICpuGpuVectorPtr expandStartsPos_ +

    expanded sequenceStartPositions or subSequenceStartPositions of input[1]

    diff --git a/doc/source/math/matrix/matrix.html b/doc/source/math/matrix/matrix.html index 374a333e7d..148e6e1c82 100644 --- a/doc/source/math/matrix/matrix.html +++ b/doc/source/math/matrix/matrix.html @@ -1756,8 +1756,8 @@ virtual -
    -virtual void copyByRowIndex(Matrix &b, IVector &rowIndex)
    +
    +virtual void copyByRowIndex(Matrix &b, const IVector &rowIndex)