Merge pull request #13095 from shanyi15/0.14.0

fix_bookdoc_0.14.0

doc/fluid/new_docs/beginners_guide/basics/image_classification/.gitignore

-*.pyc
-train.log
-output
-data/cifar-10-batches-py/
-data/cifar-10-python.tar.gz
-data/*.txt
-data/*.list
-data/mean.meta

doc/fluid/new_docs/beginners_guide/basics/index.rst

@@ -10,9 +10,9 @@
 ..  toctree::
     :maxdepth: 2
 
-    image_classification/index.md
-    word2vec/index.md
-    recommender_system/index.md
-    understand_sentiment/index.md
-    label_semantic_roles/index.md
-    machine_translation/index.md
+    image_classification/README.cn.md
+    word2vec/README.cn.md
+    recommender_system/README.cn.md
+    understand_sentiment/README.cn.md
+    label_semantic_roles/README.cn.md
+    machine_translation/README.cn.md

doc/fluid/new_docs/beginners_guide/basics/label_semantic_roles/.gitignore

-data/train.list
-data/test.*
-data/conll05st-release.tar.gz
-data/conll05st-release
-data/predicate_dict
-data/label_dict
-data/word_dict
-data/emb
-data/feature
-output
-predict.res
-train.log

doc/fluid/new_docs/beginners_guide/basics/machine_translation/.gitignore

-data/wmt14
-data/pre-wmt14
-pretrained/wmt14_model
-gen.log
-gen_result
-train.log
-dataprovider_copy_1.py
-*.pyc
-multi-bleu.perl

doc/fluid/new_docs/beginners_guide/basics/recommender_system/.gitignore

-.idea
-.ipynb_checkpoints

doc/fluid/new_docs/beginners_guide/basics/understand_sentiment/.gitignore

-data/aclImdb
-data/imdb
-data/pre-imdb
-data/mosesdecoder-master
-*.log
-model_output
-dataprovider_copy_1.py
-model.list
-*.pyc
-.DS_Store

doc/fluid/new_docs/beginners_guide/basics/understand_sentiment/index.md → doc/fluid/new_docs/beginners_guide/basics/understand_sentiment/README.cn.md

 # 情感分析
 
-本教程源代码目录在[book/understand_sentiment](https://github.com/PaddlePaddle/book/tree/develop/06.understand_sentiment)， 初次使用请参考PaddlePaddle[安装教程](https://github.com/PaddlePaddle/book/blob/develop/README.cn.md#运行这本书)。
+本教程源代码目录在[book/understand_sentiment](https://github.com/PaddlePaddle/book/tree/develop/06.understand_sentiment)， 初次使用请参考PaddlePaddle[安装教程](https://github.com/PaddlePaddle/book/blob/develop/README.cn.md#运行这本书)，更多内容请参考本教程的[视频课堂](http://bit.baidu.com/course/detail/id/177.html)。
 
 ## 背景介绍
 
@@ -36,8 +36,8 @@
 
 循环神经网络是一种能对序列数据进行精确建模的有力工具。实际上，循环神经网络的理论计算能力是图灵完备的\[[4](#参考文献)\]。自然语言是一种典型的序列数据（词序列），近年来，循环神经网络及其变体（如long short term memory\[[5](#参考文献)\]等）在自然语言处理的多个领域，如语言模型、句法解析、语义角色标注（或一般的序列标注）、语义表示、图文生成、对话、机器翻译等任务上均表现优异甚至成为目前效果最好的方法。
 
-![rnn](./image/rnn.png)
 <p align="center">
+<img src="image/rnn.png" width = "60%" align="center"/><br/>
 图1. 循环神经网络按时间展开的示意图
 </p>
 
@@ -65,8 +65,8 @@ $$ o_t = \sigma(W_{xo}x_t+W_{ho}h_{t-1}+W_{co}c_{t}+b_o) $$
 $$ h_t = o_t\odot tanh(c_t) $$
 其中，$i_t, f_t, c_t, o_t$分别表示输入门，遗忘门，记忆单元及输出门的向量值，带角标的$W$及$b$为模型参数，$tanh$为双曲正切函数，$\odot$表示逐元素（elementwise）的乘法操作。输入门控制着新输入进入记忆单元$c$的强度，遗忘门控制着记忆单元维持上一时刻值的强度，输出门控制着输出记忆单元的强度。三种门的计算方式类似，但有着完全不同的参数，它们各自以不同的方式控制着记忆单元$c$，如图2所示：
 
-![lstm](./image/lstm.png)
 <p align="center">
+<img src="image/lstm.png" width = "65%" align="center"/><br/>
 图2. 时刻$t$的LSTM [7]
 </p>
 
@@ -82,8 +82,8 @@ $$ h_t=Recrurent(x_t,h_{t-1})$$
 
 如图3所示（以三层为例），奇数层LSTM正向，偶数层LSTM反向，高一层的LSTM使用低一层LSTM及之前所有层的信息作为输入，对最高层LSTM序列使用时间维度上的最大池化即可得到文本的定长向量表示（这一表示充分融合了文本的上下文信息，并且对文本进行了深层次抽象），最后我们将文本表示连接至softmax构建分类模型。
 
-![stacked_lstm](./image/stacked_lstm.jpg)
 <p align="center">
+<img src="image/stacked_lstm.jpg" width=450><br/>
 图3. 栈式双向LSTM用于文本分类
 </p>
 
@@ -94,11 +94,11 @@ $$ h_t=Recrurent(x_t,h_{t-1})$$
 ```text
 aclImdb
 |- test
-|-- neg
-|-- pos
+   |-- neg
+   |-- pos
 |- train
-|-- neg
-|-- pos
+   |-- neg
+   |-- pos
 ```
 Paddle在`dataset/imdb.py`中提实现了imdb数据集的自动下载和读取，并提供了读取字典、训练数据、测试数据等API。
 
@@ -107,6 +107,7 @@ Paddle在`dataset/imdb.py`中提实现了imdb数据集的自动下载和读取
 在该示例中，我们实现了两种文本分类算法，分别基于[推荐系统](https://github.com/PaddlePaddle/book/tree/develop/05.recommender_system)一节介绍过的文本卷积神经网络，以及[栈式双向LSTM](#栈式双向LSTM（Stacked Bidirectional LSTM）)。我们首先引入要用到的库和定义全局变量：
 
 ```python
+from __future__ import print_function
 import paddle
 import paddle.fluid as fluid
 from functools import partial
@@ -115,6 +116,7 @@ import numpy as np
 CLASS_DIM = 2
 EMB_DIM = 128
 HID_DIM = 512
+STACKED_NUM = 3
 BATCH_SIZE = 128
 USE_GPU = False
 ```
@@ -126,23 +128,23 @@ USE_GPU = False
 
 ```python
 def convolution_net(data, input_dim, class_dim, emb_dim, hid_dim):
-emb = fluid.layers.embedding(
-input=data, size=[input_dim, emb_dim], is_sparse=True)
-conv_3 = fluid.nets.sequence_conv_pool(
-input=emb,
-num_filters=hid_dim,
-filter_size=3,
-act="tanh",
-pool_type="sqrt")
-conv_4 = fluid.nets.sequence_conv_pool(
-input=emb,
-num_filters=hid_dim,
-filter_size=4,
-act="tanh",
-pool_type="sqrt")
-prediction = fluid.layers.fc(
-input=[conv_3, conv_4], size=class_dim, act="softmax")
-return prediction
+    emb = fluid.layers.embedding(
+        input=data, size=[input_dim, emb_dim], is_sparse=True)
+    conv_3 = fluid.nets.sequence_conv_pool(
+        input=emb,
+        num_filters=hid_dim,
+        filter_size=3,
+        act="tanh",
+        pool_type="sqrt")
+    conv_4 = fluid.nets.sequence_conv_pool(
+        input=emb,
+        num_filters=hid_dim,
+        filter_size=4,
+        act="tanh",
+        pool_type="sqrt")
+    prediction = fluid.layers.fc(
+        input=[conv_3, conv_4], size=class_dim, act="softmax")
+    return prediction
 ```
 
 网络的输入`input_dim`表示的是词典的大小，`class_dim`表示类别数。这里，我们使用[`sequence_conv_pool`](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/trainer_config_helpers/networks.py) API实现了卷积和池化操作。
@@ -154,27 +156,26 @@ return prediction
 ```python
 def stacked_lstm_net(data, input_dim, class_dim, emb_dim, hid_dim, stacked_num):
 
-emb = fluid.layers.embedding(
-input=data, size=[input_dim, emb_dim], is_sparse=True)
+    emb = fluid.layers.embedding(
+        input=data, size=[input_dim, emb_dim], is_sparse=True)
 
-fc1 = fluid.layers.fc(input=emb, size=hid_dim)
-lstm1, cell1 = fluid.layers.dynamic_lstm(input=fc1, size=hid_dim)
+    fc1 = fluid.layers.fc(input=emb, size=hid_dim)
+    lstm1, cell1 = fluid.layers.dynamic_lstm(input=fc1, size=hid_dim)
 
-inputs = [fc1, lstm1]
+    inputs = [fc1, lstm1]
 
-for i in range(2, stacked_num + 1):
-fc = fluid.layers.fc(input=inputs, size=hid_dim)
-lstm, cell = fluid.layers.dynamic_lstm(
-input=fc, size=hid_dim, is_reverse=(i % 2) == 0)
-inputs = [fc, lstm]
+    for i in range(2, stacked_num + 1):
+        fc = fluid.layers.fc(input=inputs, size=hid_dim)
+        lstm, cell = fluid.layers.dynamic_lstm(
+            input=fc, size=hid_dim, is_reverse=(i % 2) == 0)
+        inputs = [fc, lstm]
 
-fc_last = fluid.layers.sequence_pool(input=inputs[0], pool_type='max')
-lstm_last = fluid.layers.sequence_pool(input=inputs[1], pool_type='max')
+    fc_last = fluid.layers.sequence_pool(input=inputs[0], pool_type='max')
+    lstm_last = fluid.layers.sequence_pool(input=inputs[1], pool_type='max')
 
-prediction = fluid.layers.fc(input=[fc_last, lstm_last],
-size=class_dim,
-act='softmax')
-return prediction
+    prediction = fluid.layers.fc(
+        input=[fc_last, lstm_last], size=class_dim, act='softmax')
+    return prediction
 ```
 以上的栈式双向LSTM抽象出了高级特征并把其映射到和分类类别数同样大小的向量上。`paddle.activation.Softmax`函数用来计算分类属于某个类别的概率。
 
@@ -184,12 +185,13 @@ return prediction
 
 ```python
 def inference_program(word_dict):
-data = fluid.layers.data(
-name="words", shape=[1], dtype="int64", lod_level=1)
+    data = fluid.layers.data(
+        name="words", shape=[1], dtype="int64", lod_level=1)
 
-dict_dim = len(word_dict)
-net = convolution_net(data, dict_dim, CLASS_DIM, EMB_DIM, HID_DIM)
-return net
+    dict_dim = len(word_dict)
+    net = convolution_net(data, dict_dim, CLASS_DIM, EMB_DIM, HID_DIM)
+    # net = stacked_lstm_net(data, dict_dim, CLASS_DIM, EMB_DIM, HID_DIM, STACKED_NUM)
+    return net
 ```
 
 我们这里定义了`training_program`。它使用了从`inference_program`返回的结果来计算误差。我们同时定义了优化函数`optimizer_func`。
@@ -200,16 +202,16 @@ return net
 
 ```python
 def train_program(word_dict):
-prediction = inference_program(word_dict)
-label = fluid.layers.data(name="label", shape=[1], dtype="int64")
-cost = fluid.layers.cross_entropy(input=prediction, label=label)
-avg_cost = fluid.layers.mean(cost)
-accuracy = fluid.layers.accuracy(input=prediction, label=label)
-return [avg_cost, accuracy]
+    prediction = inference_program(word_dict)
+    label = fluid.layers.data(name="label", shape=[1], dtype="int64")
+    cost = fluid.layers.cross_entropy(input=prediction, label=label)
+    avg_cost = fluid.layers.mean(cost)
+    accuracy = fluid.layers.accuracy(input=prediction, label=label)
+    return [avg_cost, accuracy]
 
 
 def optimizer_func():
-return fluid.optimizer.Adagrad(learning_rate=0.002)
+    return fluid.optimizer.Adagrad(learning_rate=0.002)
 ```
 
 ## 训练模型
@@ -236,9 +238,9 @@ word_dict = paddle.dataset.imdb.word_dict()
 
 print ("Reading training data....")
 train_reader = paddle.batch(
-paddle.reader.shuffle(
-paddle.dataset.imdb.train(word_dict), buf_size=25000),
-batch_size=BATCH_SIZE)
+    paddle.reader.shuffle(
+        paddle.dataset.imdb.train(word_dict), buf_size=25000),
+    batch_size=BATCH_SIZE)
 ```
 
 ### 构造训练器(trainer)
@@ -246,9 +248,9 @@ batch_size=BATCH_SIZE)
 
 ```python
 trainer = fluid.Trainer(
-train_func=partial(train_program, word_dict),
-place=place,
-optimizer_func=optimizer_func)
+    train_func=partial(train_program, word_dict),
+    place=place,
+    optimizer_func=optimizer_func)
 ```
 
 ### 提供数据
@@ -268,13 +270,13 @@ feed_order = ['words', 'label']
 params_dirname = "understand_sentiment_conv.inference.model"
 
 def event_handler(event):
-if isinstance(event, fluid.EndStepEvent):
-print("Step {0}, Epoch {1} Metrics {2}".format(
-event.step, event.epoch, map(np.array, event.metrics)))
+    if isinstance(event, fluid.EndStepEvent):
+        print("Step {0}, Epoch {1} Metrics {2}".format(
+                event.step, event.epoch, map(np.array, event.metrics)))
 
-if event.step == 10:
-trainer.save_params(params_dirname)
-trainer.stop()
+        if event.step == 10:
+            trainer.save_params(params_dirname)
+            trainer.stop()
 ```
 
 ### 开始训练
@@ -283,10 +285,10 @@ trainer.stop()
 
 ```python
 trainer.train(
-num_epochs=1,
-event_handler=event_handler,
-reader=train_reader,
-feed_order=feed_order)
+    num_epochs=1,
+    event_handler=event_handler,
+    reader=train_reader,
+    feed_order=feed_order)
 ```
 
 ## 应用模型
@@ -297,7 +299,7 @@ feed_order=feed_order)
 
 ```python
 inferencer = fluid.Inferencer(
-inference_program, param_path=params_dirname, place=place)
+        infer_func=partial(inference_program, word_dict), param_path=params_dirname, place=place)
 ```
 
 ### 生成测试用输入数据
@@ -307,14 +309,14 @@ inference_program, param_path=params_dirname, place=place)
 
 ```python
 reviews_str = [
-'read the book forget the movie', 'this is a great movie', 'this is very bad'
+    'read the book forget the movie', 'this is a great movie', 'this is very bad'
 ]
 reviews = [c.split() for c in reviews_str]
 
 UNK = word_dict['<unk>']
 lod = []
 for c in reviews:
-lod.append([word_dict.get(words, UNK) for words in c])
+    lod.append([word_dict.get(words, UNK) for words in c])
 
 base_shape = [[len(c) for c in lod]]
 
@@ -329,7 +331,7 @@ tensor_words = fluid.create_lod_tensor(lod, base_shape, place)
 results = inferencer.infer({'words': tensor_words})
 
 for i, r in enumerate(results[0]):
-print("Predict probability of ", r[0], " to be positive and ", r[1], " to be negative for review \'", reviews_str[i], "\'")
+    print("Predict probability of ", r[0], " to be positive and ", r[1], " to be negative for review \'", reviews_str[i], "\'")
 
 ```
 

doc/fluid/new_docs/beginners_guide/basics/word2vec/.gitignore

-data/train.list
-data/test.list
-data/simple-examples*

doc/fluid/new_docs/beginners_guide/quick_start/fit_a_line/README.cn.md

@@ -231,11 +231,13 @@ trainer.train(
     event_handler=event_handler_plot,
     feed_order=feed_order)
 ```
+
 <p align="center">
-    <img src = "image/train_and_test.png" width=400><br/>
+    <img src = "image/train_and_test1.png" width=400><br/>
     图3. 训练结果
 </p>
 
+
 ## 预测
 提供一个`inference_program`和一个`params_dirname`来初始化预测器。`params_dirname`用来存储我们的参数。