Separate English/Chinese README.md and fix yapf config in pre-commit

.pre-commit-config.yaml

--   repo: https://github.com/reyoung/mirrors-yapf.git
-    sha: v0.13.2
+-   repo: https://github.com/pre-commit/mirrors-yapf.git
+    sha: v0.16.0
     hooks:
     -   id: yapf
-      files: (.*\.(py|bzl)|BUILD|.*\.BUILD|WORKSPACE)$  # Bazel BUILD files follow Python syntax.
+        files: \.py$
 -   repo: https://github.com/pre-commit/pre-commit-hooks
-    sha: v0.7.1
+    sha: a11d9314b22d8f8c7556443875b731ef05965464
     hooks:
     -   id: check-merge-conflict
     -   id: check-symlinks
@@ -28,7 +28,7 @@
     hooks:
     -   id: convert-markdown-into-html
         name: convert-markdown-into-html
-       description: "Convert README.md into index.html and README.en.md into index.en.html"
+        description: Convert README.md into index.html and README.en.md into index.en.html
         entry: python pre-commit-hooks/convert_markdown_into_html.py
         language: system
         files: \.md$

README.en.md

+# Deep Learning with PaddlePaddle
+
+1. [Fit a Line](http://book.paddlepaddle.org/fit_a_line/index.en.html)
+1. [Recognize Digits](http://book.paddlepaddle.org/recognize_digits/index.en.html)
+1. [Image Classification](http://book.paddlepaddle.org/image_classification/index.en.html)
+1. [Word to Vector](http://book.paddlepaddle.org/word2vec/index.en.html)
+1. [Understand Sentiment](http://book.paddlepaddle.org/understand_sentiment/index.en.html)
+1. [Label Semantic Roles](http://book.paddlepaddle.org/label_semantic_roles/index.en.html)
+1. [Machine Translation](http://book.paddlepaddle.org/machine_translation/index.en.html)
+1. [Recommender System](http://book.paddlepaddle.org/recommender_system/index.en.html)
+
+This tutorial is contributed by <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a>, and licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.

README.md

 # 深度学习入门
 
-1. [新手入门](fit_a_line/) [[html](http://book.paddlepaddle.org/fit_a_line)]
-1. [识别数字](recognize_digits/) [[html](http://book.paddlepaddle.org/recognize_digits)]
-1. [图像分类](image_classification/) [[html](http://book.paddlepaddle.org/image_classification)]
-1. [词向量](word2vec/) [[html](http://book.paddlepaddle.org/word2vec)]
-1. [情感分析](understand_sentiment/) [[html](http://book.paddlepaddle.org/understand_sentiment)]
-1. [语义角色标注](label_semantic_roles/) [[html](http://book.paddlepaddle.org/label_semantic_roles)]
-1. [机器翻译](machine_translation/) [[html](http://book.paddlepaddle.org/machine_translation)]
-1. [个性化推荐](recommender_system/) [[html](http://book.paddlepaddle.org/recommender_system)]
+1. [新手入门](http://book.paddlepaddle.org/fit_a_line)
+1. [识别数字](http://book.paddlepaddle.org/recognize_digits)
+1. [图像分类](http://book.paddlepaddle.org/image_classification)
+1. [词向量](http://book.paddlepaddle.org/word2vec)
+1. [情感分析](http://book.paddlepaddle.org/understand_sentiment)
+1. [语义角色标注](http://book.paddlepaddle.org/label_semantic_roles)
+1. [机器翻译](http://book.paddlepaddle.org/machine_translation)
+1. [个性化推荐](http://book.paddlepaddle.org/recommender_system)
 
 
-
-# Deep Learning Introduction
-
-1. [Fit a Line](fit_a_line/) [[html](http://book.paddlepaddle.org/fit_a_line/index.en.html)]
-1. [Recognize Digits](recognize_digits/) [[html](http://book.paddlepaddle.org/recognize_digits/index.en.html)]
-1. [Image Classification](image_classification/) [[html](http://book.paddlepaddle.org/image_classification/index.en.html)]
-1. [Word to Vector](word2vec/) [[html](http://book.paddlepaddle.org/word2vec/index.en.html)]
-1. [Understand Sentiment](understand_sentiment/) [[html](http://book.paddlepaddle.org/understand_sentiment/index.en.html)]
-1. [Label Semantic Roles](label_semantic_roles/) [[html](http://book.paddlepaddle.org/label_semantic_roles/index.en.html)]
-1. [Machine Translation](machine_translation/) [[html](http://book.paddlepaddle.org/machine_translation/index.en.html)]
-1. [Recommender System](recommender_system/) [[html](http://book.paddlepaddle.org/recommender_system/index.en.html)]
-
-<br/>
 <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">本教程</span> 由 <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a> 创作，采用 <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">知识共享 署名-非商业性使用-相同方式共享 4.0 国际 许可协议</a>进行许可。
-This tutorial is contributed by <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a>, and licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.

fit_a_line/train.py

@@ -18,9 +18,8 @@ def main():
     # create optimizer
     optimizer = paddle.optimizer.Momentum(momentum=0)
 
-    trainer = paddle.trainer.SGD(cost=cost,
-                                 parameters=parameters,
-                                 update_equation=optimizer)
+    trainer = paddle.trainer.SGD(
+        cost=cost, parameters=parameters, update_equation=optimizer)
 
     feeding = {'x': 0, 'y': 1}
 
@@ -33,16 +32,14 @@ def main():
 
         if isinstance(event, paddle.event.EndPass):
             result = trainer.test(
-                reader=paddle.batch(
-                    uci_housing.test(), batch_size=2),
+                reader=paddle.batch(uci_housing.test(), batch_size=2),
                 feeding=feeding)
             print "Test %d, Cost %f" % (event.pass_id, result.cost)
 
     # training
     trainer.train(
         reader=paddle.batch(
-            paddle.reader.shuffle(
-                uci_housing.train(), buf_size=500),
+            paddle.reader.shuffle(uci_housing.train(), buf_size=500),
             batch_size=2),
         feeding=feeding,
         event_handler=event_handler,

image_classification/deprecated/classify.py

@@ -44,7 +44,8 @@ def vis_square(data, fname):
          (0, 1))  # add some space between filters
         + ((0, 0), ) *
         (data.ndim - 3))  # don't pad the last dimension (if there is one)
-    data = np.pad(data, padding, mode='constant',
+    data = np.pad(
+        data, padding, mode='constant',
         constant_values=1)  # pad with ones (white)
     # tile the filters into an image
     data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(

image_classification/train.py

@@ -36,9 +36,8 @@ def main():
     # option 2. vgg
     net = vgg_bn_drop(image)
 
-    out = paddle.layer.fc(input=net,
-                          size=classdim,
-                          act=paddle.activation.Softmax())
+    out = paddle.layer.fc(
+        input=net, size=classdim, act=paddle.activation.Softmax())
 
     lbl = paddle.layer.data(
         name="label", type=paddle.data_type.integer_value(classdim))
@@ -75,9 +74,8 @@ def main():
             print "\nTest with Pass %d, %s" % (event.pass_id, result.metrics)
 
     # Create trainer
-    trainer = paddle.trainer.SGD(cost=cost,
-                                 parameters=parameters,
-                                 update_equation=momentum_optimizer)
+    trainer = paddle.trainer.SGD(
+        cost=cost, parameters=parameters, update_equation=momentum_optimizer)
     trainer.train(
         reader=paddle.batch(
             paddle.reader.shuffle(

index.en.html

+
+<html>
+<head>
+  <script type="text/x-mathjax-config">
+  MathJax.Hub.Config({
+    extensions: ["tex2jax.js", "TeX/AMSsymbols.js", "TeX/AMSmath.js"],
+    jax: ["input/TeX", "output/HTML-CSS"],
+    tex2jax: {
+      inlineMath: [ ['$','$'] ],
+      displayMath: [ ['$$','$$'] ],
+      processEscapes: true
+    },
+    "HTML-CSS": { availableFonts: ["TeX"] }
+  });
+  </script>
+  <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js" async></script>
+  <script type="text/javascript" src="../.tmpl/marked.js">
+  </script>
+  <link href="http://cdn.bootcss.com/highlight.js/9.9.0/styles/darcula.min.css" rel="stylesheet">
+  <script src="http://cdn.bootcss.com/highlight.js/9.9.0/highlight.min.js"></script>
+  <link href="http://cdn.bootcss.com/bootstrap/4.0.0-alpha.6/css/bootstrap.min.css" rel="stylesheet">
+  <link href="https://cdn.jsdelivr.net/perfect-scrollbar/0.6.14/css/perfect-scrollbar.min.css" rel="stylesheet">
+  <link href="../.tmpl/github-markdown.css" rel='stylesheet'>
+</head>
+<style type="text/css" >
+.markdown-body {
+    box-sizing: border-box;
+    min-width: 200px;
+    max-width: 980px;
+    margin: 0 auto;
+    padding: 45px;
+}
+</style>
+
+
+<body>
+
+<div id="context" class="container markdown-body">
+</div>
+
+<!-- This block will be replaced by each markdown file content. Please do not change lines below.-->
+<div id="markdown" style='display:none'>
+# Deep Learning with PaddlePaddle
+
+1. [Fit a Line](http://book.paddlepaddle.org/fit_a_line/index.en.html)
+1. [Recognize Digits](http://book.paddlepaddle.org/recognize_digits/index.en.html)
+1. [Image Classification](http://book.paddlepaddle.org/image_classification/index.en.html)
+1. [Word to Vector](http://book.paddlepaddle.org/word2vec/index.en.html)
+1. [Understand Sentiment](http://book.paddlepaddle.org/understand_sentiment/index.en.html)
+1. [Label Semantic Roles](http://book.paddlepaddle.org/label_semantic_roles/index.en.html)
+1. [Machine Translation](http://book.paddlepaddle.org/machine_translation/index.en.html)
+1. [Recommender System](http://book.paddlepaddle.org/recommender_system/index.en.html)
+
+This tutorial is contributed by <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a>, and licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.
+
+</div>
+<!-- You can change the lines below now. -->
+
+<script type="text/javascript">
+marked.setOptions({
+  renderer: new marked.Renderer(),
+  gfm: true,
+  breaks: false,
+  smartypants: true,
+  highlight: function(code, lang) {
+    code = code.replace(/&amp;/g, "&")
+    code = code.replace(/&gt;/g, ">")
+    code = code.replace(/&lt;/g, "<")
+    code = code.replace(/&nbsp;/g, " ")
+    return hljs.highlightAuto(code, [lang]).value;
+  }
+});
+document.getElementById("context").innerHTML = marked(
+        document.getElementById("markdown").innerHTML)
+</script>
+</body>

index.html

@@ -42,31 +42,17 @@
 <div id="markdown" style='display:none'>
 # 深度学习入门
 
-1. [新手入门](fit_a_line/) [[html](http://book.paddlepaddle.org/fit_a_line)]
-1. [识别数字](recognize_digits/) [[html](http://book.paddlepaddle.org/recognize_digits)]
-1. [图像分类](image_classification/) [[html](http://book.paddlepaddle.org/image_classification)]
-1. [词向量](word2vec/) [[html](http://book.paddlepaddle.org/word2vec)]
-1. [情感分析](understand_sentiment/) [[html](http://book.paddlepaddle.org/understand_sentiment)]
-1. [语义角色标注](label_semantic_roles/) [[html](http://book.paddlepaddle.org/label_semantic_roles)]
-1. [机器翻译](machine_translation/) [[html](http://book.paddlepaddle.org/machine_translation)]
-1. [个性化推荐](recommender_system/) [[html](http://book.paddlepaddle.org/recommender_system)]
+1. [新手入门](http://book.paddlepaddle.org/fit_a_line)
+1. [识别数字](http://book.paddlepaddle.org/recognize_digits)
+1. [图像分类](http://book.paddlepaddle.org/image_classification)
+1. [词向量](http://book.paddlepaddle.org/word2vec)
+1. [情感分析](http://book.paddlepaddle.org/understand_sentiment)
+1. [语义角色标注](http://book.paddlepaddle.org/label_semantic_roles)
+1. [机器翻译](http://book.paddlepaddle.org/machine_translation)
+1. [个性化推荐](http://book.paddlepaddle.org/recommender_system)
 
 
-
-# Deep Learning Introduction
-
-1. [Fit a Line](fit_a_line/) [[html](http://book.paddlepaddle.org/fit_a_line/index.en.html)]
-1. [Recognize Digits](recognize_digits/) [[html](http://book.paddlepaddle.org/recognize_digits/index.en.html)]
-1. [Image Classification](image_classification/) [[html](http://book.paddlepaddle.org/image_classification/index.en.html)]
-1. [Word to Vector](word2vec/) [[html](http://book.paddlepaddle.org/word2vec/index.en.html)]
-1. [Understand Sentiment](understand_sentiment/) [[html](http://book.paddlepaddle.org/understand_sentiment/index.en.html)]
-1. [Label Semantic Roles](label_semantic_roles/) [[html](http://book.paddlepaddle.org/label_semantic_roles/index.en.html)]
-1. [Machine Translation](machine_translation/) [[html](http://book.paddlepaddle.org/machine_translation/index.en.html)]
-1. [Recommender System](recommender_system/) [[html](http://book.paddlepaddle.org/recommender_system/index.en.html)]
-
-<br/>
 <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">本教程</span> 由 <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a> 创作，采用 <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">知识共享 署名-非商业性使用-相同方式共享 4.0 国际 许可协议</a>进行许可。
-This tutorial is contributed by <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a>, and licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.
 
 </div>
 <!-- You can change the lines below now. -->

label_semantic_roles/db_lstm.py

@@ -75,8 +75,7 @@ settings(
     learning_method=MomentumOptimizer(momentum=0),
     learning_rate=2e-2,
     regularization=L2Regularization(8e-4),
-    model_average=ModelAverage(
-        average_window=0.5, max_average_window=10000), )
+    model_average=ModelAverage(average_window=0.5, max_average_window=10000), )
 
 ####################################### network ##############################
 #8 features and 1 target
@@ -102,13 +101,12 @@ std_default = ParameterAttribute(initial_std=default_std)
 predicate_embedding = embedding_layer(
     size=word_dim,
     input=predicate,
-    param_attr=ParameterAttribute(
-        name='vemb', initial_std=default_std))
+    param_attr=ParameterAttribute(name='vemb', initial_std=default_std))
 
 word_input = [word, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2]
 emb_layers = [
-    embedding_layer(
-        size=word_dim, input=x, param_attr=emb_para) for x in word_input
+    embedding_layer(size=word_dim, input=x, param_attr=emb_para)
+    for x in word_input
 ]
 emb_layers.append(predicate_embedding)
 mark_embedding = embedding_layer(
@@ -120,8 +118,8 @@ hidden_0 = mixed_layer(
     size=hidden_dim,
     bias_attr=std_default,
     input=[
-        full_matrix_projection(
-            input=emb, param_attr=std_default) for emb in emb_layers
+        full_matrix_projection(input=emb, param_attr=std_default)
+        for emb in emb_layers
     ])
 
 mix_hidden_lr = 1e-3
@@ -171,10 +169,8 @@ feature_out = mixed_layer(
     size=label_dict_len,
     bias_attr=std_default,
     input=[
-        full_matrix_projection(
-            input=input_tmp[0], param_attr=hidden_para_attr),
-        full_matrix_projection(
-            input=input_tmp[1], param_attr=lstm_para_attr)
+        full_matrix_projection(input=input_tmp[0], param_attr=hidden_para_attr),
+        full_matrix_projection(input=input_tmp[1], param_attr=lstm_para_attr)
     ], )
 
 if not is_predict:

label_semantic_roles/train.py

@@ -40,15 +40,14 @@ def db_lstm():
     predicate_embedding = paddle.layer.embedding(
         size=word_dim,
         input=predicate,
-        param_attr=paddle.attr.Param(
-            name='vemb', initial_std=default_std))
+        param_attr=paddle.attr.Param(name='vemb', initial_std=default_std))
     mark_embedding = paddle.layer.embedding(
         size=mark_dim, input=mark, param_attr=std_0)
 
     word_input = [word, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2]
     emb_layers = [
-        paddle.layer.embedding(
-            size=word_dim, input=x, param_attr=emb_para) for x in word_input
+        paddle.layer.embedding(size=word_dim, input=x, param_attr=emb_para)
+        for x in word_input
     ]
     emb_layers.append(predicate_embedding)
     emb_layers.append(mark_embedding)
@@ -109,13 +108,12 @@ def db_lstm():
                 input=input_tmp[1], param_attr=lstm_para_attr)
         ], )
 
-    crf_cost = paddle.layer.crf(size=label_dict_len,
+    crf_cost = paddle.layer.crf(
+        size=label_dict_len,
         input=feature_out,
         label=target,
         param_attr=paddle.attr.Param(
-                                    name='crfw',
-                                    initial_std=default_std,
-                                    learning_rate=mix_hidden_lr))
+            name='crfw', initial_std=default_std, learning_rate=mix_hidden_lr))
 
     crf_dec = paddle.layer.crf_decoding(
         name='crf_dec_l',
@@ -151,13 +149,11 @@ def main():
         model_average=paddle.optimizer.ModelAverage(
             average_window=0.5, max_average_window=10000), )
 
-    trainer = paddle.trainer.SGD(cost=crf_cost,
-                                 parameters=parameters,
-                                 update_equation=optimizer)
+    trainer = paddle.trainer.SGD(
+        cost=crf_cost, parameters=parameters, update_equation=optimizer)
 
     reader = paddle.batch(
-        paddle.reader.shuffle(
-            conll05.test(), buf_size=8192), batch_size=10)
+        paddle.reader.shuffle(conll05.test(), buf_size=8192), batch_size=10)
 
     feeding = {
         'word_data': 0,

machine_translation/api_train.py

@@ -105,9 +105,8 @@ def main():
 
     # define optimize method and trainer
     optimizer = paddle.optimizer.Adam(learning_rate=1e-4)
-    trainer = paddle.trainer.SGD(cost=cost,
-                                 parameters=parameters,
-                                 update_equation=optimizer)
+    trainer = paddle.trainer.SGD(
+        cost=cost, parameters=parameters, update_equation=optimizer)
 
     # define data reader
     feeding = {

machine_translation/seqToseq_net.py

@@ -110,8 +110,7 @@ group_inputs = [group_input1, group_input2]
 
 if not is_generating:
     trg_embedding = embedding_layer(
-        input=data_layer(
-            name='target_language_word', size=target_dict_dim),
+        input=data_layer(name='target_language_word', size=target_dict_dim),
         size=word_vector_dim,
         param_attr=ParamAttr(name='_target_language_embedding'))
     group_inputs.append(trg_embedding)
@@ -156,8 +155,7 @@ else:
 
     seqtext_printer_evaluator(
         input=beam_gen,
-        id_input=data_layer(
-            name="sent_id", size=1),
+        id_input=data_layer(name="sent_id", size=1),
         dict_file=trg_lang_dict,
         result_file=gen_trans_file)
     outputs(beam_gen)

recognize_digits/train.py

@@ -2,9 +2,8 @@ import paddle.v2 as paddle
 
 
 def softmax_regression(img):
-    predict = paddle.layer.fc(input=img,
-                              size=10,
-                              act=paddle.activation.Softmax())
+    predict = paddle.layer.fc(
+        input=img, size=10, act=paddle.activation.Softmax())
     return predict
 
 
@@ -12,14 +11,12 @@ def multilayer_perceptron(img):
     # The first fully-connected layer
     hidden1 = paddle.layer.fc(input=img, size=128, act=paddle.activation.Relu())
     # The second fully-connected layer and the according activation function
-    hidden2 = paddle.layer.fc(input=hidden1,
-                              size=64,
-                              act=paddle.activation.Relu())
+    hidden2 = paddle.layer.fc(
+        input=hidden1, size=64, act=paddle.activation.Relu())
     # The thrid fully-connected layer, note that the hidden size should be 10,
     # which is the number of unique digits
-    predict = paddle.layer.fc(input=hidden2,
-                              size=10,
-                              act=paddle.activation.Softmax())
+    predict = paddle.layer.fc(
+        input=hidden2, size=10, act=paddle.activation.Softmax())
     return predict
 
 
@@ -43,14 +40,12 @@ def convolutional_neural_network(img):
         pool_stride=2,
         act=paddle.activation.Tanh())
     # The first fully-connected layer
-    fc1 = paddle.layer.fc(input=conv_pool_2,
-                          size=128,
-                          act=paddle.activation.Tanh())
+    fc1 = paddle.layer.fc(
+        input=conv_pool_2, size=128, act=paddle.activation.Tanh())
     # The softmax layer, note that the hidden size should be 10,
     # which is the number of unique digits
-    predict = paddle.layer.fc(input=fc1,
-                              size=10,
-                              act=paddle.activation.Softmax())
+    predict = paddle.layer.fc(
+        input=fc1, size=10, act=paddle.activation.Softmax())
     return predict
 
 
@@ -76,9 +71,8 @@ optimizer = paddle.optimizer.Momentum(
     momentum=0.9,
     regularization=paddle.optimizer.L2Regularization(rate=0.0005 * 128))
 
-trainer = paddle.trainer.SGD(cost=cost,
-                             parameters=parameters,
-                             update_equation=optimizer)
+trainer = paddle.trainer.SGD(
+    cost=cost, parameters=parameters, update_equation=optimizer)
 
 lists = []
 
@@ -99,8 +93,7 @@ def event_handler(event):
 
 trainer.train(
     reader=paddle.batch(
-        paddle.reader.shuffle(
-            paddle.dataset.mnist.train(), buf_size=8192),
+        paddle.reader.shuffle(paddle.dataset.mnist.train(), buf_size=8192),
         batch_size=128),
     event_handler=event_handler,
     num_passes=100)

recommender_system/data/meta_generator.py

@@ -208,8 +208,8 @@ class EmbeddingFieldParser(object):
         elif config['dict']['type'] == 'split':
             self.dict = SplitEmbeddingDict(config['dict'].get('delimiter', ','))
         elif config['dict']['type'] == 'whole_content':
-            self.dict = EmbeddingFieldParser.WholeContentDict(config['dict'][
-                'sort'])
+            self.dict = EmbeddingFieldParser.WholeContentDict(
+                config['dict']['sort'])
         else:
             print config
             assert False

understand_sentiment/train.py

@@ -24,9 +24,8 @@ def convolution_net(input_dim, class_dim=2, emb_dim=128, hid_dim=128):
         input=emb, context_len=3, hidden_size=hid_dim)
     conv_4 = paddle.networks.sequence_conv_pool(
         input=emb, context_len=4, hidden_size=hid_dim)
-    output = paddle.layer.fc(input=[conv_3, conv_4],
-                             size=class_dim,
-                             act=paddle.activation.Softmax())
+    output = paddle.layer.fc(
+        input=[conv_3, conv_4], size=class_dim, act=paddle.activation.Softmax())
     lbl = paddle.layer.data("label", paddle.data_type.integer_value(2))
     cost = paddle.layer.classification_cost(input=output, label=lbl)
     return cost
@@ -64,16 +63,15 @@ def stacked_lstm_net(input_dim,
                              paddle.data_type.integer_value_sequence(input_dim))
     emb = paddle.layer.embedding(input=data, size=emb_dim)
 
-    fc1 = paddle.layer.fc(input=emb,
-                          size=hid_dim,
-                          act=linear,
-                          bias_attr=bias_attr)
+    fc1 = paddle.layer.fc(
+        input=emb, size=hid_dim, act=linear, bias_attr=bias_attr)
     lstm1 = paddle.layer.lstmemory(
         input=fc1, act=relu, bias_attr=bias_attr, layer_attr=layer_attr)
 
     inputs = [fc1, lstm1]
     for i in range(2, stacked_num + 1):
-        fc = paddle.layer.fc(input=inputs,
+        fc = paddle.layer.fc(
+            input=inputs,
             size=hid_dim,
             act=linear,
             param_attr=para_attr,
@@ -90,7 +88,8 @@ def stacked_lstm_net(input_dim,
         input=inputs[0], pooling_type=paddle.pooling.Max())
     lstm_last = paddle.layer.pooling(
         input=inputs[1], pooling_type=paddle.pooling.Max())
-    output = paddle.layer.fc(input=[fc_last, lstm_last],
+    output = paddle.layer.fc(
+        input=[fc_last, lstm_last],
         size=class_dim,
         act=paddle.activation.Softmax(),
         bias_attr=bias_attr,
@@ -148,9 +147,8 @@ if __name__ == '__main__':
             print "\nTest with Pass %d, %s" % (event.pass_id, result.metrics)
 
     # create trainer
-    trainer = paddle.trainer.SGD(cost=cost,
-                                 parameters=parameters,
-                                 update_equation=adam_optimizer)
+    trainer = paddle.trainer.SGD(
+        cost=cost, parameters=parameters, update_equation=adam_optimizer)
 
     trainer.train(
         reader=train_reader,

word2vec/train.py

@@ -40,15 +40,16 @@ def main():
     Efourth = wordemb(fourthword)
 
     contextemb = paddle.layer.concat(input=[Efirst, Esecond, Ethird, Efourth])
-    hidden1 = paddle.layer.fc(input=contextemb,
+    hidden1 = paddle.layer.fc(
+        input=contextemb,
         size=hiddensize,
         act=paddle.activation.Sigmoid(),
         layer_attr=paddle.attr.Extra(drop_rate=0.5),
         bias_attr=paddle.attr.Param(learning_rate=2),
         param_attr=paddle.attr.Param(
-                                  initial_std=1. / math.sqrt(embsize * 8),
-                                  learning_rate=1))
-    predictword = paddle.layer.fc(input=hidden1,
+            initial_std=1. / math.sqrt(embsize * 8), learning_rate=1))
+    predictword = paddle.layer.fc(
+        input=hidden1,
         size=dict_size,
         bias_attr=paddle.attr.Param(learning_rate=2),
         act=paddle.activation.Softmax())