fix bug in img_norm_layer, several dead links and add missing files for semantic_role_labeling

ISSUE=4619881 

git-svn-id: https://svn.baidu.com/idl/trunk/paddle@1502 1ad973e4-5ce8-4261-8a94-b56d1f490c56

demo/recommendation/dataprovider.py

@@ -12,15 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-try:
-    import cPickle as pickle
-except ImportError:
-    import pickle
-
 from paddle.trainer.PyDataProvider2 import *
 import common_utils  # parse
 
-
 def hook(settings, meta, **kwargs):
     """
     Init hook is invoked before process data. It will set obj.slots and store
@@ -47,7 +41,6 @@ def hook(settings, meta, **kwargs):
     settings.input_types = headers
     settings.meta = meta
 
-
 @provider(init_hook=hook, cache=CacheType.CACHE_PASS_IN_MEM)
 def process(settings, filename):
     with open(filename, 'r') as f:

doc/demo/quick_start/index_en.md

@@ -157,9 +157,7 @@ define_py_data_sources2(train_list='data/train.list',
                         obj="process",
                         args={"dictionary": word_dict})
 ```
-
-You can refer to the following link for more detailed examples
-: <a href = "../../ui/data_provider/python_case.html">Python Use Case</a>，The detailed documentation on data format is: <a href = "../../ui/api/py_data_provider_wrapper.html"> PyDataProviderWrapper</a>。
+You can refer to the following link for more detailed examples and data formats: <a href = "../../ui/data_provider/pydataprovider2.html">PyDataProvider2</a>.
 
 ## Network Architecture
 You will describe four kinds of network architectures in this section.

doc/demo/rec/ml_regression.rst

@@ -219,9 +219,9 @@ The network structure shows below.
 
 The demo's neural network config file "trainer_config.py" show as below.
 
-..  include:: ../../../demo/recommendation/trainer_config.py
-    :code: python
-    :literal:
+..  literalinclude:: ../../../demo/recommendation/trainer_config.py
+    :language: python
+    :lines: 15-
 
 In this :code:`trainer_config.py`, we just map each feature type to
 a feature vector, following shows how to map each feature to a vector shows below.
@@ -263,9 +263,9 @@ In these network, we use several api in `trainer_config_helpers
 Data Provider
 '''''''''''''
 
-..  include:: ../../../demo/recommendation/dataprovider.py
-    :code: python
-    :literal:
+..  literalinclude:: ../../../demo/recommendation/dataprovider.py
+    :language: python
+    :lines: 15-
 
 The data provider just read the meta.bin and rating file, yield each sample for training.
 In this :code:`dataprovider.py`, we should set\:
@@ -274,7 +274,7 @@ In this :code:`dataprovider.py`, we should set\:
 * use_seq\: Whether this :code:`dataprovider.py` in sequence mode or not.
 * process\: Return each sample of data to :code:`paddle`.
 
-The data provider details document see `there <../../ui/DataProvider.html>`_.
+The data provider details document see `there <../../ui/data_provider/pydataprovider2.html>`_.
 
 Train
 `````
@@ -283,9 +283,9 @@ After prepare data, config network, writting data provider, now we can run paddl
 
 The run.sh is shown as follow:
 
-..  include:: ../../../demo/recommendation/run.sh
-    :code: bash
-    :literal:
+..  literalinclude:: ../../../demo/recommendation/run.sh
+    :language: bash
+    :lines: 16-
 
 It just start a paddle training process, write the log to `log.txt`,
 then print it on screen.

doc/demo/semantic_role_labeling/index.rst

+Semantic Role Labeling Tutorial
+===============================
+
+.. toctree::
+    :maxdepth: 3
+
+    semantic_role_labeling.md

doc/demo/semantic_role_labeling/semantic_role_labeling.md

+# Semantic Role labeling Tutorial #
+
+Semantic role labeling (SRL) is a form of shallow semantic parsing whose goal is to discover the predicate-argument structure of each predicate in a given input sentence. SRL is useful as an intermediate step in a wide range of natural language processing tasks, such as information extraction. automatic document categorization and question answering.  An instance is as following [1]:
+
+ [ <sub>A0</sub> He ] [ <sub>AM-MOD</sub> would ][ <sub>AM-NEG</sub> n’t ] [ <sub>V</sub> accept] [ <sub>A1</sub> anything of value ] from [<sub>A2</sub> those he was writing about ]. 
+
+- V: verb
+- A0: acceptor
+- A1: thing accepted
+- A2: accepted-from
+- A3: Attribute
+- AM-MOD: modal 
+- AM-NEG: negation
+
+Given the verb "accept", the chunks in sentence would play certain semantic roles. Here, the label scheme is from Penn Proposition Bank. 
+
+To this date, most of the successful SRL systems are built on top of some form of parsing results where pre-defined feature templates over the syntactic structure are used. This tutorial will present an end-to-end system using deep bidirectional long short-term memory (DB-LSTM)[2] for solving the SRL task, which largely outperforms the previous state-of-the-art systems. The system regards SRL task as the sequence labelling problem. 
+
+## Data Description
+The relevant paper[2] takes the data set in CoNLL-2005&2012 Shared Task for training and testing. Accordingto data license,  the demo adopts the test data set of CoNLL-2005, which can be reached on website.
+
+To download and process the original data, user just need to execute the following command:
+
+```bash
+cd data
+./get_data.sh
+```
+Several new files appear in the `data `directory as follows.
+```bash
+conll05st-release：the test data set of CoNll-2005 shared task 
+test.wsj.words：the Wall Street Journal data sentences
+test.wsj.props:  the propositional arguments
+src.dict：the dictionary of words in sentences
+tgt.dict：the labels dictionary
+feature: the extracted features from data set
+```
+
+## Training
+### DB-LSTM
+Please refer to the Sentiment Analysis demo to learn more about the long short-term memory unit. 
+
+Unlike Bidirectional-LSTM that used in Sentiment Analysis demo,  the DB-LSTM adopts another way to stack LSTM layer. First a standard LSTM processes the sequence in forward direction. The input and output of this LSTM layer are taken by the next LSTM layer as input, processed in reversed direction. These two standard LSTM layers compose a pair of LSTM. Then we stack LSTM layers pair after pair to obtain the deep LSTM model. 
+
+The following figure shows a temporal expanded 2-layer DB-LSTM network.
+<center>
+![pic](./network_arch.png)
+</center>
+
+### Features
+Two input features play an essential role in this pipeline: predicate (pred) and argument (argu). Two other features: predicate context (ctx-p) and region mark (mr) are also adopted. Because a single predicate word can not exactly describe the predicate information, especially when the same words appear more than one times in a sentence. With the predicate context, the ambiguity can be largely eliminated. Similarly, we use region mark m<sub>r</sub> = 1 to denote the argument position if it locates in the predicate context region, or m<sub>r</sub> = 0 if does not. These four simple features are all we need for our SRL system. Features of one sample with context size set to 1 is showed as following[2]:
+<center>
+![pic](./feature.jpg)
+</center>
+
+In this sample, the coresponding labelled sentence is:
+
+[ <sub>A1</sub> A record date ] has [ <sub>AM-NEG</sub> n't ] been [ <sub>V</sub> set ] . 
+
+In the demo, we adopt the feature template as above, consists of :  `argument`, `predicate`, `ctx-p (p=-1,0,1)`, `mark` and use `B/I/O` scheme to label each argument. These features and labels are stored in `feature` file, and separated by `\t`.
+
+### Data Provider
+
+`dataprovider.py` is the python file to wrap data. `hook()` function is to define the data slots for network. The  Six features and label are all IndexSlots.
+```
+def hook(settings, word_dict, label_dict, **kwargs):
+    settings.word_dict = word_dict
+    settings.label_dict = label_dict
+    #all inputs are integral and sequential type
+    settings.slots = [
+        integer_value_sequence(len(word_dict)),
+        integer_value_sequence(len(word_dict)),
+        integer_value_sequence(len(word_dict)),
+        integer_value_sequence(len(word_dict)),
+        integer_value_sequence(len(word_dict)),
+        integer_value_sequence(2),
+        integer_value_sequence(len(label_dict))]
+```
+The corresponding data iterator is as following:
+```
+@provider(use_seq=True, init_hook=hook)
+def process(obj, file_name):
+    with open(file_name, 'r') as fdata:
+        for line in fdata:
+            sentence, predicate, ctx_n1, ctx_0, ctx_p1, mark, label = line.strip().split('\t')
+            words = sentence.split()
+            sen_len = len(words)
+            word_slot = [obj.word_dict.get(w, UNK_IDX) for w in words]
+
+            predicate_slot = [obj.word_dict.get(predicate, UNK_IDX)] * sen_len
+            ctx_n1_slot = [obj.word_dict.get(ctx_n1, UNK_IDX) ] * sen_len
+            ctx_0_slot = [obj.word_dict.get(ctx_0, UNK_IDX) ] * sen_len
+            ctx_p1_slot = [obj.word_dict.get(ctx_p1, UNK_IDX) ] * sen_len
+
+            marks = mark.split()
+            mark_slot = [int(w) for w in marks]
+
+            label_list = label.split()
+            label_slot = [obj.label_dict.get(w) for w in label_list]
+
+            yield word_slot, predicate_slot, ctx_n1_slot, ctx_0_slot, ctx_p1_slot, mark_slot, label_slot
+```
+The `process`function yield 7 lists which are six features and labels.
+ 
+### Neural Network Config
+`db_lstm.py` is the neural network config file to load the dictionaries and define the  data provider module and network architecture during the training procedure. 
+
+Seven `data_layer` load instances from data provider. Six features are transformed into embedddings respectively, and mixed by `mixed_layer` .  Deep bidirectional LSTM layers extract features for the softmax layer. The objective function is cross entropy of labels.
+
+### Run Training 
+The script for training is `train.sh`, user just need to execute:
+```bash
+  ./train.sh
+```
+The content in `train.sh`:
+```
+paddle train \
+  --config=./db_lstm.py \
+  --save_dir=./output \
+  --trainer_count=4 \
+  --log_period=10 \
+  --num_passes=500 \
+  --use_gpu=false \
+  --show_parameter_stats_period=10 \
+  --test_all_data_in_one_period=1 \
+2>&1 | tee 'train.log'
+```
+
+-  \--config=./db_lstm.py : network config file.
+-  \--save_di=./output: output path to save models.
+-  \--trainer_count=4 : set thread number (or GPU count).
+-  \--log_period=10 : print log every 20 batches.
+-  \--num_passes=500: set pass number, one pass in PaddlePaddle means training all samples in dataset one time.
+-  \--use_gpu=false: use CPU to train, set true, if you install GPU version of PaddlePaddle and want to use GPU to train.
+-  \--show_parameter_stats_period=10: show parameter statistic every 100 batches.
+-  \--test_all_data_in_one_period=1: test all data in every testing.
+
+
+After training, the models  will be saved in directory `output`.
+
+### Run testing
+The script for testing is `test.sh`, user just need to execute:
+```bash
+  ./test.sh
+```
+The main part in `tesh.sh`
+```
+paddle train \
+  --config=./db_lstm.py \
+  --model_list=$model_list \
+  --job=test \
+  --config_args=is_test=1 \
+```
+
+  - \--config=./db_lstm.py: network config file
+  - \--model_list=$model_list.list: model list file
+  - \--job=test: indicate the test job
+  - \--config_args=is_test=1: flag to indicate test
+  
+
+### Run prediction
+The script for prediction is `predict.sh`, user just need to execute:
+```bash
+  ./predict.sh
+  
+```
+In `predict.sh`, user should offer the network config file, model path, label file, word dictionary file, feature file
+```
+python predict.py 
+     -c $config_file 
+     -w $model_path 
+     -l $label_file 
+     -d $dict_file 
+     -i $input_file
+```
+
+`predict.py` is the main executable python script, which includes functions: load model, load data, data prediction. The network model will output the probability distribution of labels. In the demo, we take the label with maximum probability as result. User can also implement the beam search or viterbi decoding upon the probability distribution matrix.
+
+After prediction,  the result is saved in `predict.res`.
+
+## Reference
+[1] Martha Palmer, Dan Gildea, and Paul Kingsbury. The Proposition Bank: An Annotated Corpus of Semantic Roles , Computational Linguistics, 31(1), 2005. 
+
+[2] Zhou, Jie, and Wei Xu. "End-to-end learning of semantic role labeling using recurrent neural networks." Proceedings of the Annual Meeting of the Association for Computational Linguistics. 2015.

doc/ui/data_provider/index.rst

-PaddlePaddle DataProvider Introduction
-================================
+DataProvider Introduction
+=========================
 DataProvider is a module that loads training or testing data into cpu or gpu
 memory for the following triaining or testing process.
 

doc/ui/predict/swig_py_paddle_en.rst

@@ -28,7 +28,7 @@ python's :code:`help()` function. Let's walk through the above python script:
     - Note: As swig_paddle can only accept C++ matrices, we offer a utility
       class DataProviderWraaperConverter that can accept the same input data with
       PyDataProviderWrapper, for more information please refer to document
-      of `PyDataProviderWrapper <../py_data_provider_wrapper_api.html>`_.
+      of `PyDataProvider2 <../data_provider/pydataprovider2.html>`_.
 * Do the prediction and output the result at line 100, forwardTest is another
   utility class that directly takes the activations of the output layer.
 

python/paddle/trainer_config_helpers/layers.py

@@ -1500,7 +1500,7 @@ def img_pool_layer(input, pool_size, name=None,
 
 
 def __img_norm_layer__(name, input, size, norm_type, scale, power,
-                       num_channels, blocked=0, layer_attr):
+                       num_channels, blocked, layer_attr):
     if num_channels is None:
         assert input.num_filters is not None
         num_channels = input.num_filters