Merge branch 'develop' of github.com:baidu/Paddle into feature/refine_doc_drnn

Author: reyoung

CMakeLists.txt

@@ -2,8 +2,8 @@ cmake_minimum_required(VERSION 2.8)
 
 project(paddle CXX C)
 set(PADDLE_MAJOR_VERSION 0)
-set(PADDLE_MINOR_VERSION 8)
-set(PADDLE_PATCH_VERSION 0b3)
+set(PADDLE_MINOR_VERSION 9)
+set(PADDLE_PATCH_VERSION 0a0)
 set(PADDLE_VERSION ${PADDLE_MAJOR_VERSION}.${PADDLE_MINOR_VERSION}.${PADDLE_PATCH_VERSION})
 
 set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${CMAKE_SOURCE_DIR}/cmake")

README.md

@@ -14,7 +14,7 @@ developed by Baidu scientists and engineers for the purpose of applying deep
 learning to many products at Baidu.
 
 Our vision is to enable deep learning for everyone via PaddlePaddle.
-Please refer to our [release announcement](https://github.com/baidu/Paddle/releases) to track the latest feature of PaddlePaddle. 
+Please refer to our [release announcement](https://github.com/baidu/Paddle/releases) to track the latest feature of PaddlePaddle.
 
 ## Features
 
@@ -26,15 +26,15 @@ Please refer to our [release announcement](https://github.com/baidu/Paddle/relea
     connection.
 
 -  **Efficiency**
-  
+
     In order to unleash the power of heterogeneous computing resource,
     optimization occurs at different levels of PaddlePaddle, including
     computing, memory, architecture and communication. The following are some
     examples:
 
       - Optimized math operations through SSE/AVX intrinsics, BLAS libraries
-      (e.g. MKL, ATLAS, cuBLAS) or customized CPU/GPU kernels. 
-      - Highly optimized recurrent networks which can handle **variable-length** 
+      (e.g. MKL, ATLAS, cuBLAS) or customized CPU/GPU kernels.
+      - Highly optimized recurrent networks which can handle **variable-length**
       sequence without padding.
       - Optimized local and distributed training for models with high dimensional
       sparse data.
@@ -57,41 +57,39 @@ Please refer to our [release announcement](https://github.com/baidu/Paddle/relea
 
 ## Installation
 Check out the [Install Guide](http://paddlepaddle.org/doc/build/) to install from
-pre-built packages (**docker image**, **deb package**) or 
+pre-built packages (**docker image**, **deb package**) or
 directly build on **Linux** and **Mac OS X** from the source code.
- 
+
 ## Documentation
 Both [English Docs](http://paddlepaddle.org/doc/) and [Chinese Docs](http://paddlepaddle.org/doc_cn/) are provided for our users and developers.
 
 - [Quick Start](http://paddlepaddle.org/doc/demo/quick_start/index_en) <br>
    You can follow the quick start tutorial to learn how use PaddlePaddle
    step-by-step.
-    
+
 - [Example and Demo](http://paddlepaddle.org/doc/demo/) <br>
    We provide five demos, including: image classification, sentiment analysis,
-   sequence to sequence model, recommendation, semantic role labeling. 
-   
+   sequence to sequence model, recommendation, semantic role labeling.
+
 - [Distributed Training](http://paddlepaddle.org/doc/cluster) <br>
   This system supports training deep learning models on multiple machines
   with data parallelism.
-   
+
 - [Python API](http://paddlepaddle.org/doc/ui/) <br>
    PaddlePaddle supports using either Python interface or C++ to build your
    system. We also use SWIG to wrap C++ source code to create a user friendly
    interface for Python. You can also use SWIG to create interface for your
    favorite programming language.
- 
+
 - [How to Contribute](http://paddlepaddle.org/doc/build/contribute_to_paddle.html) <br>
    We sincerely appreciate your interest and contributions. If you would like to
-   contribute, please read the contribution guide.   
+   contribute, please read the contribution guide.
 
 - [Source Code Documents](http://paddlepaddle.org/doc/source/) <br>
 
 ## Ask Questions
-Please join the [**gitter chat**](https://gitter.im/PaddlePaddle/Deep_Learning) or send email to
-**[email protected]** to ask questions and talk about methods and models.
-Framework development discussions and
-bug reports are collected on [Issues](https://github.com/baidu/paddle/issues).
+
+You are welcome to submit questions and bug reports as [Github Issues](https://github.com/baidu/paddle/issues).
 
 ## Copyright and License
 PaddlePaddle is provided under the [Apache-2.0 license](LICENSE).

demo/sentiment/trainer_config.py

@@ -29,6 +29,7 @@
     batch_size=128,
     learning_rate=2e-3,
     learning_method=AdamOptimizer(),
+    average_window=0.5,
     regularization=L2Regularization(8e-4),
     gradient_clipping_threshold=25)
 

doc/algorithm/rnn/rnn.rst

@@ -17,7 +17,7 @@ PaddlePaddle does not need any preprocessing to sequence data, such as padding.
 
 .. code-block:: python
 
-    settings.slots = [
+    settings.input_types = [
       integer_value_sequence(len(settings.src_dict)),
       integer_value_sequence(len(settings.trg_dict)),
       integer_value_sequence(len(settings.trg_dict))]

doc/demo/sentiment_analysis/sentiment_analysis.md

@@ -6,7 +6,7 @@ Sentiment analysis is also used to monitor social media based on large amount of
 
 On the other hand, grabbing the user comments of products and analyzing their sentiment are useful to understand user preferences for companies, products, even competing products.
 
-This tutorial will guide you through the process of training a Long Short Term Memory (LSTM) Network to classify the sentiment of sentences from [Large Movie Review Dataset](http://ai.stanford.edu/~amaas/data/sentiment/), sometimes known as the [Internet Movie Database (IMDB)](http://ai.stanford.edu/~amaas/papers/wvSent_acl2011.pdf). This dataset contains movie reviews along with their associated binary sentiment polarity labels, namely positive and negative. So randomly guessing yields 50% accuracy.
+This tutorial will guide you through the process of training a Long Short Term Memory (LSTM) Network to classify the sentiment of sentences from [Large Movie Review Dataset](http://ai.stanford.edu/~amaas/data/sentiment/), sometimes known as the Internet Movie Database (IMDB). This dataset contains movie reviews along with their associated binary sentiment polarity labels, namely positive and negative. So randomly guessing yields 50% accuracy.
 
 ## Data Preparation
 
@@ -39,7 +39,7 @@ imdbEr.txt  imdb.vocab  README  test  train
 * imdbEr.txt: expected rating for each token in imdb.vocab.
 * README: data documentation.
 
-Both train and test set directory contains:
+The file in train set directory is as follows. The test set also contains them except `unsup` and `urls_unsup.txt`.
 
 ```
 labeledBow.feat  neg  pos  unsup  unsupBow.feat  urls_neg.txt  urls_pos.txt  urls_unsup.txt
@@ -151,6 +151,7 @@ settings(
   batch_size=128,
   learning_rate=2e-3,
   learning_method=AdamOptimizer(),
+  average_window=0.5,
   regularization=L2Regularization(8e-4),
   gradient_clipping_threshold=25
 )
@@ -163,17 +164,18 @@ stacked_lstm_net(dict_dim, class_dim=class_dim,
 
 * **Data Definition**:
    * get\_config\_arg(): get arguments setted by `--config_args=xx` in commandline argument.
-   * Define TrainData and TestData provider, here using Python interface (PyDataProviderWrapper) of PaddlePaddle to load data. For details, you can refer to the document of PyDataProvider.
+   * Define data provider, here using Python interface to load data. For details, you can refer to the document of PyDataProvider2.
 
 * **Algorithm Configuration**:
-   * use sgd algorithm.
-   * use adam optimization.
    * set batch size of 128.
-   * set average sgd window.
    * set global learning rate.
+   * use adam optimization.
+   * set average sgd window.
+   * set L2 regularization.
+   * set gradient clipping threshold.
 * **Network Configuration**:
-   * dict_dim: get dictionary dimension.
-   * class_dim: set category number, IMDB has two label, namely positive and negative label.
+   * dict_dim: dictionary dimension.
+   * class_dim: category number, IMDB has two label, namely positive and negative label.
    * `stacked_lstm_net`: predefined network as shown in Figure 3, use this network by default.
    * `bidirectional_lstm_net`: predefined network as shown in Figure 2.
 

doc/dev/new_layer/new_layer.rst

@@ -60,7 +60,7 @@ Implement C++ Class
 
 The C++ class of the layer implements the initialization, forward, and backward part of the layer. The fully connected layer is at :code:`paddle/gserver/layers/FullyConnectedLayer.h` and :code:`paddle/gserver/layers/FullyConnectedLayer.cpp`. We list simplified version of the code below.
 
-It needs to derive the base class :code:`paddle::BaseLayer`, and it needs to override the following functions:
+It needs to derive the base class :code:`paddle::Layer`, and it needs to override the following functions:
 
 - constructor and destructor.
 - :code:`init` function. It is used to initialize the parameters and settings.

doc/source/api/api.rst renamed to doc/source/api.rst

@@ -1,5 +1,5 @@
 API
-========
+===
 
 .. doxygenfile:: paddle/api/PaddleAPI.h
 .. doxygenfile:: paddle/api/Internal.h

doc/source/cuda/cuda/cuda.rst

@@ -0,0 +1,9 @@
+CUDA
+====
+
+.. toctree::
+  :maxdepth: 2
+
+  matrix.rst
+  nn.rst
+  utils.rst