* Change contribute to paddle to fit new branching model

* Python trainer API and demo

* Adding missing PaddleAPIPrivate.h

* Adding api_train.sh

* More comments

* Bump up patch version to 0b3

* add input sparse data check for sparse layer at runtime,
to avoid invalid data access at pserver end while doing prefetch

* remote sparse design support binary sparse and float saprse both

Compiled failed on gcc 4.6

* add maxout layer, including interface and unittest

* follow maxout comments

* auto setting channels

* fix unittest bug in test_RecurrentGradientMachine

* fix error in doc of quick_start
* There are some warning when execute preprocess.sh 

* Follow #223
* Fix #222

* Fix sparse training for trainer_count=1

For trainer_count=1, the gradient machine is NeuralNetwork, which does not create parameter buf for PARAMETER_GRADIENT for sparse update in Parameter::enableType. But gradient parameter buf is still used in SgdThreadUpdater.

* Minor update to comment

* Fix bug in yield dictionary in DataProvider.
* Also make virtualenv work in Paddle.

* Init commit for doing FAQ

* Add speed up training

* Add graphviz to ci

* Add shared paramter

* Tiny refine

* hierarchical rnn document, add new config example

* update inputs_type of label

* add check for unsupported config

* refine hierarchical document

* refine doc title

* update docs, fix paddle to PaddlePaddle

* follow comments

* fix DataProvider create function args bug

Change-Id: I9e3a1c535c805bf30204a14aea8d5143ff534784

* remove PserverForPython.h which is not used

Change-Id: I2b27f1f3c11a42766a92fc689f0f5f1f73ee1d70

* add internal document script

Change-Id: Ia0fec79456caea0b271f9903cc13e8a3d32e0774

* add interface and unittest for nce layer

* follow comments

* added mnist demo

* modified .gitignore for .project files

* normalize pixel in mnist_provider.py and set use_gpu=0

* If cmake -DWITH_AVX=OFF during configuration, should not compile the file src/hl_math.cc and src/hl_avx_functions.cc.

* save the model file including git sha1

* Use C++ 11 atomic_flag in MacOS as spin lock
* Add unittest for it.

* support rectangle padding, stride, window and input for PoolProjection

* Follow comments.
1. Remove start
2. refine img_pool_a/b.conf for test_NetworkCompare
3. Split unit test

* Modify the test in img_layers.py