* some tiny fixs

* use VLOG(3)

* Init

* Add numpy deps

* Refine

* Fix SRL hang when exit.

* Error occurred when enable Async Load in TestDataProvider.
  * It because DataProvider is calling getNextBatchInternal in one thread, and destructing DataProvider in other thread.
  * Add wait routine in DataProvider destructing.
* Also fix another bug, when destructing TestDataProvider and do not read any test data.

Fix #286

* Follow comments, Use mutex is cool!

* Add elementwise math operations
This allows use to use expressions like: y=log(1+exp(x))
Also added unittests for ActivationFunction
* Enforce keyword arguments for non-positional arguments
* Add LogActivation to doc

* Fix glog check type unmatch in Util.cpp

#352 

* Add matrix inverse

For multiple installation of paddle, there might be multiple versions of python package at opt/paddle/share/wheels/. We should install the right version.
Ideally, we should remove the wrong versions when install. But it's not easy to do this with cmake.

Change-Id: Ida8a8d60643ad9e42cf1c85776de9122d5ba1392

* Forget to finishTestPeriod in testOnePeriod.
* Fix #318

* Add benchmark for PaddlePaddle, tensorflow and caffe

* ConvProjection to reduce memory for goolenet

* Add unit test for ConvProjection.
1. unit test in test_LayerGrad.
2. compare the ConvPorjection and CudnnConvLayer, also compare the concat_layer+img_conv_layer and concat_layer_conv_projection.

* Reduce cudnn_conv memory and add benchmark document.
1. Use TmpMatrix as the workspace in cudnn_conv to reduce gpu memory. It reduce lots of memory.
2. Add benchmark document.
3. fix smallnet_mnist_cifar.py in paddle.

* Add job=time and refine cudnn_conv to reduce gpu memroy and speed up

* Refine cudnn_conv and shared biases operation in concat_layer and mixed_layer.

* follow comments

* follow comments

* Use unique_ptr to prevent memory leaks in CudnnConvLayer.

* DYLD_LIBRARY_PATH is disable after Mac OS X 10.11
* fix clang + gpu compile error on Mac OS
* fix some words and errors in build docs

* Because in cluster maybe use a lot machine to train a model, and some parameter size could be too small for ParameterServer. Then some of pservers could not have any ParamBlock.
* Also, because ports_num or ports_num_for_sparse is too large, then give a warning in runtime.

* Also fix #243 

* 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 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.