When the define_py_data_sources2 has both train_list and test_list,
for job=test, the trainer will create both dataProvider_ and testDataProvider_.
But dataProvider_ is not used. This causes SIGSEGV at finishAsync() because asyncLoader_ is not created.

Change-Id: If579f715f80a70ebc795094792c3436bfa0f5746

 * always do test on all test data
 * do test at the end of each pass if test_period=0, otherwise do test if test_period batches passed

* Check all files by pre commit hooks

* 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.

* Also fix #243 

* Python trainer API and demo

* Adding missing PaddleAPIPrivate.h

* Adding api_train.sh

* More comments

* Bump up patch version to 0b3

* 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

* add interface and unittest for nce layer

* follow comments