* Because it is a cuda source file, and we need to support c++ 03 in
  cuda.

* Different Type compare.
* ostream << should pass a const object.
* remove always true checks.

* Because it is a cuda source file, and we need to support c++ 03 in
  cuda.

* Different Type compare.
* ostream << should pass a const object.
* remove always true checks.

Change-Id: Ic80845c892c96c37a0df0ddc433fe1aeaa5a9d1c

out = w * input
where w is a parameter of size 1

Change-Id: Ife682d62323ceb1a20cbbf6269421b20a862d888

* Check all files by pre commit hooks

* Add matrix inverse

* 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.

* 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

* add maxout layer, including interface and unittest

* follow maxout comments

* auto setting channels

* fix unittest bug in test_RecurrentGradientMachine

* 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

* reduce trainer count for unit test on MAC OSX

* using map to replace unordered_map on Mac