Added a test in test_NetworkCompare to verify cudnn and exconv, fixed a bug in cudnn_conv in dealing with groups

The instance weight and coeff are only mulipled to the gradient with respect to input.

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

Change-Id: Ife682d62323ceb1a20cbbf6269421b20a862d888

This allows user to implement any type of cost by summing over the output of non-cost layers.

Change-Id: Ic55aaabbf0c1299e70b8e48a0effcc91f8f5bd29

* 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 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

Change-Id: I636cf13af5becb1168bc9749266b55580c46f6c9

ISSUE=4586865

git-svn-id: https://svn.baidu.com/idl/trunk/paddle@1415 1ad973e4-5ce8-4261-8a94-b56d1f490c56

ISSUE=4586495

git-svn-id: https://svn.baidu.com/idl/trunk/paddle@1408 1ad973e4-5ce8-4261-8a94-b56d1f490c56