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

Change-Id: Ife682d62323ceb1a20cbbf6269421b20a862d888

rebase deconv implementation with develop branch and resolve conflicts with pull#218 commit 45c81a41

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

Change-Id: Ic55aaabbf0c1299e70b8e48a0effcc91f8f5bd29

* 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

* 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

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

* 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

* 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

* add interface and unittest of RecurrentGradientMachine for the function of multiple Subsequence inlinks with unequal token length

* Correctly handling multiple inputs and integer inputs for recurrent_group

* Fix ScatterAgentLayer for generation

* Revert sequence_(nest)_rnn.conf

* Also refine unittest to multiple iteration to prevent luckily random number.