* add maxout layer, including interface and unittest

* follow maxout comments

* auto setting channels

* fix unittest bug in test_RecurrentGradientMachine

* Follow #223
* Fix #222

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

* Init commit for doing FAQ

* Add speed up training

* Add graphviz to ci

* Add shared paramter

* Tiny refine

* add interface and unittest for nce layer

* 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

* refine sparse momentum api and unittest
* fix unittests bug

* Fix lots of trainer_config_helpers bug, and complete unittest for `layers.py`

* Correctly handling multiple inputs and integer inputs for recurrent_group

* Fix ScatterAgentLayer for generation

* Revert sequence_(nest)_rnn.conf

* Add `device` parameter to ExtraAttr in trainer_config_helpers.
* add unittest for it.

* add type compatible check for ParamAttr

* fix bug in dotmul_operator's api and anotation
* update rnn document
* remove redundant info of projection and operator in layers.py

* remove unnecessary field set in ParameterConfig, Evaluators, etc

* split dotmul_projection and dotmul_operator
* bug fix in outputsize checking for mixed layer

* min_pool_size would be infinite by default.
  * add unittest for min_pool_size
* Fix bug in can_over_batch_size
  * add unittest for can_over_batch_size
* Add DEFINE_PROVIDER_EX
* Add default value of should_shuffle
  * When training, the default value of should_shuffle is True.
  * When testing, the default value of should_shuffle is False.
  * User a set a provider should_shuffle or not by pass it to `@provider`
  * should_shuffle can handle a list of value, not just boolean
* Add input order mapping by using name
  * Add unittest
* Add check to check input format.
  * Default is close for speed reason.
  * User could stop train when check error, or continue train without
    this train sample.
* use deque instead of vector in generators pool, make erase
  generator faster.
* Add chinese/english documentation
* Make should shuffle = false in unittest
* Add python files to depends.

Sequences should be sorted according to the number of subsequences they have.

Sequences should be sorted according to the number of subsequences they have.

* Add some comments for CTC layer and fix CTC evalutors, also add interface test

* modify RecurrentGradientMachine to support hasSubSeq sequence inlinks with the same number of sentence but different number of tokens for each sentence

Change-Id: Ic71f00a4bb346b4fa93e650dfb4b1a0d8d2338b0

Change cos_sim to use CosSimLayer layer when size=1 and rename convex_comb_layer to linear_comb_layer

Also add an unittest for layers.py, currently for a few things. More need to be added later.

* fix CUDNN_VERSION for backward of CudnnBatchNormLayer

* fix cudnn version number for BatchNorm

* the last layer in the stack already has all the suffixes