* Use C++ 11 atomic_flag in MacOS as spin lock
* Add unittest for it.

* 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

* some bug fix for sparse matrix

* a minor bug fix

* also refine install docs, too

* follow comments to fix bugs

* refine sparse momentum api and unittest
* fix unittests bug

This bug occasionally causes dead lock in test_RecurrentGradientMachine
In general, conditional_variable::notify should be used together with mutex for changing condition.

* Add noavx to docker
* Not to use directory to split Docker image.

* reduce trainer count for unit test on MAC OSX

* 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.

Local training with "sparse_update = True" parameter triggers kSgdSparseCpuTraining mode, fix bugs under it.

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

* it makes unit test failed.

* using map to replace unordered_map on Mac

* remove unnecessary field set in ParameterConfig, Evaluators, etc

* 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.

* try to fix bug for ctc_error_evaluator

Just forget to add deploy_key to this repository.