* Simplize Gradient Check

* Stash

* Extract apply_backward_pass to backward.py

Rename apply_backward_pass to append_backward_ops

* Use graph API to check gradient

* Fix ci

* Fix CI

* Fix backward for double precision

* Stash

* Fix CI

* Fix ci

* Ignore GRU test

* Ignore xe op

* Fix CI

* Fix softmax with xe gradient

The correct equation should be IG = OG * (d_softmax_with_xe())

* Fix typo

* Fix merge error

* Disable LRN

* Cross Entropy Wrong

* Fix XE

* Polish gradient check for xe

* Fix compile

* init batch norm op

* prepare input output

* compute mean_out var_out save_mean save_var on CPU

* active is test

* use eigen to do computation

* complete batch norm forward

* set default momentum to 0.9

* add batch norm grad op in CPU

* add tensor_format and NHWC support, add python test

* add test training

* add batch norm gradient test

* improve comment, fix foward Python UnitTest

* add gradient test

* fix eigen warning

* follow name style

* fix a bug

* change float to T

* add simple forward test

* test with different place

* add backward test

* refine python test

* remove old python test code

* code clean

* follow code style

* update comment

Pass all forward op test

* Changing learning rate from attribute to input(float)
* Removing obsolete code

InferShape in Operator should be performed in OperatorBase::Run.

* cond_op, recurrent_op and mnist might be changed in following PR

Split Op CPU Kernel

2. Fix unitest

Fix check grad with multi outputs