add Max strategy for sequence_pool op

Add huber loss operator.

Auc op

fix ft job converge

* Adding L1 norm op for L1 regularization

* Addressing code review feedback

* Address code review feedback

* Change variable names to match google style guide

* Op developer can add `InferShape` to any operator

* add sparse support for sum op

* typo fix

* fix gpu build error

* fix unittest error

* typo fix

* infer var type and shape in op_test

* follow comments

* fix build error

* bypass some unittests depend on NetOp

* Python API for save/load variables

* Polish names

* Add regularizer code
* Fix code

* 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

* compelete restoring program_bind from program_desc

* Fix bugs

* fix compile errors

* fix errors and add unit tests

* rename some vars

* Follow comments

Fix pserver checkpoint

Add sequence_conv_op  and sequence_projection functor

Add local response normalize

refine paddle_merge_model

* Cross Entropy Wrong

* Fix XE

* Polish gradient check for xe

* Fix compile

* Init

* Stash

* Polish SaveLoadOp

* Fix CI

* Polish code

* Save GPU Tensor

* Stash

* Fix CI

* Adding the L2 loss operator for L2 regularization
* Renaming l2_loss op to squared_l2_norm_op
* Addressing code review feedback

enable mkldnn_batch_norm

fix sparse update size

* Removing survey out of the design doc and fixing typos
* Fix Typos

"correct the description"

Fix pserver SGD with momemtum checkpoint recover.