* add sync_with_cpp to Python Program and Block

* sync vars and ops in block from cpp

* optimize code and add some comment

* add more check for sync

* add target to Backward, generate var in block when call backward

* modify backward_test

* fix executor_test

* set var desc default type to LOD_TENSOR

* update backward_test

* insert loss in the top level of backward

* create grad vars for all blocks in current program

* optimize code

* update test_program.py

* only create var for newly create blocks when backward

* Add debug string for Python ProtoBuf

and Rename `Sync` to `Flush`

* Add check of ProtoBuf initialization

* Update VarDesc from design doc

* Fix GCC compile

* Fix unittest

* Adding Hard Sigmoid Activation

* Adding a comment for slope to be only positive

* Fixing grammatical mistake in comment

* add adam op

moment1_out = beta1 * moment1 + (1 − beta1) * grad
moment2_out = beta2 * moment2 + (1 − beta2) * grad * grad
moment1_hat =  moment1_out / (1 - beta1^t)
moment2_hat =  moment2_out / (1 - beta2^t)
param_out = param - learning_rate * moment1_hat / (sqrt(moment2_hat) +
epsilon)

* fix moment 2

* Adding the Adam optimization operator

* Adding more tests for Adam op

* Implementing the DecayedAdagrad optimizer step operator

* implementing DecayedAdagrad operator

* remove file

* small fix

* add cudnn_conv_op

* WIP

* update

* update

* fix grad check

* use platform::memory

* add support group for cudnn

* update

* follow comments

* fix onlycpu build

* update cuda define

* follow comments

* follow comments

* merge with updates

* fix compile error

* follow comments

* follow comments

* Adding thresholded_relu op
* Adding test for thresholded relu op

* implementing softplus

* small fix

* small fix

* small fix

* small fix

* Implemented the hardShrink activation

* Fixing the unit test

* Add numerically-stable logsigmoid activation

* Add softshrink operator

* Adjust relative tolerance for grad-check

* Address review comments

* conv_shift_op: initial implementation using Eigen

Limitations:
- both gradient outputs must be specified and are always computed
- explicit for loops => could be optimized in various ways
  (e.g., different memory layout)

* conv shift - gradient fixes

fix case when not all output gradients desired

* conv shift: minor cleanup

* conv shift - more minor cleanup

* conv shift: clean up & initial GPU implementation

* fix rebase issue