* 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

Init Python API

Fix OpDesc bug

remove unused PADDLE_ONLY_CPU comments

fix InferShapeContextBase to InferShapeContext

Add max pool op (with index)

Removed unreached code

ELU activation

fix compiler error: ‘unique_ptr’ is not a member of ‘std’

Implementing the fill constant op for the executor

add interpolation op

Use PROTO_LITE when refactoring Paddle

Add unit test of attributes in backward

* Adding implementation for copying a vector to tensor
* Changing Tensor test to access gpu memory indirectly

It will significantly reduce binary size. It is useful for mobile
deployment.

Fix compile error in develop branch

Fix bug of foward default attribute not passed to backward

Merge infershapecontext and ExecutionContext

Fix flushing vars

* Implementing the Adamax optimizer step operator
* Adding unit tests for adamax_op

* Changing learning rate and time step to inputs from attributes

* Changing learning rate and time step to input(tensors)

* Making the Adamax operator conform to naming convention

* Removing Tensor<float> from comments

* Rectifying the Adamax implementation

* Changing Unit Test values and adding comments

* Changing Unit Test to test multiple steps