* init grad op checker

* can run

* add GradeChecker class

* use get_numeric_gradient

* refine code

* add softmax and cross entropy auto grad test

* use close to judge op_grad and numeric_grad

* add cpu and gpu compare

* add comments

* add support_gpu

* fix allclose

* fix name error and symplify code

* optimize gradient checker

* add test_cross_entropy_op

* update gradient_checker.py

* optimize code

* use random.uniform instead of random.random

* fix type bug

* optimize check_grad

* put SupportGPU into OperatorBase

* typo

* add support_gpu

* fix allclose

* fix name error and symplify code

* finish forward debug

It can be run both CPU/GPU. configure attributes are:

* min: the min value of uniform random
* max: the max value of uniform random
* dims: the dimension of output tensor
* seed: the random seed of uniform random. 0 means generate a seed each
        time.

* init cross entropy graident

* add crossentropy grad op

* remove details

* fix static compile

Make ci pass

Currently use `Operator("fc", X="x", W='w1', B='b1')` as operator
creation method.

Fix #3198

Rename operator.py to op.py because it is conflict with protobuf

* init softmax grad op

* add compute code

* export Backward to python

* update test ,export op.type to python

* update python test, fix compute bug

* update unit test

* use eigen

* optimize eigen code

* add gpu test

* register softmax_grad GPU kernel and fix test bug

* typo

* follow comments

* export Backward to python

Add get_numeric_gradient API and its unittest.

* Make interface of Operator to `const Scope&`

* add rnn op interfaces

* add Run

* rename state -> memory

* change state -> memory

* make compilable

* add .cc

* init test

* add op fake implementation

* add CreateStepNet and CreateScopes implementation.

* add TODO list

* init memory attributes.

* add LinkMemories

* add PlainNet fake implementation

* Use std::shared_ptr<Scope> in the OpRunContext.

* add test

* disable mutable_data

* finist segmentInput function

* enable mutable_data with a trick

* RNNOp test.

* enable LinkMemories with mutable_data

* update SegmentInput function with comments

* finish ConcatOutput function

* reformat inputs and attributes

boot_memories

* Refine unit test.

* Refine unit test.

* modify inlinks.

* add OpDesc to Net

* fix bug and update unit test.

* move step scopes from inputs to outputs

* fix merge conflict, update SegmentInput function

* add RecurrentOpProtoAndCheckerMaker.

* clean the codes

* Abstract GetStepScopes and GetMaxSeqLen function

* refine LinkMemories

* Refine code and add some comments.

* add backward core

* update for develop branch.

* add forward core

* add forward algorithm

* Add RecurrentGradientAlgorithm implenmention.

* use CopyFrom and Slice function in RecurrentOp

* add unit test for LinkMemories.

* fix unit test.

* use the latest tensor.h, solve conflict

* add maker

* move SegmentInput and ConcatOutput to details nameplace

* unit test for RecurrentGradientAlgorithm.

* apply OperatorBase

* apply net operator.

* move memorys to attributes

* add RecurrentGradientOp

* open test unit test in recurrent_network_op_test.

* revert some files.

* add RecurrentArgument and Link struct to simplify member variable.

* rename.

* move recurrent_op from framework to operators

* add RecurrentGradientOp Init

* fix name

* fix Link.interal/external name

* use namespace operators instead of framework

* clean the code

* use the latest add_op and mul_op, don't test backward now

* Remove ScopePtr and OperatorPtr

* add get_net to pybind

* add test_recurrent_op.py

* add random into gen_tensor

* update to develop branch and refine some code.

* add some comments.

* Refine unittest also