* Add LoDRankTable

LoD Rank Table stores the `level` of `lod` which is ordered by sequence
length in descending order. It is useful when implement dynamic RNN and
is shared by dynamic RNN memory, dynamic RNN slice input and dynamic
RNN slice output operators.

* Add InferVarType

* init

* Fix bug

* rename test_filw

* refine test

* Fix bug in lookup_table_op & layers

* Missing Act in layers

* Should += in CPU

* Remove check in python

* Fix bug in sequence_conv_pool()

* Fix a bug in test_recommender_system.py

* Just skip test_evaluator

* Adding the Xavier Initializer
* Addressing code review feedback

* "add net drawer for visualizing the graph"

* "fix "

* "add dep"

* Init commit

* Make executor use ProgramDescBind

* Change Attribute from BlockDesc to BlockDescBind

* Since we will get the program desc in RNN, just BlockDesc is not
  enough.

* Add DeviceContext to Executor API

* Rewrite RNN

* Pass Python

* AddBiasOp does not care num_flatten_dims

* Stash

* Fix MacOS Compile

* Pass RNN forward

* add python test

* refactor test

* Make compile pass

* add gradopmaker

* First draft done

* Polish code

* add grad op maker and grad infershape

* Polish code

* Fix backward.cc bug

* Fix infershape

* Rename function

* add backward test

* simplify recurrent test

* Update

* Pass unittest

* Add comments & refine test

* Add comments

* refactor test

* Complete Unittest

* fix StepScopes enforce

* Remove unused unittest

* no type error

* Update

* Make RNN Pass unittest

* "add sequence conv layer"

* "add book recommender_system testing"

* "add training loop"

* "add sequence layer"

* "add recommender system training data"

* "fix conv2d layer bug"

* add sequence_conv_pool

* "fix input is Null"

* add networks

* "fix based comment"

* "add sum op layer"

* "merge layers"

* Update layers.py

* "fix input is NULL bug"

* "debug embedding table"

* "modify layers.py"

* "fix pool interface"

* "add export type to layers"

* "fix based on comment"

* "need lod info support in all operator"

* "remove accuracy layer"

* "tuning learning rate"

* "add sparse test"

* "add gpu test"

* Update test_recommender_system.py

* optimizer use init_program

* create persistable variable

* add create_persistable_var to block

* optimizer use create_persistable_var

* fix prefix

* move create_global_persistable_var from Block to LayerHelper

* Polish Optimizer initialization code.

* Using the LayerHelper to create initialize operator and variables

* add_accumulator should use an independent data type

* default use param data type for accumulator

* Fix bug

* update

* "replace enum with string"

* "fix layers"

* AddBiasOp does not care num_flatten_dims

* Add comments

* "modify layers.py"

* "fix pool interface"

* "add export type to layers"

* "fix based on comment"

* refine evaluator op types

* update

* follow comments

* update

* fix v2 mnist case

* fix v2 mnist case

* update

* update

* AddBiasOp does not care num_flatten_dims

* Add comments

* "modify layers.py"

* "fix pool interface"

* "add export type to layers"

* "fix based on comment"

* refine evaluator op types

* update

* follow comments

* update

* fix v2 mnist case

* fix v2 mnist case

* update

* update

* Complete accuray layer

* Fix error

* Fix error

* Add 'accuracy' to __all__

* update

* Fix Type error

* Fix error

* Refine unit tests

* Fix an unit test error

* reimplement pow operator

* add pow_grad operator

* fix code style

* fix build error

* fix op_test bug

* revert pow operator

* add FIXME comment

* improve unique_name, uniq id is related to prefix

* fix join

* "add sequence conv layer"

* "add sequence layer"

* add networks

* "fix based comment"

* Update layers.py

* add resnet

* optimize code

* add init_gflags interface

* refine code

* follow comments

* Fix Type error

* Fix error

* add batch_norm_layer

* add img_conv_group layer and test

* add check to Tensor.type()

* forward can run

* with backward

* change label data time from int32 to int64

* refine code

* follow comment

Use fixed activation in the lstm kernel, since there is some bug in the activation function pointer. It will be fixed later.