* Add DataFeeder

A v2 API like data feeder for book demos.
We can feed data directly from reader.

* Fix CI

* Remove batch_size_dim for feeder

Also add __all__ to data_feeder.py

* Follow comment

Change books just use `import fluid`, not submodules

* Stash

* Stash

* Polish Evaluator

* Merge code

* Revert

* Change all `data_type` in Python to `dtype`

* Change `date_type` in C++ to `dtype`

* Refine

- Removed all main_program and startup_program in the demo.
- Using paddle.default_main_program() hides the implementation detail (e.g., using g_main_program) from the user, we can change the implementation in the future much easier.

* init commit

* change some dir name

* 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

* 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

* 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

* support sparse output for lookup table grad op

* refine codes

* fix gpu build error

* fix lookup table grad gpu kernel

* fix ci

* fix ci

* fix ci

* fix bug in lookup_table_grad op

* fix bug in test_word2vec

* register double kernel for some operators

* set is_sparse=True in test_word2vec

* fix lookup table grad op CUDA kernel bug

* disable test_modified_huber_loss_op temporarily

* disable test_lstm_unit_op temporarily

* Init file

* Update

* Update

* Complete conv net of MNIST