* init nmt

* encoder ready

* only generation implementation

waiting for dynamic rnn ready to train

* init python

* remove decoder temporary

* clean

* clean

* Add DataFeeder

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

* Fix CI

* Add an unittest for while/rnn op forward

* Add unittest for raw while op backward

* Fix CI

* Complete Dynamic RNN

* add crf_decoding layer

* fix some typo

* init trunk_evaluator

* add trunk_evaluator layer

* update chunk_eval_op and test, change int32 to int64

* fix a numeric problem

* change layers.trunk_evaluator to layers.trunk_eval

* fix typo

* add precision_val

* "add init seed"

* "fix compile error"

* "add program level seed setting"

* "fixed based on comments"

* add crf_decoding layer

* fix some typo

* fix test_crf_decoding_op

* Add DataFeeder

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

* Fix CI

* Add an unittest for while/rnn op forward

* Add unittest for raw while op backward

* Fix CI

* 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

* Add hinge loss op

* Update hinge-loss equation for proper latex

* Expose sigmoid_cross_entropy_with_logits

Also, change the `labels` to `label` for api consistency

* Very simple GAN based on pure FC layers

Also, change the `labels` to `label` for api consistency

* Unify fluid submodules to fluid module

Change books just use `import fluid`, not submodules

* Remove g_main_program/g_startup_program

Use default_main_program/default_startup_program instead

* Typo

* Add API for switch default program

* Two functions: switch_main_program/switch_startup_program
* A guard: program_guard. Users can use the `with` statement change
  default programs
* Change unittests in `test_layers`

* Fix CI

* Fix CI

* Fix CI

* Make param_attr as a strong typed class

Fix #5819

* Adding log loss operator

* Removing comments

* batch norm support matrix input

* update gpu code

* format code

* Add Python wrap of conv2d_transpose and its unittest

* Follow comments

* Fix format

1. Remove checking for num_neg_samples.
2. Fix dims of Output(Cost) and Input(Bias).
3. Renamed num_sampled_classes to num_neg_samples.
4. Add TODO for add more distribution sampler.
5. Init grad_data of bias by zero.
6. Refine comments.
7. Register a kernel for type double.

* Unify fluid submodules to fluid module

Change books just use `import fluid`, not submodules

* Remove g_main_program/g_startup_program

Use default_main_program/default_startup_program instead

* Typo

* Fix CI

* Restore the param infos in Program.clone()

The Program.clone only clone the variables and ops
in the program into a new program. However, the
information of Parameter is not clone.

So we need restore the information of Parameters.

Fix #5871

* Follow comments

* Fix CI

* Fix CI

* Fix CI

* py_test support argument, test_image_classification_train support argument

* use REMOVE_ITEM to rm item from list in cmake

Change books just use `import fluid`, not submodules

* conv cudnn 3d

* update test case

* update

* update

* follow comments and remove groups from helper

* update

* refine

* update

* follow comments2

* update

* fix compile

* "add asnumpy interface"

* Just for unittest

* Change unittests for numpy I/O

* Fix CI

* Stash

* Stash

* Polish Evaluator

* Merge code

* Revert

* "add floor, ceil, round op"

* "reuse zero gradient"

* "fix divide zero"

* "fix numpy floor error"