* Update the mnist test cases with the new API syntax.

* Turn on the tests for MNIST

* delete the test files got merged accidently.

* Enable the mnist tests. ready for test driven development.

* Comment out the infer first
This is to confirm that the Trainer.train is working

* Add CMake file to include the tests

* Make the train program only return avg_cost for now

* Update the tests to use the latest syntax

* add Inference.infer

* optimize code

* update no_test_word2vec_new_api.py

* update trainer

* split check_and_get_place

* use inference_program to save inference model in Trainer

* update demo

* update save_inference_model

* clean code

* a draft of trainer.test()

* polish trainer.test()

* polish trainer.test()

* update code format

* update

* polish code

* polish code

* polish code

* Make trainer.test follow the rule of returning [loss, metric, metric, ..]

* Put all new tests under high-level-api

* be consistent with the naming

* Save the base of the new test.

* Create the notest_recognize_digits_conv and notest_recognize_digits_mlp

* precommit check

* Change the function name from _network to _program.
Update several variable names to make them more consistant.
Update the Inferencer construction call.

* Fix the incorrect format.

* Add label semantic examples with new api

* Address review comments

* Address review comment

* add test float16 inference accuracy example

* complete the test

* clean code

* add argument parse and refine tests

* add shell script

* add float16 benchmark code

* refine code

* prepare for contrib/float16

* put things in contrib float16 folder

* update benchmark result

* further update benchmark report

* add float16 inference report

* update report

* Add understand apiu with stacked lstm for new API

* Complete exam

- The trainer and inferencer will load params from disk if param_path
  argument is not None in their constructor.

- Remove params.py, we will expose core.Scope to the user if needed
  (e.g., for GAN). Currently we will not expose it, unless we clearly
  know doing so can support GAN.

- Add `save_params` to Trainer (a TODO item).

- rename "network" to "program"

* add float16 transpiler

* fix feed fetch target names mismatch

* fix cast op input change issue

* fix program desc flush error

* fix inconsistent var names in block desc bug

* code clean up

* add float16 infernce C++ example and fix prune bug

* remove share lod; it has already been performed in infershape

* slightly release test bound of test_image_classification

Fix a bug in save_inference_model and prune when the program is initailized by load_inference_model (#10011)

* Fix bug in save_inference_model and prune when the program is initialized by load_inference_program.

* Save the transpiled program instead.

In this way we can build and test using WITH_FLUID_ONLY flag being set
to ON.

- move paddle.v2.dataset,reader to paddle.dataset,reader
- remove unused code (which depends on v2) in paddle.dataset,reader

* fix is_test issue

* add paddle enforce

* fix bug

* add new func

* small fix

* address comments

* change learning_rate_decay to learning_rate_scheduler
* optimize code
* change nn.cast to tensor.cast