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.

Fix conv2d bias

Add dilations for conv2d and optimize conv2d code

Enable manylinux builds

* fix gitignore

* refine cmake file

* add decayed adagrad python code

* fix typo and order

* small fix

The size of the bias parameter should be the number of filters.

Update the annotations of layers.py.

 Simpily demo, add paddle.default_main_program() and paddle.default_startup_program()

Feature/evaluator

Fix/reduce op

- 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.

* Adding greater than and less than equal ops to compare op
* Changing the name of the less_than_equal and greater_than_equal op
* Also changing the name of the functors

Remove some code to reduce the size of the mobile inference library.

fix pooling functor parameter order

Add dilation for exconv layer