The top level user API in Python should be as same as API in `paddle.v2` after refactoring Paddle from a layer based framework to an operator based framework. There are many new classes in CPP in [compile time] for describing neural networks, such as `Variable`, `Operator`, `Block`. The issue about current design is how to give a proper way to wrap the C++ API to `paddle.v2` API and writing layers in Python.
The top level user API in Python should be as same as API in `paddle.v2` after refactoring Paddle from a layer based framework to an operator based framework. There are many new classes in CPP in [compile time] for describing neural networks, such as `Variable`, `Operator`, `Block`. The issue about current design is how to give a proper way to wrap the C++ API to `paddle.v2` API and writing layers in Python.
This implementation of Python API includes two steps.
This implementation of Python API includes two steps.
1. Implement the Python API using current C++ runtime concepts.
1. Implement the Python API using current C++ runtime concepts.
2. Replace the implementation by using compile-time concepts when they are completed.
2. Replace the implementation by using compile-time concepts when they are completed.
...
The implementation of the first step is a temporary implementation. We should design our Python API concepts based on `compile-time` concepts. We just use `runtime` classes to implement it for now.
## Python Class and compile-time protobuf
As we design our Python API concepts based on `compile-time`, we try to map our Python classes to every compile-time result, i.e., the protobuf messages. They are:
