MXNet的训练基础脚本：base_module.py

写在前面：在MXNet中有一个很重要的脚本：base_module.py，这个脚本中的BaseModule类定义了和模型实现相关的框架。另外还有一个脚本module.py会在另外一篇博客中讲，这个类继承BaseModule类，并进行了具体的实现，比如forward方法，update_metric方法等。

在创建模型的时候用的是mx.mod.Module这个类，比如：model = mx.mod.Module(symbol = sym)，具体这个Module类的内容是什么？其实mx.mod.Module准确地写应该是mxnet.module.Module()，这个类的路径是~/mxnet/python/mxnet/module/module.py，但是在实际调用的时候是调用python库中的module.py，路径是：/usr/local/lib/python2.7/dist-packages/mxnet-0.10.1-py2.7.egg/mxnet/module/module.py，这个mxnet-0.10.1-py2.7.egg就是在编译python，即运行setup.py时候生成的。module.py这个脚本中定义了Module类，而这个类继承另一个类BaseModule，BaseModule这个类是在base_module.py脚本中定义的，该脚本和module.py脚本在同一目录下。因为fit()方法继承自BaseModule类并且在BaseModule类中实现，Module类中并没有做修改，所以在BaseModule类中主要看fit()方法。注意到在BaseModule类中其他方法最后都有一句raise NotImplementedError()，这句话是引发未实现的异常的，也就是说如果你继承了BaseModule类却没有实现一些方法（带raise这句话），那么在调用这个方法的时候就会引发异常。

base_module.py代码的git地址：
https://github.com/dmlc/mxnet/blob/master/python/mxnet/module/base_module.py

base_module.py中最重要的就是BaseModule类，接下来截取这个类中重要的几个函数来介绍，大部分函数只是在这里定义，具体实现一般由别的类通过继承该类进行实现：

class BaseModule(object):
"""The base class of a module.

    A module represents a computation component. One can think of module as a computation machine.
    A module can execute forward and backward passes and update parameters in a model.
    We aim to make the APIs easy to use, especially in the case when we need to use the imperative
    API to work with multiple modules (e.g. stochastic depth network).

A module has several states:

    - Initial state: Memory is not allocated yet, so the module is not ready for computation yet.
    - Binded: Shapes for inputs, outputs, and parameters are all known, memory has been allocated,
      and the module is ready for computation.
    - Parameters are initialized: For modules with parameters, doing computation before
      initializing the parameters might result in undefined outputs.
    - Optimizer is installed: An optimizer can be installed to a module. After this, the parameters
      of the module can be updated according to the optimizer after gradients are computed
      (forward-backward).

In order for a module to interact with others, it must be able to report the
    following information in its initial state (before binding):

    - `data_names`: list of type string indicating the names of the required input data.
    - `output_names`: list of type string indicating the names of the required outputs.

    After binding, a module should be able to report the following richer information:

    - state information
        - `binded`: `bool`, indicates whether the memory buffers needed for computation
          have been allocated.
        - `for_training`: whether the module is bound for training.
        - `params_initialized`: `bool`, indicates whether the parameters of this module
          have been initialized.
        - `optimizer_initialized`: `bool`, indicates whether an optimizer is defined
          and initialized.
        - `inputs_need_grad`: `bool`, indicates whether gradients with respect to the
          input data a