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- .. _
-
- Bridge the machine learning and reasoning parts
- ===============================================
-
- We next need to bridge the machine learning and reasoning parts. In ABL-Package, the ``BaseBridge`` class gives necessary abstract interface definitions to bridge the two parts and ``SimpleBridge`` provides a basic implementation.
- We build a bridge with previously defined ``model``, ``reasoner``, and ``metric_list`` as follows:
-
- .. code:: python
-
- bridge = SimpleBridge(model, reasoner, metric_list)
-
- ``BaseBridge.train`` and ``BaseBridge.test`` trigger the training and testing processes, respectively.
-
- The two methods take the previous prepared ``train_data`` and ``test_data`` as input.
-
- .. code:: python
-
- bridge.train(train_data)
- bridge.test(test_data)
-
- Aside from data, ``BaseBridge.train`` can also take some other training configs shown as follows:
-
- .. code:: python
-
- bridge.train(
- # training data
- train_data,
- # number of Abductive Learning loops
- loops=5,
- # data will be divided into segments and each segment will be used to train the model iteratively
- segment_size=10000,
- # evaluate the model every eval_interval loops
- eval_interval=1,
- # save the model every save_interval loops
- save_interval=1,
- # directory to save the model
- save_dir='./save_dir',
- )
-
- In the MNIST Add example, the code to train and test looks like
-
- .. code:: python
-
- bridge.train(train_data, loops=5, segment_size=10000, save_interval=1, save_dir=weights_dir)
- bridge.test(test_data)
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