| @@ -22,7 +22,7 @@ where ``X`` is the input of the machine learning model, | |||||
| train_data = get_mnist_add(train=True, get_pseudo_label=True) | train_data = get_mnist_add(train=True, get_pseudo_label=True) | ||||
| test_data = get_mnist_add(train=False, get_pseudo_label=True) | test_data = get_mnist_add(train=False, get_pseudo_label=True) | ||||
| In the above ``get_mnist_add``, the return values are tuples of ``(X, gt_pseudo_label, Y)``. | |||||
| In the ``get_mnist_add`` above, the return values are tuples of ``(X, gt_pseudo_label, Y)``. | |||||
| Machine Learning (Map input to pseudo labels) | Machine Learning (Map input to pseudo labels) | ||||
| --------------------------------------------- | --------------------------------------------- | ||||
| @@ -71,7 +71,7 @@ obtained by machine learning. | |||||
| kb = AddKB(pseudo_label_list=list(range(10))) | kb = AddKB(pseudo_label_list=list(range(10))) | ||||
| Then we define a reasoner, which defines | |||||
| Then, we define a reasoner, which defines | |||||
| how to minimize the inconsistency between the knowledge base and machine learning. | how to minimize the inconsistency between the knowledge base and machine learning. | ||||
| .. code:: python | .. code:: python | ||||
| @@ -81,7 +81,7 @@ how to minimize the inconsistency between the knowledge base and machine learnin | |||||
| Bridge Machine Learning and Reasoning | Bridge Machine Learning and Reasoning | ||||
| ------------------------------------- | ------------------------------------- | ||||
| First, we use `SimpleBridge` to combine machine learning and reasoning together, | |||||
| First, we use ``SimpleBridge`` to combine machine learning and reasoning together, | |||||
| setting the stage for subsequent integrated training, validation, and testing. | setting the stage for subsequent integrated training, validation, and testing. | ||||
| .. code:: python | .. code:: python | ||||