[DOC] resolve some comments

abl/learning/abl_model.py

@@ -1,14 +1,3 @@
-# coding: utf-8
-# ================================================================#
-#   Copyright (C) 2020 Freecss All rights reserved.
-#
-#   File Name     ：models.py
-#   Author        ：freecss
-#   Email         ：karlfreecss@gmail.com
-#   Created Date  ：2020/04/02
-#   Description   ：
-#
-# ================================================================#
 import pickle
 from typing import Any, Dict
 

abl/learning/basic_nn.py

@@ -1,15 +1,3 @@
-# coding: utf-8
-# ================================================================#
-#   Copyright (C) 2020 Freecss All rights reserved.
-#
-#   File Name     ：basic_model.py
-#   Author        ：freecss
-#   Email         ：karlfreecss@gmail.com
-#   Created Date  ：2020/11/21
-#   Description   ：
-#
-# ================================================================#
-
 import os
 import logging
 from typing import Any, Callable, List, Optional, T, Tuple

docs/Intro/Learning.rst

@@ -21,7 +21,7 @@ Learning Part
 
             self.base_model = base_model
 
-All scikit-learn models satisify this requiremnts, so we can directly use the model to create an instance of ``ABLModel``. For example, we can customize our machine learning model by
+All scikit-learn models satisify this requirements, so we can directly use the model to create an instance of ``ABLModel``. For example, we can customize our machine learning model by
 
 .. code:: python
 
@@ -36,6 +36,7 @@ For a PyTorch-based neural network, we first need to encapsulate it within a ``B
 .. code:: python
 
     # Load a PyTorch-based neural network
+    import torchvision
     cls = torchvision.models.resnet18(pretrained=True)
 
     # criterion and optimizer are used for training

docs/Intro/Quick-Start.rst

@@ -72,7 +72,7 @@ ABL-Package offers several `dataset classes <../API/abl.dataset.html>`_ for diff
 
 Read more about `preparing datasets <Datasets.html>`_.
 
-Building the Machine Learning Part
+Building the Learning Part
 ----------------------------------
 
 To build the machine learning part, we need to wrap our machine learning model into the ``ABLModel`` class. The machine learning model can either be a scikit-learn model or a PyTorch neural network. We use a simple LeNet5 in the MNIST Add example.
@@ -141,13 +141,6 @@ function specifying how to perform (deductive) reasoning.
          return sum(nums)
 
    kb = AddKB(pseudo_label_list=list(range(10)))
-   print(kb)
-
-Out:
-
-.. code-block:: none
-
-   AddKB is a KB with pseudo_label_list=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], max_err=1e-10, use_cache=True.
 
 Then, we create a reasoner by defining an instance of class
 ``ReasonerBase`` and passing the knowledge base as an parameter.
@@ -178,10 +171,10 @@ In the case of MNIST Add example, the metric definition looks like
 
 Read more about `building evaluation metrics <Evaluation.html>`_
 
-Bridging Machine Learning and Reasoning
+Bridging Learning and Reasoning
 ---------------------------------------
 
-Now, we use ``SimpleBridge`` to combine machine learning and reasoning together.
+Now, we use ``SimpleBridge`` to combine learning and reasoning in a unified model.
 
 .. code:: python
 
@@ -189,7 +182,7 @@ Now, we use ``SimpleBridge`` to combine machine learning and reasoning together.
 
    bridge = SimpleBridge(model, reasoner, metric_list)
 
-Finally, we proceed with testing and training.
+Finally, we proceed with training and testing.
 
 .. code:: python