AbductiveLearning
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ABLkit
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ABLkit/examples/mnist_add
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README.md

MNIST Addition

This example shows a simple implementation of MNIST Addition task, where pairs of MNIST handwritten images and their sums are given, alongwith a domain knowledge base containing information on how to perform addition operations. The task is to recognize the digits of handwritten images and accurately determine their sum.

Run

pip install -r requirements.txt
python main.py

Usage

usage: main.py [-h] [--no-cuda] [--epochs EPOCHS] [--lr LR] 
               [--alpha ALPHA] [--batch-size BATCH_SIZE]
               [--loops LOOPS] [--segment_size SEGMENT_SIZE]
               [--save_interval SAVE_INTERVAL] [--max-revision MAX_REVISION]
               [--require-more-revision REQUIRE_MORE_REVISION]
               [--prolog | --ground]

MNIST Addition example

optional arguments:
  -h, --help            show this help message and exit
  --no-cuda             disables CUDA training
  --epochs EPOCHS       number of epochs in each learning loop iteration
                        (default : 1)
  --lr LR               base model learning rate (default : 0.001)
  --alpha ALPHA         alpha in RMSprop (default : 0.9)
  --batch-size BATCH_SIZE
                        base model batch size (default : 32)
  --loops LOOPS         number of loop iterations (default : 5)
  --segment_size SEGMENT_SIZE
                        segment size (default : 1/3)
  --save_interval SAVE_INTERVAL
                        save interval (default : 1)
  --max-revision MAX_REVISION
                        maximum revision in reasoner (default : -1)
  --require-more-revision REQUIRE_MORE_REVISION
                        require more revision in reasoner (default : 0)
  --prolog              use PrologKB (default: False)
  --ground              use GroundKB (default: False)
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An efficient Python toolkit for Abductive Learning (ABL), a novel paradigm that integrates machine learning and logical reasoning in a unified framework.

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