ABLkit/examples/hed

• Handwritten Equation Decipherment
• • Run
  • Usage

Handwritten Equation Decipherment

This notebook shows an implementation of Handwritten Equation Decipherment. In this task, the handwritten equations are given, which consist of sequential pictures of characters. The equations are generated with unknown operation rules from images of symbols ('0', '1', '+' and '='), and each equation is associated with a label indicating whether the equation is correct (i.e., positive) or not (i.e., negative). Also, we are given a knowledge base which involves the structure of the equations and a recursive definition of bit-wise operations. The task is to learn from a training set of above mentioned equations and then to predict labels of unseen equations.

Run

pip install -r requirements.txt
python main.py

Usage

usage: main.py [-h] [--no-cuda] [--epochs EPOCHS] [--lr LR]
               [--weight-decay WEIGHT_DECAY] [--batch-size BATCH_SIZE]
               [--loops LOOPS] [--segment_size SEGMENT_SIZE]
               [--save_interval SAVE_INTERVAL] [--max-revision MAX_REVISION]
               [--require-more-revision REQUIRE_MORE_REVISION]
               [--ground] [--max-err MAX_ERR]

Handwritten Equation Decipherment 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)
  --weight-decay WEIGHT_DECAY
                        weight decay (default : 0.0001)
  --batch-size BATCH_SIZE
                        base model batch size (default : 32)
  --save_interval SAVE_INTERVAL
                        save interval (default : 1)
  --max-revision MAX_REVISION
                        maximum revision in reasoner (default : 10)