SAINT-pytorch

A Simple pyTorch implementation of "Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing" based on https://arxiv.org/abs/2002.07033.

SAINT: Separated Self-AttentIve Neural Knowledge Tracing. SAINT has an encoder-decoder structure where exercise and response embedding sequence separately enter the encoder and the decoder respectively, which allows to stack attention layers multiple times.

SAINT model architecture

Usage

import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import copy

from saint import saint, random_data

seq_len = 100
total_ex = 1200
total_cat = 234
total_in = 2

in_ex, in_cat, in_de = random_data(64, 
                                seq_len , 
                                total_ex, 
                                total_cat, 
                                total_in)


model = saint(dim_model=128,
            num_en=6,
            num_de=6,
            heads_en=8,
            heads_de=8,
            total_ex=total_ex,
            total_cat=total_cat,
            total_in=total_in )

outs = model(in_ex, in_cat, in_de)

print(outs.shape)
# torch.Size([64, 100, 1])

Parameters

• dim_model: int.
  Dimension of model ( embeddings, attention, linear layers).
• num_en: int.
  Number of encoder layers.
• num_de: int.
  Number of decoder layers.
• heads_en: int.
  Number of heads in multi-head attention block in each layer of encoder.
• heads_de: int.
  Number of heads in multi-head attention block in each layer of decoder.
• total_ex: int.
  Total number of unique excercise.
• total_cat: int.
  Total number of unique concept categories.
• total_in: int.
  Total number of unique interactions.

todo

• change positional embedding to sine.

Citations

@article{choi2020towards,
  title={Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing},
  author={Choi, Youngduck and Lee, Youngnam and Cho, Junghyun and Baek, Jineon and Kim, Byungsoo and Cha, Yeongmin and Shin, Dongmin and Bae, Chan and Heo, Jaewe},
  journal={arXiv preprint arXiv:2002.07033},
  year={2020}
}

@misc{vaswani2017attention,
    title   = {Attention Is All You Need},
    author  = {Ashish Vaswani and Noam Shazeer and Niki Parmar and Jakob Uszkoreit and Llion Jones and Aidan N. Gomez and Lukasz Kaiser and Illia Polosukhin},
    year    = {2017},
    eprint  = {1706.03762},
    archivePrefix = {arXiv},
    primaryClass = {cs.CL}
}