Towards A Visual Programming Tool to Create Deep Learning Models
Abstract
Deep Learning (DL) developers come from different backgrounds, e.g., medicine, genomics, finance, and computer science. To create a DL model, they must learn and use high-level programming languages (e.g., Python), thus needing to handle related setups and solve programming errors. This paper presents DeepBlocks, a visual programming tool that allows DL developers to design, train, and evaluate models without relying on specific programming languages. DeepBlocks works by building on the typical model structure: a sequence of learnable functions whose arrangement defines the specific characteristics of the model. We derived DeepBlocks’ design goals from a 5-participants formative interview, and we validated the first implementation of the tool through a typical use case. Results are promising and show that developers could visually design complex DL architectures.
BibTeX
@inproceedings{10.1145/3596454.3597181,
author = {Calò, Tommaso and De Russis, Luigi},
title = {Towards A Visual Programming Tool to Create Deep Learning Models},
year = {2023},
isbn = {9798400702068},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3596454.3597181},
doi = {10.1145/3596454.3597181},
abstract = {},
booktitle = {Companion Proceedings of the 2023 ACM SIGCHI Symposium on Engineering Interactive Computing Systems},
pages = {38–44},
numpages = {7},
keywords = {visual programming, user interface, deep learning, debugging},
location = {Swansea, United Kingdom},
series = {EICS '23 Companion}
}