让R用户轻松用深度学习处理表格和图像数据
mlr3torch: A Deep Learning Framework in R based on mlr3 and torch
- 基于mlr3和torch构建，支持神经网络全流程定义与训练
- 可统一建模工作流，包含预处理、数据增强与网络结构
- 适合R用户做深度学习实验，尤其擅长多模态与调参任务
深度学习已成为现代机器学习实践的核心。我们介绍R语言包mlr3torch，这是一个基于torch构建的可扩展深度学习框架，集成于mlr3生态。该框架简化了针对表格数据和通用张量（如图像）的分类与回归任务中神经网络的定义、训练与评估。它提供预定义模型架构，支持将torch模型快速转化为mlr3学习器，并允许用户以图形式定义神经网络。此图表示基于mlr3pipelines的图语言，可统一建模流程，涵盖预处理、数据增强与网络结构。通过mlr3生态集成，支持便捷的重采样、基准测试与预处理。本文阐述其设计与功能，展示如何定制与扩展至新问题。并通过三个案例演示：超参数调优、微调与多模态数据架构构建。最后给出部分运行时性能基准。
原文摘要 · Abstract (English)
Deep learning (DL) has become a cornerstone of modern machine learning (ML) praxis. We introduce the R package mlr3torch, which is an extensible DL framework for the mlr3 ecosystem. It is built upon the torch package, and simplifies the definition, training, and evaluation of neural networks for both tabular data and generic tensors (e.g., images) for classification and regression. The package implements predefined architectures, and torch models can easily be converted to mlr3 learners. It also allows users to define neural networks as graphs. This representation is based on the graph language defined in mlr3pipelines and allows users to define the entire modeling workflow, including preprocessing, data augmentation, and network architecture, in a single graph. Through its integration into the mlr3 ecosystem, the package allows for convenient resampling, benchmarking, preprocessing, and more. We explain the package's design and features and show how to customize and extend it to new problems. Furthermore, we demonstrate the package's capabilities using three use cases, namely hyperparameter tuning, fine-tuning, and defining architectures for multimodal data. Finally, we present some runtime benchmarks.
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