想研究深度学习实现故障？这个新基准Deep4ge包含1.4万次训练运行、27种故障注入方式，帮你做检测和诊断。
Deep4ge是一个由14,227次训练运行组成的基准数据集，基于59个TensorFlow/Keras程序生成，通过27种源代码转换引入7类已知故障。包含9,845个故障运行和4,382个正确基线运行，记录每轮训练的4个评估指标和26个特征（权重、梯度、激活值、准确率和损失趋势、学习率、硬件使用）。支持二进制故障检测、多类故障诊断和早期故障预测。数据集和故障注入框架已发布。
Deep4ge: DNN Training Trajectories for Fault Detection and Diagnosis
Deep learning systems often fail due to subtle implementation faults that alter training behavior. Recent work has studied how to detect and diagnose such failures from changes observed across training epochs. However, the software engineering community still lacks a public dataset of per-epoch training runs with documented fault history, feature extraction details, and clear reuse support for fault detection and diagnosis tasks. We present Deep4ge, a controlled benchmark of 14,227 training runs generated from 59 adapted TensorFlow/Keras deep neural network (DNN) programs collected from Stack Overflow. We generated faulty variants using 27 source-code transformations that introduce known faults across seven categories. The dataset contains 9,845 faulty runs and 4,382 correct baseline runs. For each run, we record 4 evaluation metrics and 26 features that measure training behavior at every epoch. These features capture weights, gradients, activations, accuracy and loss trends, learning rate, and hardware use. Deep4ge supports binary fault detection, multi-class fault diagnosis, and early fault prediction from partial training runs. We release the dataset and fault-injection framework at https://doi.org/10.5281/zenodo.20337241.