Name
Abstract | ||
|---|---|---|
For many machine learning tasks, deep learning greatly outperforms all other existing learning algorithms. However, constructing a deep learning model on a big data set often takes days or months. During this long process, it is preferable to provide a progress indicator that keeps predicting the model construction time left and the percentage of model construction work done. Recently, we developed the first method to do this that permits early stopping. That method revises its predicted model construction cost using information gathered at the validation points, where the model's error rate is computed on the validation set. Due to the sparsity of validation points, the resulting progress indicators often have a long delay in gathering information from enough validation points and obtaining relatively accurate progress estimates. In this paper, we propose a new progress indication method to overcome this shortcoming by judiciously inserting extra validation points between the original validation points. We implemented this new method in TensorFlow. Our experiments show that compared with using our prior method, using this new method reduces the progress indicator's prediction error of the model construction time left by 57.5% on average. Also, with a low overhead, this new method enables us to obtain relatively accurate progress estimates faster. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/ACCESS.2022.3181493 | IEEE ACCESS |
Keywords | DocType | Volume |
Computational modeling, Predictive models, Costs, Deep learning, Error analysis, Data models, Delays, Progress indicator, deep learning, TensorFlow, model construction | Journal | 10 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
1 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qifei Dong | 1 | 0 | 0.68 |
| Xiaoyi Zhang | 2 | 0 | 0.34 |
| Gang Luo | 3 | 741 | 44.73 |