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Improving the Accuracy of Progress Indication for Constructing Deep Learning Models.

Qifei Dong1, Xiaoyi Zhang1, Gang Luo1

  • 1Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA 98195, USA.

IEEE Access : Practical Innovations, Open Solutions
|July 25, 2022
PubMed
Summary
This summary is machine-generated.

Deep learning model training can be lengthy. A new method improves progress indicators by adding validation points, reducing prediction errors for remaining training time by 57.5% on average.

Keywords:
Progress indicatorTensorFlowdeep learningmodel construction

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Area of Science:

  • Computer Science
  • Artificial Intelligence

Background:

  • Deep learning models offer superior performance for many machine learning tasks.
  • Training deep learning models on large datasets can be time-consuming, often taking days or months.
  • Accurate progress indicators are crucial for estimating remaining training time and completion percentage.

Purpose of the Study:

  • To develop an improved method for progress indication in deep learning model training.
  • To address the delay and inaccuracy issues in existing progress indicators caused by sparse validation points.

Main Methods:

  • Proposed a novel method involving the strategic insertion of additional validation points during model training.
  • Implemented the new method using the TensorFlow framework.
  • Compared the performance of the new method against a previously developed technique.

Main Results:

  • The new method significantly reduced the prediction error for remaining model construction time by an average of 57.5%.
  • Achieved relatively accurate progress estimates more rapidly compared to the prior method.
  • Demonstrated low computational overhead associated with the enhanced progress indication.

Conclusions:

  • The proposed method effectively overcomes the limitations of sparse validation points in deep learning progress indicators.
  • This advancement allows for faster and more accurate estimation of deep learning model training completion.
  • The findings are valuable for optimizing the training process of large-scale deep learning models.