Two-Stage Deep Learning Training with Early Stopping
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Deep learning model training faces challenges in determining the optimal stopping point to avoid overfitting, with existing methods like early stopping requiring trial and error for patience parameters and checkpoint methods requiring extensive training time.
Innovation Solution
A two-stage training approach that uses early stopping for the first stage and resumes training with a small number of epochs in the second stage, allowing for efficient use of computing resources and prioritization of training jobs to optimize model evaluation and training time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If early stopping is used to prevent overfitting, then model overfitting is reduced, but training time is extended due to trial and error for patience parameters
Solution Approach 1:
The training process is divided into two distinct stages: first-stage training with early stopping to prevent overfitting, and second-stage training with continuation to improve accuracy. This segmentation allows the system to benefit from both early stopping (preventing overfitting) and extended training (improving accuracy) without the trial-and-error time cost, as the second stage automatically continues training after early stopping triggers.
2Reliability
If training continues after early stopping to obtain better models, then model accuracy may be improved, but computing resource efficiency deteriorates
Solution Approach 1:
The system dynamically adjusts the training process by implementing a two-stage approach where the second stage automatically continues training after early stopping. This dynamic adjustment allows the system to capture potential accuracy improvements without manual intervention or trial-and-error, thereby improving computing resource efficiency while maintaining model accuracy.
3Reliability
If checkpoint method is used to obtain best model, then model accuracy is improved, but training time is extended to maximum iterations
Solution Approach 1:
The first-stage training with early stopping serves as a preliminary action that identifies the optimal stopping point to prevent overfitting. This preliminary training phase allows the system to save computational resources by avoiding unnecessary training iterations, while the second stage then continues training to achieve the best model accuracy, eliminating the need to wait for maximum iterations.
4Reliability
If patience parameter is optimized through trial and error, then early stopping effectiveness is improved, but training time increases
Solution Approach 1:
The patent extracts the patience parameter optimization problem from the overall training process by implementing automatic continuation training in the second stage. This extraction eliminates the need for trial-and-error parameter tuning, as the system automatically determines the optimal stopping point and continues training to achieve the best model, thereby removing the time cost of parameter optimization.
Data Source
AI summary
A computer-implemented method, a computer program product, and a computer system for efficient use of computing resources in two stage training of a deep learning model. A computer executes a first first-stage training job to train a deep learning model. The computer finishes the first first-stage training job by using early stopping and then registers a first second-stage training job to train a deep learning model that has been trained in the first first-stage training job. The computer executes the first second-stage training job with a small number of epochs. The computer interrupts the first second-stage training job and executes a second first-stage training job, in response to receiving a registration of the second first-stage training job. The computer interrupts the first second-stage training job and executes a second second-stage training job that has a higher priority, in response to receiving a registration of the second second-stage training job.