8-BIT OPTIMIZERS IN DEEP LEARNING MODEL TRAINING FOR IMAGE CLASSIFICATION
This research focuses on the implementation of 8-bit optimizers to support the process of training machine learning models, specifically deep learning models for image classification. This optimizer has been chosen because of its novelty, hence it has not been widely tested for various use cases....
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| 主要作者: | |
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| 格式: | Final Project |
| 語言: | Indonesia |
| 在線閱讀: | https://digilib.itb.ac.id/gdl/view/75909 |
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| 機構: | Institut Teknologi Bandung |
| 語言: | Indonesia |
| 總結: | This research focuses on the implementation of 8-bit optimizers to support the
process of training machine learning models, specifically deep learning models
for image classification. This optimizer has been chosen because of its novelty,
hence it has not been widely tested for various use cases. Tests have been done for
the use of 8-bit optimizers in natural language processing (NLP) with impeccable
performance compared to previous existing optimizers, henceforth this research
will test its performance on the case of image classification. This research is done
by comparing the performance of deep learning models utilizing the Adam
optimizer, 8-bit optimizers, and no optimizers. With GPU memory usage, model
accuracy, and model training time as this research’s metrics, 8-bit optimizers have
shown to have the best accuracy and save a significant amount of GPU memory,
but with a slightly longer training time. It is hoped that this research is able to
serve as a stepping stone for better understanding of optimizers, especially in the
deep learning field. |
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