Exploring the Relationship Between Learning Rate, Batch Size, and Epochs in Deep Learning: An Experimental Study

Shafi, Sadaf; Assad, Assif

doi:10.1007/978-981-19-6525-8_16

Lecture Notes in Networks and Systems

2023

DOI: 10.1007/978-981-19-6525-8_16

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Exploring the Relationship Between Learning Rate, Batch Size, and Epochs in Deep Learning: An Experimental Study

Sadaf Shafi

Assif Assad

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2024

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Cited by 3 publications

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“…These four algorithms have made significant contributions in the field of Deep Learning.2.4. Epoch, Learning Rate and Batch SizeIn deep learning, there are three parameters that are important in measuring the training dataset: epoch, learning rate and batch size[57]. The epoch value in the context of Teachable Machine, one epoch means that the model has processed all the samples in the training dataset and updated the parameters based on the calculated loss.…”

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confidence: 99%

Teachable Machine: Optimization of Herbal Plant Image Classification Based on Epoch Value, Batch Size and Learning Rate

Saitakela

2024

J. Appl. Data Sci.

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confidence: 99%

Teachable Machine: Optimization of Herbal Plant Image Classification Based on Epoch Value, Batch Size and Learning Rate

Saitakela

2024

J. Appl. Data Sci.

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An modified intelligent real-time crack detection method for bridge based on improved target detection algorithm and transfer learning

Yang,

Li,

Yao

et al. 2024

Front. Mater.

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The combination of UAV camera and intelligent algorithm is a promising method for non-contact bridge crack detection. In this paper, an inspection tool based on UAV Image Acquisition Technology (UAVIAT) and Improved Intelligent Target Detection Technology (IITDT) called Improved Intelligent Real-Time Crack Detection Method for Bridges (IIRTCDMB) is proposed for efficient crack detection. The contributions of this paper are (1) The Squeeze-Excitement (SE) attention module is integrated into the target detection algorithm - You Only Look Once version 7 (YOLOv7) model to improve the learning ability of the feature channel. A Focal-efficient intersection over union (Focal-EIoU) loss function is also introduced to improve the regression accuracy of the model. As a result, a new crack image detection algorithm, YOLOv7-CD, is proposed. (2) A training process based on two-stage transfer learning (TSTL) is established, and hyper-parameter optimization of YOLOv7-CD is carried out. The feasibility and excellent performance of the proposed method are verified by applying it on the Cuntan Yangtze River Bridge. The results show that the average precision (AP) of the YOLOv7-CD model is improved by 3.19% compared with the original YOLOv7 model. After TSTL and hyperparameter optimization, the AP of the YOLOv7-CD model for bridge crack detection reaches 98.01%, which is higher than that of the popular target detection models. The IIRTCDMB proposed in this paper can acquire bridge surface images more safely and efficiently, and provide inspectors with more accurate structural crack information with lower computational and hardware requirements, which can provide technical support for the assessment of structural safety conditions and the formulation of maintenance programs.

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Hybrid Intelligent Model for Estimating the Cost of Huizhou Replica Traditional Vernacular Dwellings

Huang,

Quan

et al. 2024

Buildings

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Amidst the backdrop of rural revitalization and cultural renaissance, there is a surge in the construction demand for replica traditional vernacular dwellings. Traditional cost estimation methods struggle to meet the need for rapid and precise estimation due to the complexity inherent in their construction. To address this challenge, this study aims to enhance the accuracy and efficiency of cost estimation by innovatively developing an Adaptive Self-Explanatory Convolutional Neural Network (ASCNN) model, tailored to meet the specific cost estimation needs of replica traditional vernacular dwellings in the Huizhou region. The ASCNN model employs a Random Forest model to filter key features, inputs these into the CNN for cost estimation, and utilizes Particle Swarm Optimization (PSO) to optimize parameters, thereby improving predictive accuracy. The decision-making process of the model is thoroughly interpreted through SHAP value analysis, ensuring credibility and transparency. During the construction of the ASCNN model, this study collected and analyzed bidding control price data from 98 replica traditional vernacular dwellings. The empirical results demonstrate that the ASCNN model exhibits outstanding predictive performance on the test set, with a Root Mean Square Error (RMSE) of 9828.06 yuan, a Mean Absolute Percentage Error (MAPE) of 0.6%, and a Coefficient of Determination (R2) as high as 0.989, confirming the model’s high predictive accuracy and strong generalization capability. Through SHAP value analysis, this study further identifies key factors such as floor plan layout, roof area, and column material coefficient that are central to cost prediction. The ASCNN model proposed in this study not only significantly improves the accuracy of cost estimation for Huizhou replica traditional vernacular dwellings, but also enhances its transparency and credibility through model interpretation methods, providing a reliable basis for related investment decisions. The findings of this study also offer valuable references and insights for rapid and precise cost estimation of replica buildings in other regions worldwide.

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Exploring the Relationship Between Learning Rate, Batch Size, and Epochs in Deep Learning: An Experimental Study

Cited by 3 publications

References 6 publications

Teachable Machine: Optimization of Herbal Plant Image Classification Based on Epoch Value, Batch Size and Learning Rate

Teachable Machine: Optimization of Herbal Plant Image Classification Based on Epoch Value, Batch Size and Learning Rate

An modified intelligent real-time crack detection method for bridge based on improved target detection algorithm and transfer learning

Hybrid Intelligent Model for Estimating the Cost of Huizhou Replica Traditional Vernacular Dwellings

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