Optimizing CNN Performance: Impact of Learning Rate Tuning
This report explores the impact of varying learning rates on CNN model performance using Weights & Biases (W&B). By leveraging W&B's robust tracking and visualization tools, the analysis clearly demonstrates how different learning rates affect accuracy and loss, enabling rapid convergence and effective weight updates. The seamless management of training artifacts—model versions and logs—ensures reproducibility and facilitates continuous improvement, underscoring the significant benefits of using W&B in
Created on February 22|Last edited on February 22
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Weights & Biases Sweep Analysis
This analysis reviews the sweep results for a neural network model focused on tuning the learning rate while maintaining constant key hyperparameters. It provides a comprehensive overview of the experimental setup, run configurations, performance metrics, and overall findings.
⚙️ Experiment Overview
Objective:
Optimize model performance by adjusting the learning rate.
Fixed Hyperparameters:
Batch Size: 64
Epochs: 5
Hidden Neurons: 128
Variable Hyperparameter:
Learning Rate: Tested values of 0.001, 0.0005, and 0.0001
Each run was executed with a different learning rate to observe how this parameter affects the model's convergence, accuracy, and loss during training and validation.
🏃♂️Detailed Run Configurations and Results
Run 1: Learning Rate = 0.001
Configuration Summary:
Batch Size: 64
Epochs: 5
Hidden Neurons: 128
Learning Rate: 0.001
Performance Metrics:
Training Accuracy: 99.14%
Training Loss: 0.02794
Validation Accuracy: 98.73%
Validation Loss: 0.04378
Analysis:
The training process shows exceptionally high accuracy and minimal loss.
The validation metrics closely follow the training performance, indicating that the model is well-generalized.
The convergence graphs demonstrate a steady decrease in loss and an increase in accuracy from the early epochs, reaching a plateau near the end.
🔍 Key Insight:
A learning rate of 0.001 strikes a good balance, allowing the model to update its weights effectively without overshooting the optimal parameters.
Run 2: Learning Rate = 0.0005
Configuration Summary:
Batch Size: 64
Epochs: 5
Hidden Neurons: 128
Learning Rate: 0.0005
Performance Metrics:
Training Accuracy: 98.93%
Training Loss: 0.04027
Validation Accuracy: 98.62%
Validation Loss: 0.04457
Analysis:
The performance remains strong with high accuracy and low loss, though slightly behind Run 1.
The training and validation curves reveal a stable convergence, though with marginally slower progress compared to the 0.001 learning rate.
The minor drop in accuracy and increase in loss suggest that while the model is learning effectively, the rate of learning is a bit conservative.
🔍 Key Insight:
A learning rate of 0.0005 provides reliable training outcomes but may not be as optimal as 0.001 for faster convergence and peak performance.
Run 3: Learning Rate = 0.0001
Configuration Summary:
Batch Size: 64
Epochs: 5
Hidden Neurons: 128
Learning Rate: 0.0001
Performance Metrics:
Training Accuracy: 97.54%
Training Loss: 0.08108
Validation Accuracy: 97.54%
Validation Loss: 0.08197
Analysis:
This run shows a more noticeable decrease in both training and validation accuracy.
The loss values are significantly higher, indicating that the learning process is less effective.
The convergence graphs for this run exhibit slower progression, suggesting that the model struggles to update its weights efficiently with such a low learning rate.
🔍 Key Insight:
A learning rate of 0.0001 may be too low, leading to underfitting where the model is unable to capture the underlying patterns effectively, resulting in lower accuracy and higher loss.
📈 Performance Comparison and Overall Analysis
Optimal Learning Rate:
The best performance is achieved with a learning rate of 0.001. This configuration resulted in the highest training and validation accuracies and the lowest loss values, indicating effective learning and robust model fitting.
Learning Rate Trade-Offs:
0.0005: While still effective, this rate offers slightly reduced performance, likely due to more conservative weight updates.
0.0001: This rate appears too low, causing the model to converge slowly and settle at suboptimal accuracy levels.
Visual Trends:
All runs display similar learning curves with clear trends of loss reduction and accuracy improvement across epochs. However, the extent and speed of these improvements vary:
Run 1 exhibits rapid convergence and strong performance metrics.
Run 2 shows stable, yet slightly less aggressive learning.
Run 3 suggests that an excessively low learning rate impedes progress, highlighting the importance of a balanced hyperparameter setting.
✅ Conclusion
The hyperparameter sweep underscores the critical role of the learning rate in model training. The optimal learning rate in this experiment is 0.001, which facilitates efficient convergence and superior overall performance.
Run 1 (0.001): Best performance with near-perfect accuracy and minimal loss.
Run 2 (0.0005): Competent performance but slightly less optimal.
Run 3 (0.0001): Underfitting due to too slow learning dynamics.
• Optimal Performance:
The run with a learning rate of 0.001 demonstrates the best overall performance, achieving the highest accuracy and the lowest loss values.
• Conservative Learning:
With a learning rate of 0.0005, the model still performs well but updates weights more conservatively, leading to slightly lower accuracy and higher loss compared to the optimal setting.
• Underfitting Risk:
A learning rate of 0.0001 results in the lowest accuracies and the highest loss values, suggesting that the learning rate is too low for effective convergence, potentially causing underfitting.
Significance of Artifact Management in MLOps
Ensuring Reproducibility
Effective artifact management plays a pivotal role in tracking every element of our experiments—ranging from models to datasets. By saving each model version as an artifact in W&B, we can retrieve precise configurations and parameters, making it straightforward to reproduce experiments. This process is essential for validating outcomes and guaranteeing that our findings can be consistently replicated.
Robust Version Control
Artifact management also serves as a powerful version control mechanism for models and datasets. Each training cycle produces a new artifact, allowing us to easily revert to earlier versions if needed. This systematic approach not only facilitates detailed comparisons between model iterations but also enhances our understanding of how variations in hyperparameters or data influence performance. Ultimately, robust version control is key to maintaining a reliable and transparent machine learning workflow.
Trade-offs Between Different Configurations
In any hyperparameter optimization exercise, finding the right balance is key. In the context of our neural network sweep using Weights & Biases, we explored three different learning rate configurations—0.001, 0.0005, and 0.0001—while keeping other parameters constant (batch size: 64, epochs: 5, hidden neurons: 128). Each configuration exhibited unique trade-offs in terms of convergence speed, training and validation accuracy, and loss.
1. Learning Rate = 0.001 (Clean-Sweep-1)
Pros:
Rapid Convergence: This configuration demonstrated the fastest and most consistent decrease in both training and validation loss.
High Accuracy: With training accuracy at 99.14% and validation accuracy at 98.73%, the model not only fit the training data exceptionally well but also generalized effectively to unseen data.
Effective Weight Updates: The chosen learning rate allows for significant weight adjustments without overshooting the optimum, striking a balance between exploration and stability.
Cons:
Risk of Overshooting: While not evident in this experiment, higher learning rates can sometimes lead to overshooting if the parameter adjustments are too aggressive. In our case, however, 0.001 appears to be well-tuned.
2. Learning Rate = 0.0005 (Crisp-Sweep-2)
Pros:
Stable Convergence: The model converged steadily with robust performance metrics (98.93% training accuracy and 98.62% validation accuracy). This indicates reliable learning, albeit at a more conservative pace.
Lower Risk: A lower learning rate tends to be more stable, minimizing the risk of overshooting but at the cost of slower convergence.
Cons:
Slightly Reduced Performance: Compared to 0.001, the model achieved marginally lower accuracy and higher loss. This suggests that while the learning is safe and controlled, it might not fully leverage the capacity of the model to converge quickly to an optimal solution.
Longer Training Times: The slower pace might become an issue in scenarios where rapid convergence is critical.
3. Learning Rate = 0.0001 (Gallant-Sweep-3)
Pros:
Precise Adjustments: A very low learning rate ensures that weight updates are minute, reducing the chance of drastic missteps during training.
Cons:
Underfitting: The training accuracy drops to 97.54%, with a corresponding increase in both training and validation loss. This indicates that the model is not learning effectively, as the small weight updates fail to capture the underlying data patterns.
Slow Convergence: The minimal progress over epochs points to inefficiencies in the learning process. Even after five epochs, the model lags behind the other configurations in reaching a satisfactory performance level.
Overall Trade-Offs
Convergence Speed vs. Stability:
A higher learning rate (0.001) promotes faster convergence and achieves higher accuracy but could potentially risk overshooting if not tuned properly. A lower learning rate (0.0005 or 0.0001), on the other hand, provides stability but may slow down the learning process. In our experiments, 0.0005 strikes a middle ground, yet it still falls short of the performance reached with 0.001.
Risk of Overfitting vs. Underfitting:
While a high learning rate might sometimes lead to overfitting if the model learns the training data too quickly and fails to generalize, our observations indicate that 0.001 achieves an excellent balance, with validation metrics closely mirroring training performance. Conversely, 0.0001 leads to underfitting, where the model does not capture the complexity of the data well enough.
Artifact Management with W&B:
Using Weights & Biases enabled meticulous tracking of these metrics and artifacts for each run. The clear visualization of trends in loss and accuracy across different configurations was instrumental in assessing the trade-offs. It allowed us to pinpoint the optimal learning rate and understand the consequences of choosing more conservative settings, thus highlighting the critical role of artifact management in MLOps for reproducibility and iterative improvement.
Conclusion
The trade-offs between these configurations underscore the delicate balance required in hyperparameter tuning. The learning rate of 0.001 emerged as the optimal setting in our experiments, offering fast convergence and high accuracy without risking overshooting. In contrast, while a lower learning rate like 0.0005 offers safety and stability, it does so at the cost of slower progress, and an excessively low rate like 0.0001 leads to underfitting. Understanding these trade-offs is essential for optimizing model performance and ensuring robust and reproducible results in machine learning workflows.
Chart Analysis
Comparative Chart Analysis
Validation Loss
clean-sweep-1 (LR=0.001) maintains the lowest validation loss throughout, reflecting robust generalization.
crisp-sweep-2 (LR=0.0005) follows a similar downward trend but settles slightly higher than clean-sweep-1.
gallant-sweep-3 (LR=0.0001) remains at the highest validation loss, indicating less effective learning on unseen data.
Validation Accuracy
clean-sweep-1 attains the highest validation accuracy, quickly outpacing the other runs.
crisp-sweep-2 nearly keeps pace, though it remains marginally behind clean-sweep-1.
gallant-sweep-3 lags in validation accuracy, though it does show steady improvement over epochs.
Training Loss
All three runs see a steep drop initially. clean-sweep-1 drops fastest and stays lowest, pointing to efficient updates.
crisp-sweep-2 follows a similar path, just slightly above the green line.
gallant-sweep-3 hovers at a higher loss, reinforcing its slower convergence.
Training Accuracy
clean-sweep-1 reaches near-perfect accuracy quickly and holds that level.
crisp-sweep-2 is competitive but slightly lower in final accuracy.
gallant-sweep-3 continues to improve but ends up below the other two in training accuracy.
Epoch
Each run spans the same number of epochs (five), highlighting that differences in performance arise from the learning rate rather than training duration.
Overall Findings
Clean-Sweep-1 Leads
Highest training and validation accuracy, lowest loss, and swift convergence.
Learning rate 0.001 appears optimal among these three configurations.
Crisp-Sweep-2 Remains Close
Consistently strong performance but slightly below Run 1.
A somewhat lower learning rate yields stable training but slower or less optimal convergence.
Gallant-Sweep-3 Underperforms
Lower accuracy and higher loss point to underfitting.
A learning rate of 0.0001 is likely too conservative, impeding weight updates.
Summary:
Tuning the learning rate significantly impacts both the speed of convergence and the final accuracy/loss metrics. A learning rate of 0.001 provides the best balance, allowing the model to learn efficiently without overshooting. While 0.0005 performs well, it does so more slowly, and 0.0001 proves too low, causing underfitting.
Run set
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