## Heatmap: Training Accuracy Over Epochs ### Overview The heatmap illustrates the training accuracy of a model over different numbers of epochs (R=1, R=2, R=4, R=8) and training times (0, 10, 20, 30, 40 hours). The x-axis represents the number of epochs, while the y-axis represents the training time in hours. The colors indicate the accuracy levels, with darker shades representing higher accuracy. ### Components/Axes - **Epochs**: The x-axis is labeled "Epochs" and ranges from 0 to 40. - **Training Time (Hours)**: The y-axis is labeled "Training Time (Hours)" and ranges from 0 to 40 hours. - **Accuracy**: The color scale ranges from 0.00 to 1.00, with darker shades indicating higher accuracy. ### Detailed Analysis - **R=1**: The accuracy is relatively stable and high, especially after 20 epochs. - **R=2**: The accuracy shows a slight increase after 20 epochs and then stabilizes. - **R=4**: The accuracy increases significantly after 20 epochs and then stabilizes. - **R=8**: The accuracy increases the most after 20 epochs and then stabilizes. ### Key Observations - The accuracy is highest for R=4 and R=8, especially after 20 epochs. - The accuracy for R=1 and R=2 is lower but still relatively high. - There is a noticeable increase in accuracy with more epochs for all R values. ### Interpretation The heatmap suggests that the model's accuracy improves with more epochs, especially for R=4 and R=8. The training time also affects the accuracy, with longer training times generally resulting in higher accuracy. The model's performance is consistent across different R values, indicating that the choice of R value does not significantly impact the training accuracy.

## Line Graphs: Model Performance Across Training Parameters ### Overview The image contains three line graphs comparing model accuracy across different training parameters (R values) and stages. Each graph uses distinct axes and visualizations to highlight performance trends. --- ### Components/Axes 1. **Left Graph** - **X-axis**: Epochs (0–8000) - **Y-axis**: Accuracy (0.00–1.00) - **Legend**: - R=1 (blue) - R=2 (orange) - R=4 (green) - R=8 (red) - **Shading**: Confidence intervals (light gray around lines). 2. **Middle Graph** - **X-axis**: Training Time (Hours) (0–40) - **Y-axis**: Accuracy (0.00–1.00) - **Legend**: Same R values as left graph. - **Shading**: Confidence intervals. 3. **Right Graph** - **X-axis**: Epochs (log scale, 10⁰–10⁴) - **Y-axis**: Accuracy (0.00–1.00) - **Legend**: - Train (blue) - Test ID (orange) - Test OOD (green) - **Vertical Dashed Lines**: Stage1, Stage2, Stage3 markers. --- ### Detailed Analysis #### Left Graph (Epochs vs Accuracy) - **R=1 (blue)**: Starts at ~0.05 accuracy, plateaus at ~0.75 by 2000 epochs. - **R=2 (orange)**: Gradual rise to ~0.75 by 6000 epochs. - **R=4 (green)**: Stable at ~0.75 from ~2000 epochs. - **R=8 (red)**: Sharp increase to ~0.85 by 6000 epochs. - **Uncertainty**: All lines have ±0.05–0.10 variability (shaded regions). #### Middle Graph (Training Time vs Accuracy) - **R=1 (blue)**: Reaches ~0.75 by 10 hours, then plateaus. - **R=2 (orange)**: Slower rise to ~0.75 by 30 hours. - **R=4 (green)**: Stable at ~0.75 after 10 hours. - **R=8 (red)**: Rapid rise to ~0.85 by 20 hours. #### Right Graph (Log-Scaled Epochs vs Accuracy) - **Train (blue)**: Exponential growth, plateaus at ~0.95 by 10³ epochs. - **Test ID (orange)**: Sharp rise to ~0.95 by 10² epochs, then plateaus. - **Test OOD (green)**: Gradual rise to ~0.75 by 10³ epochs. - **Stages**: - Stage1: ~10¹ epochs (blue/orange divergence). - Stage2: ~10² epochs (orange plateau). - Stage3: ~10³ epochs (green rise). --- ### Key Observations 1. **R Value Impact**: - Higher R (e.g., R=8) achieves faster convergence but risks overfitting (Test OOD lags Test ID). - Lower R (e.g., R=1) stabilizes earlier but with lower peak accuracy. 2. **Training Dynamics**: - Test OOD accuracy (~0.75) is significantly lower than Test ID (~0.95), suggesting poor generalization. - Log-scale visualization emphasizes early-stage performance differences. 3. **Stage Progression**: - Stage1: Initial training phase (blue/orange divergence). - Stage2: Model stabilization (orange plateau). - Stage3: OOD performance improvement (green rise). --- ### Interpretation - **Model Complexity Trade-off**: Higher R values improve training accuracy but may overfit, as seen in the disparity between Test ID and Test OOD. - **Efficiency vs Robustness**: Lower R values (e.g., R=1) train faster but sacrifice peak performance. - **Stage3 Significance**: The gradual rise in Test OOD accuracy suggests late-stage adaptation to out-of-distribution data, possibly through regularization or architectural adjustments. This analysis underscores the need to balance model complexity (R) with generalization goals, particularly for real-world deployment where OOD performance is critical.