## Line Charts: D16 Compression and Alpha Profiles ### Overview The image contains three line charts analyzing training dynamics of a D16 neural network model across four random seeds (Seed 1-4). The charts track compression gradients, regularization strength (L2 alpha), and final layer alpha values across training steps and network layers. ### Components/Axes 1. **Left Chart: D16 Compression Gradient (4 Seeds)** - X-axis: Training Step (0-8000) - Y-axis: SR Gradient (range: -200 to 0) - Legend: Seed 1 (orange), Seed 2 (green), Seed 3 (pink), Seed 42 (blue) - Spatial: Legend positioned right-aligned 2. **Center Chart: D16 L2 Alpha (Peak Layer)** - X-axis: Training Step (0-8000) - Y-axis: Weighted Alpha (range: 0.1-0.5) - Legend: Same color coding as left chart - Spatial: Legend positioned right-aligned 3. **Right Chart: D16 Final α Profile** - X-axis: Layer (0-14) - Y-axis: Final Alpha (range: 0.1-0.5) - Legend: Same color coding - Spatial: Legend positioned right-aligned ### Detailed Analysis **Left Chart Trends:** - Seed 42 (blue) shows sharp initial drop (-200) at step 0, then stabilizes near -50 by step 2000 - Seed 1 (orange) exhibits erratic oscillations (-150 to -50) before stabilizing - Seed 2 (green) shows gradual descent to -100 by step 1000 - Seed 3 (pink) demonstrates fastest initial drop (-180) but recovers to -75 by step 2000 **Center Chart Trends:** - All seeds show gradual alpha increase from 0.1 to 0.5 over 8000 steps - Seed 42 reaches 0.5 first at step 4000 - Seed 1 lags behind, reaching 0.5 at step 6000 - Seed 2 and 3 show similar trajectories with minor fluctuations **Right Chart Trends:** - Seed 42 peaks at layer 2 (0.55) then declines to 0.25 by layer 14 - Seed 1 peaks at layer 4 (0.45) with gradual decline - Seed 2 shows peak at layer 6 (0.4) with sharper drop - Seed 3 peaks at layer 8 (0.35) with gradual decline ### Key Observations 1. **Compression Instability**: All seeds show initial gradient spikes (-200 to -150) suggesting training instability 2. **Alpha Convergence**: Regularization strength (alpha) converges to 0.5 across all seeds by final training step 3. **Layer-Specific Peaks**: Final alpha profiles show distinct peak layers per seed, indicating architecture sensitivity 4. **Seed Variance**: Seed 42 demonstrates most stable training dynamics across all metrics ### Interpretation The data reveals critical insights about model training dynamics: - The initial compression gradient spikes (-200) suggest catastrophic forgetting or sharp parameter updates common in early training phases - The gradual alpha increase (0.1→0.5) indicates progressive regularization strengthening to combat overfitting - Layer-specific alpha peaks (layers 2-8) correlate with model capacity utilization patterns - earlier layers (seed 42) show stronger regularization needs - Seed variance highlights the importance of multiple initializations in neural architecture search Notable anomalies include Seed 42's unusually high final alpha (0.55) at layer 2, suggesting potential over-regularization in early layers. The compression gradient stabilization pattern (left chart) implies successful convergence of loss landscape optimization by step 2000.