[line-loss-training] Training Loss Curve

[MarkusNeusinger]

A line plot showing training and validation loss curves over epochs during neural network training. Essential for monitoring model training, detecting overfitting, and determining optimal stopping points.

[claude]

✅ Spec ID: line-loss-training

Rationale: This is a line plot showing training/validation loss over epochs, distinct from learning-curve-basic which shows scores vs training set size.

Add label approved to begin generation.

[github-actions]

✅ Specification Ready: line-loss-training

PR: #2868

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specification.md

line-loss-training: Training Loss Curve

Description

A line plot showing training and validation loss curves over epochs during neural network training. This visualization is essential for monitoring model training, detecting overfitting (when validation loss diverges from training loss), and determining optimal early stopping points. The dual-curve display reveals the gap between training and generalization performance.

Applications

• Monitoring deep learning model training to detect overfitting or underfitting
• Determining the optimal epoch for early stopping based on validation loss plateau or increase
• Comparing training dynamics across different model architectures or hyperparameter configurations

Data

• epoch (numeric) - Training epoch number or iteration count
• train_loss (numeric) - Loss values on training data at each epoch
• val_loss (numeric) - Loss values on validation data at each epoch
• Size: 10-500 epochs typical, logged at each epoch or at regular intervals
• Example: Training history from Keras/PyTorch containing epoch, training loss, and validation loss

Notes

• Use distinct colors for training (e.g., blue) and validation (e.g., orange) curves
• Include a legend clearly labeling each curve
• X-axis should start at epoch 1 (or 0) and extend to final epoch
• Y-axis label should specify the loss function used (e.g., Cross-Entropy Loss, MSE)
• Optionally mark the epoch with minimum validation loss to indicate optimal stopping point
• Consider log scale for y-axis if loss spans multiple orders of magnitude

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Next: Add approved label to merge this specification to main.

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🎨 seaborn Preview

PR: #2902

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🎨 plotly Preview

PR: #2903
Interactive: Open HTML

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