AI: hyperparameter, metric, metricDecisionThreshold - can be multiple. Property talks about only one.

[VenkatTechnologist]

There could be multiple hyperparameters, metrics, and metricDecisionThresholds for an AI model.
The properties 'hyperparameter', 'metric' and 'metricDecisionThreshold' talk about only one value, and their class
is DictionaryEntry, which can hold only one key-pair. How can we accommodate multiple hyperparameters,
metrics, and metricDecisionThresholds of an AI model in the AI profile of SPDX?

[bennetkl]

We decided for first release of AI BOM  to keep it relatively simple, but true we should consider this functionality in 3.1.  Do you have an use case /  example of multiple  hyperparameters in use (ideally an open source project). On Thursday, May 9, 2024 at 07:13:23 a.m. EDT, VenkatTechnologist ***@***.***> wrote: There could be multiple hyperparameters while training an AI model. The property 'hyperparameter' talks about one value, and it's data type is dictionary, which has minValue as 1 and maxValue as 1. How can we accommodate multiple hyperparameters of an AI model in the AI profile of SPDX? — Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you are subscribed to this thread.Message ID: ***@***.***>

[VenkatTechnologist]

Some real world, open source examples where multiple hyperparameters are used at once:

Machine Learning:

• XGBoost (eXtreme Gradient Boosting) : n_estimators, learning_rate, max_depth, L1/L2 regularization parameters
• TensorFlow/Keras Convolutional Neural Network (CNN): No. of convolution layers & filters, Kernel size, Pooling layer settings, Optimizer and learning rate
• Scikit-learn Support Vector Machine (SVM): Regularization parameter C, Kernel, Gamma (for RBF Kernel)

Generative-AI:

• StyleGAN2 - TensorFlow/Keras (Generative Adversarial Network): No. of layers & filters, Learning rates for generator and discriminator, Noise input dimension, Regularization hyperparameters)
• BicycleGAN - PyTorch (Conditional GAN): Network architectures for generator & discriminator, Loss functions, Weight normalization
• OpenAI Gym with TensorFlow/Keras (Reinforcement Learning for Generative Models): Reward function design, Exploration vs exploitation, RL algorithm hyperparameters

[VenkatTechnologist]

Metrics used to assess and their associated thresholds can be multiple in machine learning as well as generative AI algorithms.

Real world, Open source examples:

Generative AI:

StyleGAN2 - PyTorch (Generative Adversarial Networks): Fréchet Inception Distance (FID), Inception Score (IS), Human Evaluation
MelNet - TensorFlow (WaveNet-based Text-to-Speech): Mel-Cepstral Distortion (MCD, Log mel Spectrogram Similarity,
OpenAI GPT-2 (Large Language Model): Perplexity, BLEU

Machine Learning:

Scikit-learn Multi-Class Classification: Accuracy, Confusion Matrix, Precision and Recall, F1-Score
TensorFlow/Keras Object Detection: Mean Average Precision, Intersection over Union
XGBoost Regression with Feature Importance: Mean Squared Error, R-squared, Feature Importance

[VenkatTechnologist]

On that note, it looks like we need a class called 'Dictionary' to list multiple hyperparameters, metrics, and metric thresholds.

See #773.