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ProbabalisticBayesianModel-Wine
keras-io/ProbabalisticBayesianModel-Wine
ProbabalisticBayesianModel-Wine is a machine learning model from keras-io. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for keras.
This repo contains model weights for the the probabilistic model from Probabilistic Bayesian Neural Networks. This example demonstrates how to build basic probabilistic Bayesian neural networks to account for these tw…
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Updated Jun 12, 2022
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7.6 MB
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At a glance
- Library
- keras
- Access
- Public
- Created
- Jun 6, 2022
- Updated
- Jun 12, 2022
- SHA
- 7cf45a19
Model card
From the Hugging Face model README
Model description
This repo contains model weights for the the probabilistic model from Probabilistic Bayesian Neural Networks. This example demonstrates how to build basic probabilistic Bayesian neural networks to account for these two types of uncertainty. We use TensorFlow Probability library, which is compatible with Keras API.
Taking a probabilistic approach to deep learning allows to account for uncertainty, so that models can assign less levels of confidence to incorrect predictions. Sources of uncertainty can be found in the data, due to measurement error or noise in the labels, or the model, due to insufficient data availability for the model to learn effectively.
Full credits go to Khalid Salama
Using this model
This repo contains model weights only. To use this model, refer to the following code contained in load_bnn_model.py.
Training and evaluation data 🍷
We use the wine quality dataset found here. Each wine was scored from 0-10 by wine experts, and includes 11 physicochemical features about the wine.
Versioning
The training was done using TensorFlow 2.8.0 and TensorFlow Probability 0.16.0. When working with TensorFlow Probability, it is encouraged to check out the releases to make sure you are using a stable TensorFlow counterpart.
Training hyperparameters
| Optimizer | learning_rate | decay | rho | momentum | epsilon | centered | training_precision |
|---|---|---|---|---|---|---|---|
| RMSprop | 0.001 | 0.0 | 0.9 | 0.0 | 1e-07 | False | float32 |
- Library
- keras
- Created
- Jun 6, 2022
- Updated
- Jun 12, 2022