tensorflow_model_remediation-0.1.7.tar.gz

# TensorFlow Model Remediation TensorFlow Model Remediation is a library that provides solutions for machine learning practitioners working to create and train models in a way that reduces or eliminates user harm resulting from underlying performance biases. [](https://badge.fury.io/py/tensorflow-model-remediation) [](https://www.tensorflow.org/responsible_ai/model_remediation/min_diff/tutorials/min_diff_keras) [](https://www.tensorflow.org/responsible_ai/model_remediation) ## Installation You can install the package from `pip`: ```shell $ pip install tensorflow-model-remediation ``` Note: Make sure you are using TensorFlow 2.x. ## Documentation This library contains a collection of machine learning remediation techniques for addressing potential bias in a model. Currently TensorFlow Model Remediation contains the below techniques: * MinDiff technique: Typically used to ensure that a model predicts the preferred label equally well for all values of a sensitive attribute. Helpful when trying to achieve (equality of opportunity)[https://developers.google.com/machine-learning/glossary/fairness#equality-of-opportunity]. * Counterfactual Logit Pairing technique: Typically used to ensure that a model’s prediction does not change between “counterfactual pairs”, where the sensitive attribute referenced in a feature is different. Helpful when trying to achieve [counterfactual fairness](https://developers.google.com/machine-learning/glossary/fairness#counterfactual-fairness). We recommend starting with the [overview guide](https://www.tensorflow.org/responsible_ai/model_remediation) to get an idea of TensorFlow Model Remediation. Next try one of our interactive guides like the [MinDiff tutorial notebook](https://www.tensorflow.org/responsible_ai/model_remediation/min_diff/tutorials/min_diff_keras). [Counterfactual tutorial notebook](https://www.tensorflow.org/responsible_ai/model_remediation/counterfactual/guide/counterfactual_keras). ```python import tensorflow_model_remediation as tfmr import tensorflow as tf # Start by defining a Keras model. original_model = ... # Next pick the remediation technique you'd like to use. For example, a # MinDiff implementation might look like the below: # Set the MinDiff weight and choose a loss. min_diff_loss = tfmr.min_diff.losses.MMDLoss() min_diff_weight = 1.0 # Hyperparamater to be tuned. # Create a MinDiff model. min_diff_model = tfmr.min_diff.keras.MinDiffModel( original_model, min_diff_loss, min_diff_weight) # Compile the MinDiff model as you normally would do with the original model. min_diff_model.compile(...) # Create a MinDiff Dataset and train the min_diff_model on it. min_diff_model.fit(min_diff_dataset, ...) ``` #### *Disclaimers* *If you're interested in learning more about responsible AI practices, including* *fairness, please see Google AI's [Responsible AI Practices](https://ai.google/education/responsible-ai-practices).* *`tensorflow/model_remediation` is Apache 2.0 licensed. See the [`LICENSE`](LICENSE) file.*