tensorflow_model_remediation-0.1.4.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 will ultimately contain a collection of techniques for addressing a wide range of concerns. For now it contains a single technique, MinDiff, which can help reduce performance gaps between example subgroups. We recommend starting with the [overview guide](https://www.tensorflow.org/responsible_ai/model_remediation) or trying it interactively in our [tutorial notebook](https://www.tensorflow.org/responsible_ai/model_remediation/min_diff/tutorials/min_diff_keras). ```python from tensorflow_model_remediation import min_diff import tensorflow as tf # Start by defining a Keras model. original_model = ... # Set the MinDiff weight and choose a loss. min_diff_loss = min_diff.losses.MMDLoss() min_diff_weight = 1.0 # Hyperparamater to be tuned. # Create a MinDiff model. min_diff_model = 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.*