Metadata-Version: 2.1
Name: tensorflow-model-remediation
Version: 0.1.5
Summary: TensorFlow Model Remediation
Home-page: https://github.com/tensorflow/model-remediation
Author: Google LLC
Author-email: packages@tensorflow.org
License: Apache 2.0
Description: # 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.
        
        [![PyPI version](https://badge.fury.io/py/tensorflow-model-remediation.svg)](https://badge.fury.io/py/tensorflow-model-remediation)
        [![Tutorial](https://img.shields.io/badge/doc-tutorial-blue.svg)](https://www.tensorflow.org/responsible_ai/model_remediation/min_diff/tutorials/min_diff_keras)
        [![Overview](https://img.shields.io/badge/doc-overview-blue.svg)](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.*
        
Keywords: tensorflow model remediation fairness responsible machine learning
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.6,<4
Description-Content-Type: text/markdown
