Google I/O returns May 18-20! Reserve space and build your schedule Register now

Integrating MinDiff with MinDiffModel


There are two steps to integrating MinDiff into your model:

  1. Prepare the data (covered in the input preparation guide).

  2. Alter or create a model that will integrate MinDiff during training.

This guide will cover the simplest way to complete the second step: using MinDiffModel.


pip install -q --upgrade tensorflow-model-remediation
import tensorflow as tf
tf.get_logger().setLevel('ERROR')  # Avoid TF warnings.
from tensorflow_model_remediation import min_diff
from import uci as tutorials_utils

First, download the data. For succinctness, the input preparation logic has been factored out into helper functions as described in the input preparation guide. You can read the full guide for details on this process.

# Original DataFrame for training, sampled at 0.3 for reduced runtimes.
train_df = tutorials_utils.get_uci_data(split='train', sample=0.3)

# Dataset needed to train with MinDiff.
train_with_min_diff_ds = (
    tutorials_utils.get_uci_with_min_diff_dataset(split='train', sample=0.3))

Original Model

This guide uses a basic, untuned keras.Model using the Functional API to highlight using MinDiff. In a real world application, you would carefully choose the model architecture and use tuning to improve model quality before attempting to address any fairness issues.

Since MinDiffModel is designed to work with most Keras Model classes, we have factored out the logic of building the model into a helper function: get_uci_model.

Training with a Pandas DataFrame

This guide trains over a single epoch for speed, but could easily improve the model's performance by increasing the number of epochs.

model = tutorials_utils.get_uci_model()

model.compile(optimizer='adam', loss='binary_crossentropy')

df_without_target = train_df.drop(['target'], axis=1)  # Drop 'target' for x.
_ =
    x=dict(df_without_target),  # The model expects a dictionary of features.
77/77 [==============================] - 3s 23ms/step - loss: 0.8589

Training with a

The equivalent training with a would look very similar (although initialization and input randomness may yield slightly different results).

model = tutorials_utils.get_uci_model()

model.compile(optimizer='adam', loss='binary_crossentropy')

_ =
    tutorials_utils.df_to_dataset(train_df, batch_size=128),  # Converted to Dataset.
77/77 [==============================] - 3s 23ms/step - loss: 0.6416

Integrating MinDiff for training

Once the data has been prepared, apply MinDiff to your model with the following steps:

  1. Create the original model as you would without MinDiff.
original_model = tutorials_utils.get_uci_model()
  1. Wrap it in a MinDiffModel.
min_diff_model = min_diff.keras.MinDiffModel(
  1. Compile it as you would without MinDiff.
min_diff_model.compile(optimizer='adam', loss='binary_crossentropy')
  1. Train it with the MinDiff dataset (train_with_min_diff_ds in this case).
_ =, epochs=1)
77/77 [==============================] - 6s 31ms/step - loss: 0.7883 - min_diff_loss: 0.0379

Evaluation and Prediction with MinDiffModel

Both evaluating and predicting with a MinDiffModel are similar to doing so with the original model.

When calling evaluate you can pass in either the original dataset or the one containing MinDiff data. If you choose the latter, you will also get the min_diff_loss metric in addition to any other metrics being measured loss will also include the min_diff_loss.

When calling evaluate you can pass in either the original dataset or the one containing MinDiff data. If you include MinDiff in the call to evaluate, two things will differ:

  • An additional metric called min_diff_loss will be present in the output.
  • The value of the loss metric will be the sum of the original loss metric (not shown in the output) and the min_diff_loss.
_ = min_diff_model.evaluate(
    tutorials_utils.df_to_dataset(train_df, batch_size=128))
# Calling with MinDiff data will include min_diff_loss in metrics.
_ = min_diff_model.evaluate(train_with_min_diff_ds)
77/77 [==============================] - 2s 22ms/step - loss: 0.4638
77/77 [==============================] - 3s 32ms/step - loss: 0.5087 - min_diff_loss: 0.0451

When calling predict you can technically also pass in the dataset with the MinDiff data but it will be ignored and not affect the output.

_ = min_diff_model.predict(
    tutorials_utils.df_to_dataset(train_df, batch_size=128))
_ = min_diff_model.predict(train_with_min_diff_ds)  # Identical to results above.

Limitations of using MinDiffModel directly

When using MinDiffModel as described above, most methods will use the default implementations of tf.keras.Model (exceptions listed in the API documentation).

print(' ==')
print( ==
print('MinDiffModel.train_step == keras.Model.train_step')
print(min_diff.keras.MinDiffModel.train_step == tf.keras.Model.train_step) ==
MinDiffModel.train_step == keras.Model.train_step

For keras.Sequential or keras.Model, this is perfectly fine since they use the same functions.

print(' ==')
print( ==
print('tf.keras.Sequential.train_step == keras.Model.train_step')
print(tf.keras.Sequential.train_step == tf.keras.Model.train_step) ==
tf.keras.Sequential.train_step == keras.Model.train_step

However, if your model is a subclass of keras.Model, wrapping it with MinDiffModel will effectively lose the customization.

class CustomModel(tf.keras.Model):

  def train_step(self, **kwargs):
    pass  # Custom implementation.

print('CustomModel.train_step == keras.Model.train_step')
print(CustomModel.train_step == tf.keras.Model.train_step)
CustomModel.train_step == keras.Model.train_step

If this is your use case, you should not use MinDiffModel directly. Instead, you will need to subclass it as described in the customization guide.

Additional Resources