keras

Wrapper class you can use to make any Keras model compatible with CleanLearning and sklearn. Use KerasWrapperModel to wrap existing functional API code for keras.Model objects, and KerasWrapperSequential to wrap existing tf.keras.models.Sequential objects. Most of the instance methods of this class work the same as the ones for the wrapped Keras model, see the Keras documentation for details.

This is a good example of making any bespoke neural network compatible with cleanlab.

You must have Tensorflow 2 installed (only compatible with Python versions >= 3.7). This wrapper class is only fully compatible with tensorflow<2.11, if using tensorflow>=2.11, please replace your Optimizer class with the legacy Optimizer here.

Warning

For those on TensorFlow version 2.16 or higher, please note that direct compatibility is not yet fully established. We are actively working to extend support to these newer versions.

In the interim, users are advised to use TensorFlow versions up to 2.15 to ensure stability and maintain compatibility. This can be done by specifying the TensorFlow version in your package manager, for example:

pip install tensorflow<2.16

This approach ensures that you can continue utilizing the full functionality of this wrapper class until an update accommodating newer TensorFlow versions is released.

Tips:

• If this class lacks certain functionality, you can alternatively try scikeras.

• Unlike scikeras, our KerasWrapper classes can operate directly on tensorflow.data.Dataset objects (like regular Keras models).

• To call fit() on a tensorflow Dataset object with a Keras model, the Dataset should already be batched.

• Check out our example using this class: huggingface_keras_imdb

• Our unit tests also provide basic usage examples.

Classes:

KerasWrapperModel(model[, model_kwargs, ...])	Takes in a callable function to instantiate a Keras Model (using Keras functional API) that is compatible with CleanLearning and sklearn.
KerasWrapperSequential([layers, name, ...])	Makes any tf.keras.models.Sequential object compatible with CleanLearning and sklearn.

class cleanlab.models.keras.KerasWrapperModel(model, model_kwargs={}, compile_kwargs={'loss': <keras.losses.SparseCategoricalCrossentropy object>}, params=None)

Bases: object

Takes in a callable function to instantiate a Keras Model (using Keras functional API) that is compatible with CleanLearning and sklearn.

The instance methods of this class work in the same way as those of any keras.Model object, see the Keras documentation for details. For using Keras sequential instead of functional API, see the KerasWrapperSequential class.

Parameters:

• model (Callable) –

  A callable function to construct the Keras Model (using functional API). Pass in the function here, not the constructed model!

  For example:

  def model(num_features, num_classes):
      inputs = tf.keras.Input(shape=(num_features,))
      outputs = tf.keras.layers.Dense(num_classes)(inputs)
      return tf.keras.Model(inputs=inputs, outputs=outputs, name="my_keras_model")
  

• model_kwargs (dict, default = {}) – Dict of optional keyword arguments to pass into model() when instantiating the keras.Model.

• compile_kwargs (dict, default = {"loss": tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)}) – Dict of optional keyword arguments to pass into model.compile() for declaring loss, metrics, optimizer, etc.

Methods:

get_params([deep])	Returns the parameters of the Keras model.
set_params(**params)	Set the parameters of the Keras model.
fit(X[, y])	Trains a Keras model.
predict_proba(X, *[, apply_softmax])	Predict class probabilities for all classes using the wrapped Keras model.
predict(X, **kwargs)	Predict class labels using the wrapped Keras model.
summary(**kwargs)	Returns the summary of the Keras model.

get_params(deep=True)

Returns the parameters of the Keras model.

set_params(**params)

Set the parameters of the Keras model.

fit(X, y=None, **kwargs)

Trains a Keras model.

Parameters:

• X (tf.Dataset or np.array or pd.DataFrame) – If X is a tensorflow dataset object, it must already contain the labels as is required for standard Keras fit.

• y (np.array or pd.DataFrame, default = None) – If X is a tensorflow dataset object, you can optionally provide the labels again here as argument y to be compatible with sklearn, but they are ignored. If X is a numpy array or pandas dataframe, the labels have to be passed in using this argument.

predict_proba(X, *, apply_softmax=True, **kwargs)

Predict class probabilities for all classes using the wrapped Keras model. Set extra argument apply_softmax to True to indicate your network only outputs logits not probabilities.

Parameters:

X (tf.Dataset or np.array or pd.DataFrame) – Data in the same format as the original X provided to fit().

predict(X, **kwargs)

Predict class labels using the wrapped Keras model.

Parameters:

X (tf.Dataset or np.array or pd.DataFrame) – Data in the same format as the original X provided to fit().

summary(**kwargs)

Returns the summary of the Keras model.

class cleanlab.models.keras.KerasWrapperSequential(layers=None, name=None, compile_kwargs={'loss': <keras.losses.SparseCategoricalCrossentropy object>}, params=None)

Bases: object

Makes any tf.keras.models.Sequential object compatible with CleanLearning and sklearn.

KerasWrapperSequential is instantiated in the same way as a keras Sequential object, except for optional extra compile_kwargs argument. Just instantiate this object in the same way as your tf.keras.models.Sequential object (rather than passing in an existing Sequential object). The instance methods of this class work in the same way as those of any keras Sequential object, see the Keras documentation for details.

Parameters:

• layers (list) – A list containing the layers to add to the keras Sequential model (same as for tf.keras.models.Sequential).

• name (str, default = None) – Name for the Keras model (same as for tf.keras.models.Sequential).

• compile_kwargs (dict, default = {"loss": tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)}) – Dict of optional keyword arguments to pass into model.compile() for declaring loss, metrics, optimizer, etc.

Methods:

get_params([deep])	Returns the parameters of the Keras model.
set_params(**params)	Set the parameters of the Keras model.
fit(X[, y])	Trains a Sequential Keras model.
predict_proba(X, *[, apply_softmax])	Predict class probabilities for all classes using the wrapped Keras model.
predict(X, **kwargs)	Predict class labels using the wrapped Keras model.
summary(**kwargs)	Returns the summary of the Keras model.

get_params(deep=True)

Returns the parameters of the Keras model.

set_params(**params)

Set the parameters of the Keras model.

fit(X, y=None, **kwargs)

Trains a Sequential Keras model.

Parameters:

• X (tf.Dataset or np.array or pd.DataFrame) – If X is a tensorflow dataset object, it must already contain the labels as is required for standard Keras fit.

• y (np.array or pd.DataFrame, default = None) – If X is a tensorflow dataset object, you can optionally provide the labels again here as argument y to be compatible with sklearn, but they are ignored. If X is a numpy array or pandas dataframe, the labels have to be passed in using this argument.

predict_proba(X, *, apply_softmax=True, **kwargs)

Predict class probabilities for all classes using the wrapped Keras model. Set extra argument apply_softmax to True to indicate your network only outputs logits not probabilities.

Parameters:

X (tf.Dataset or np.array or pd.DataFrame) – Data in the same format as the original X provided to fit().

predict(X, **kwargs)

Predict class labels using the wrapped Keras model.

Parameters:

X (tf.Dataset or np.array or pd.DataFrame) – Data in the same format as the original X provided to fit().

summary(**kwargs)

Returns the summary of the Keras model.