alibi_detect.cd.tensorflow.preprocess

HiddenOutput

Inherits from: Model, TensorFlowTrainer, Trainer, Layer, TFLayer, KerasAutoTrackable, AutoTrackable, Trackable, Operation, KerasSaveable

Constructor

HiddenOutput(self, model: keras.src.models.model.Model, layer: int = -1, input_shape: tuple = None, flatten: bool = False) -> None

Name	Type	Default	Description
model	keras.src.models.model.Model
layer	int	-1
input_shape	Optional[tuple]	None
flatten	bool	False

Methods

call

call(x: Union[numpy.ndarray, tensorflow.python.framework.tensor.Tensor]) -> tensorflow.python.framework.tensor.Tensor

Name	Type	Default	Description
x	Union[numpy.ndarray, tensorflow.python.framework.tensor.Tensor]

Returns

• Type: tensorflow.python.framework.tensor.Tensor

UAE

Inherits from: Model, TensorFlowTrainer, Trainer, Layer, TFLayer, KerasAutoTrackable, AutoTrackable, Trackable, Operation, KerasSaveable

Constructor

UAE(self, encoder_net: Optional[keras.src.models.model.Model] = None, input_layer: Union[keras.src.layers.layer.Layer, keras.src.models.model.Model, NoneType] = None, shape: Optional[tuple] = None, enc_dim: Optional[int] = None) -> None

Name	Type	Default	Description
encoder_net	Optional[keras.src.models.model.Model]	None
input_layer	Union[keras.src.layers.layer.Layer, keras.src.models.model.Model, None]	None
shape	Optional[tuple]	None
enc_dim	Optional[int]	None

Methods

call

call(x: Union[numpy.ndarray, tensorflow.python.framework.tensor.Tensor, Dict[str, tensorflow.python.framework.tensor.Tensor]]) -> tensorflow.python.framework.tensor.Tensor

Name	Type	Default	Description
x	Union[numpy.ndarray, tensorflow.python.framework.tensor.Tensor, Dict[str, tensorflow.python.framework.tensor.Tensor]]

Returns

• Type: tensorflow.python.framework.tensor.Tensor

Functions

preprocess_drift

preprocess_drift(x: Union[numpy.ndarray, list], model: keras.src.models.model.Model, preprocess_batch_fn: Optional[Callable] = None, tokenizer: Optional[Callable] = None, max_len: Optional[int] = None, batch_size: int = 10000000000, dtype: type[numpy.generic] = <class 'numpy.float32'>) -> Union[numpy.ndarray, tensorflow.python.framework.tensor.Tensor]

Prediction function used for preprocessing step of drift detector.

Name	Type	Default	Description
x	Union[numpy.ndarray, list]		Batch of instances.
model	keras.src.models.model.Model		Model used for preprocessing.
preprocess_batch_fn	Optional[Callable]	None	Optional batch preprocessing function. For example to convert a list of objects to a batch which can be processed by the TensorFlow model.
tokenizer	Optional[Callable]	None	Optional tokenizer for text drift.
max_len	Optional[int]	None	Optional max token length for text drift.
batch_size	int	10000000000	Batch size.
dtype	type[numpy.generic]	<class 'numpy.float32'>	Model output type, e.g. np.float32 or tf.float32.

Returns

• Type: Union[numpy.ndarray, tensorflow.python.framework.tensor.Tensor]