alibi_detect.utils.tensorflow.prediction

Functions

predict_batch

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

Make batch predictions on a model.

Name	Type	Default	Description
x	Union[list, numpy.ndarray, tensorflow.python.framework.tensor.Tensor]		Batch of instances.
model	Union[Callable, keras.src.models.model.Model]		tf.keras model or one of the other permitted types defined in Data.
batch_size	int	10000000000	Batch size used during prediction.
preprocess_fn	Optional[Callable]	None	Optional preprocessing function for each batch.
dtype	Union[type[numpy.generic], tensorflow.python.framework.dtypes.DType]	<class 'numpy.float32'>	Model output type, e.g. np.float32 or tf.float32.

Returns

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

predict_batch_transformer

predict_batch_transformer(x: Union[list, numpy.ndarray], model: keras.src.models.model.Model, tokenizer: Callable, max_len: int, batch_size: int = 10000000000, dtype: Union[type[numpy.generic], tensorflow.python.framework.dtypes.DType] = <class 'numpy.float32'>) -> Union[numpy.ndarray, tensorflow.python.framework.tensor.Tensor]

Make batch predictions using a transformers tokenizer and model.

Name	Type	Default	Description
x	Union[list, numpy.ndarray]		Batch of instances.
model	keras.src.models.model.Model		Transformer model.
tokenizer	Callable		Tokenizer for model.
max_len	int		Max token length.
batch_size	int	10000000000	Batch size.
dtype	Union[type[numpy.generic], tensorflow.python.framework.dtypes.DType]	<class 'numpy.float32'>	Model output type, e.g. np.float32 or tf.float32.

Returns

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