Advanced control of the model that most users won't need to use.
tfdf.keras.AdvancedArguments(
infer_prediction_signature: Optional[bool] = True,
yggdrasil_training_config: Optional[tfdf.keras.core.YggdrasilTrainingConfig
] = None,
yggdrasil_deployment_config: Optional[tfdf.keras.core.YggdrasilDeploymentConfig
] = None,
fail_on_non_keras_compatible_feature_name: Optional[bool] = True,
predict_single_probability_for_binary_classification: Optional[bool] = True,
metadata_framework: Optional[str] = 'TF Keras',
metadata_owner: Optional[str] = None,
populate_history_with_yggdrasil_logs: bool = False,
disable_categorical_integer_offset_correction: bool = False,
node_format: Optional[tfdf.keras.core.NodeFormat
] = None,
allow_slow_inference: bool = True,
force_ydf_port: Optional[int] = None,
output_secondary_class_predictions: bool = False
)
Attributes |
infer_prediction_signature
|
Instantiate the model graph after training. This
allows the model to be saved without specifying an input signature and
without calling "predict", "evaluate". Disabling this logic can be useful
in two situations: (1) When the exported signature is different from the
one used during training, (2) When using a fixed-shape pre-processing that
consume 1 dimensional tensors (as keras will automatically expend its
shape to rank 2). For example, when using tf.Transform.
|
yggdrasil_training_config
|
Yggdrasil Decision Forests training
configuration. Expose a few extra hyper-parameters.
yggdrasil_deployment_config: Configuration of the computing resources used
to train the model e.g. number of threads. Does not impact the model
quality.
|
fail_on_non_keras_compatible_feature_name
|
If true (default), training will
fail if one of the feature name is not compatible with part of the Keras
API. If false, a warning will be generated instead.
|
predict_single_probability_for_binary_classification
|
Only used for binary
classification. If true (default), the prediction of a binary class model
is a tensor of shape [None, 1] containing the probability of the positive
class (value=1). If false, the prediction of a binary class model is a
tensor of shape [None, num_classes=2] containing the probability of the
complementary classes.
|
metadata_framework
|
Metadata describing the framework used to train the
model.
|
metadata_owner
|
Metadata describing who trained the model.
|
populate_history_with_yggdrasil_logs
|
If false (default) and if a validation
dataset is provided, populate the model's history with the final
validation evaluation computed by the Keras metric (i.e. one evaluation).
If true or if no validation dataset is provided, populate the model's
history with the yggdrasil training logs. The yggdrasil training logs
contains more metrics, but those might not be comparable with other non
TF-DF models.
|
disable_categorical_integer_offset_correction
|
Yggdrasil Decision Forests
reserves the value 0 of categorical integer features to the OOV item, so
the value 0 cannot be used directly. If the
disable_categorical_integer_offset_correction is true, a +1 offset might
be applied before calling the inference code. This attribute should be
disabled when creating manually a model with categorical integer features.
Ultimately, Yggdrasil Decision Forests will support the value 0 as a
normal value and this parameter will be removed. If
disable_categorical_integer_offset_correction is false, this +1 offset
is never applied.
|
node_format
|
Yggdrasil Decision Forests node format for the saved model. If
not specified, uses the recommended format. The node format is visible in
the node summary. For models to be compatible with the open-source version
of TensorFlow Decision Forests and TensorFlow Serving, the node format
should be BLOB_SEQUENCE.
|
allow_slow_inference
|
If false, slow inference engines are not allowed. If
the model is only available with the slow engine, an error is raised. If
true, the fastest compatible inference engine (possibly the slow one) will
be used.
|
force_ydf_port
|
Socket port for YDF GRPC to use during distributed training
in addition to the TF GRPC. The chief and the workers should be able to
communicate thought this port. If not set, an available port is
automatically selected.
|
output_secondary_class_predictions
|
If true, in the case of a multi-task
model, the predictions of secondary tasks are exported in the model
predictions. If false, the model only outputs the primary tasks
predictions.
|