|View source on GitHub|
Advanced control of the model that most users won't need to use.
tfdf.keras.AdvancedArguments( infer_prediction_signature=True, yggdrasil_training_config=, yggdrasil_deployment_config=, fail_on_non_keras_compatible_feature_name=True )
||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 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.|
||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.|