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tfr.keras.pipeline.BaseDatasetBuilder

Builds datasets from feature specs.

Inherits From: AbstractDatasetBuilder

The BaseDatasetBuilder class is an abstract class inherit from AbstractDatasetBuilder to serve training and validation datasets and signatures for training ModelFitPipeline.

To be implemented by subclasses:

  • _features_and_labels(): Contains the logic to map a dict of tensors of dataset to feature tensors and label tensors.

Example subclass implementation:

class SimpleDatasetBuilder(BaseDatasetBuilder):

  def _features_and_labels(self, features):
    label = features.pop("utility")
    return features, label

context_feature_spec Maps context (aka, query) names to feature specs.
example_feature_spec Maps example (aka, document) names to feature specs.
training_only_example_spec Feature specs used for training only like labels and per-example weights.
mask_feature_name If set, populates the feature dictionary with this name and the coresponding value is a tf.bool Tensor of shape [batch_size, list_size] indicating the actual example is padded or not.
hparams A dict containing model hyperparameters.
training_only_context_spec Feature specs used for training only per-list weights.

Methods

build_signatures

View source

See AbstractDatasetBuilder.

build_train_dataset

View source

See AbstractDatasetBuilder.

build_valid_dataset

View source

See AbstractDatasetBuilder.