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Builds datasets from feature specs with a single label spec.
Inherits From: BaseDatasetBuilder
, AbstractDatasetBuilder
tfr.keras.pipeline.SimpleDatasetBuilder(
context_feature_spec: Dict[str, Union[tf.io.FixedLenFeature, tf.io.VarLenFeature, tf.io.
RaggedFeature]],
example_feature_spec: Dict[str, Union[tf.io.FixedLenFeature, tf.io.VarLenFeature, tf.io.
RaggedFeature]],
mask_feature_name: str,
label_spec: Tuple[str, tf.io.FixedLenFeature],
hparams: tfr.keras.pipeline.DatasetHparams
,
sample_weight_spec: Optional[Tuple[str, tf.io.FixedLenFeature]] = None
)
This supports a single dataset with a single label, which is supposed to be a dense Tensor.
Example usage:
context_feature_spec = {}
example_feature_spec = {
"example_feature_1": tf.io.FixedLenFeature(
shape=(1,), dtype=tf.float32, default_value=0.0)
}
mask_feature_name = "list_mask"
label_spec = {
"utility": tf.io.FixedLenFeature(
shape=(1,), dtype=tf.float32, default_value=0.0)
}
dataset_hparams = DatasetHparams(
train_input_pattern="train.dat",
valid_input_pattern="valid.dat",
train_batch_size=128,
valid_batch_size=128)
dataset_builder = SimpleDatasetBuilder(
context_feature_spec,
example_feature_spec,
mask_feature_name,
label_spec,
dataset_hparams)
Args | |
---|---|
context_feature_spec
|
Maps context (aka, query) names to feature specs. |
example_feature_spec
|
Maps example (aka, document) names to feature specs. |
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.
|
label_spec
|
A tuple of the label name and a tf.io.FixedLenFeature spec, or a dict that maps task name to label spec in multi-task setting. |
hparams
|
A dict containing model hyperparameters. |
sample_weight_spec
|
Feature spec for per-example weight. |
Methods
build_signatures
build_signatures(
model: tf.keras.Model
) -> Any
See AbstractDatasetBuilder
.
build_train_dataset
build_train_dataset() -> tf.data.Dataset
See AbstractDatasetBuilder
.
build_valid_dataset
build_valid_dataset() -> tf.data.Dataset
See AbstractDatasetBuilder
.