gluonts.mx.model.san package#
- class gluonts.mx.model.san.SelfAttentionEstimator(freq: str, prediction_length: int, cardinalities: List[int] = [], context_length: Optional[int] = None, trainer: gluonts.mx.trainer._base.Trainer = gluonts.mx.trainer._base.Trainer(add_default_callbacks=True, callbacks=None, clip_gradient=10.0, ctx=None, epochs=100, hybridize=True, init='xavier', learning_rate=0.001, num_batches_per_epoch=50, weight_decay=1e-08), model_dim: int = 64, ffn_dim_multiplier: int = 2, num_heads: int = 4, num_layers: int = 3, num_outputs: int = 3, kernel_sizes: List[int] = [3, 5, 7, 9], distance_encoding: Optional[str] = 'dot', pre_layer_norm: bool = False, dropout: float = 0.1, temperature: float = 1.0, time_features: Optional[List[Callable[[pandas.core.indexes.period.PeriodIndex], numpy.ndarray]]] = None, use_feat_dynamic_real: bool = True, use_feat_dynamic_cat: bool = False, use_feat_static_real: bool = False, use_feat_static_cat: bool = True, train_sampler: Optional[gluonts.transform.sampler.InstanceSampler] = None, validation_sampler: Optional[gluonts.transform.sampler.InstanceSampler] = None, batch_size: int = 32)[source]#
Bases:
gluonts.mx.model.estimator.GluonEstimator- create_predictor(transformation: gluonts.transform._base.Transformation, trained_network: mxnet.gluon.block.HybridBlock) gluonts.mx.model.predictor.RepresentableBlockPredictor[source]#
Create and return a predictor object.
- Parameters
transformation – Transformation to be applied to data before it goes into the model.
module – A trained HybridBlock object.
- Returns
A predictor wrapping a HybridBlock used for inference.
- Return type
- create_training_data_loader(data: gluonts.dataset.Dataset, **kwargs) Iterable[Dict[str, Any]][source]#
Create a data loader for training purposes.
- Parameters
data – Dataset from which to create the data loader.
- Returns
The data loader, i.e. and iterable over batches of data.
- Return type
DataLoader
- create_training_network() gluonts.mx.model.san._network.SelfAttentionTrainingNetwork[source]#
Create and return the network used for training (i.e., computing the loss).
- Returns
The network that computes the loss given input data.
- Return type
HybridBlock
- create_transformation() gluonts.transform._base.Transformation[source]#
Create and return the transformation needed for training and inference.
- Returns
The transformation that will be applied entry-wise to datasets, at training and inference time.
- Return type
- create_validation_data_loader(data: gluonts.dataset.Dataset, **kwargs) Iterable[Dict[str, Any]][source]#
Create a data loader for validation purposes.
- Parameters
data – Dataset from which to create the data loader.
- Returns
The data loader, i.e. and iterable over batches of data.
- Return type
DataLoader
- lead_time: int#
- prediction_length: int#