batched_inception_time

torch_uncertainty.models.batched_inception_time(in_channels, num_classes, num_estimators, kernel_size=40, embed_dim=32, num_blocks=6, dropout=0.0, residual=True, repeat_strategy='paper')

BatchEnsemble of InceptionTime.

Parameters:

• in_channels (int) – Number of input channels.

• num_classes (int) – Number of output classes.

• num_estimators (int) – Number of estimators for BatchEnsemble.

• kernel_size (int) – Size of the convolutional kernels. Default is 40.

• embed_dim (int) – Dimension of the embedding. Default is 32.

• num_blocks (int) – Number of inception blocks. Default is 6.

• dropout (float) – Dropout rate. Default is 0.0.

• residual (bool) – Whether to use residual connections. Default is True.

• repeat_strategy ("legacy"|"paper", optional) –

  The repeat strategy to use during training:

  • ”legacy”: Repeat inputs for each estimator during both training and evaluation.

  • ”paper”(default): Repeat inputs for each estimator only during evaluation.

Returns:

An instance of the InceptionTime model.

Return type:

_BatchedInceptionTime