tabensemb.model.AbstractNN._forward#
method
- AbstractNN._forward(x: Tensor, derived_tensors: Dict[str, Tensor]) Tensor[source]#
The real forward method.
- Parameters:
- x
A tensor that contains continuous features.
- derived_tensors
It mostly has the same structure as the derived unstacked data
derived_datastored in atabensemb.data.datamodule.DataModule. If some models are required (defined inAbstractModel.required_models()), there will be a key named “data_required_models” containing the data batch, the output, and possibly the hidden representation of the required models.
- Returns:
- torch.Tensor
The output of the model.
Notes
For classification tasks, DO NOT turn logits into probabilities here because we have already implemented this later. See also
output_norm().