predict_uncertain_helper
- predict_uncertain_helper(model, batch, score_method, num_samples, slice_size=None)[source]
Predict with uncertainty estimates via Monte-Carlo dropout.
- Parameters
model (
Model
) – the model used for predicting scoresbatch (
LongTensor
) – the batch on which to predict. Its shape and content has to match what the score_method requires.score_method (
Callable
[…,FloatTensor
]) – the base score method to use (from score_{hrt,h,r,t})num_samples (
int
) – >1 The number of samples to use. More samples lead to better estimates, but increase memory requirements and runtime.slice_size (
Optional
[int
]) – >0 The divisor for the scoring function when using slicing.
- Return type
- Returns
A tuple (score_mean, score_std) of the mean and std of the scores sampled from the dropout distribution. The std may be interpreted as a measure of uncertainty.
- Raises
MissingDropoutError – if the model does not contain dropout layers.
Warning
This function sets the model to evaluation mode and all dropout layers to training mode.