CrossEntropy

class pytorch_forecasting.metrics.CrossEntropy(reduction: str = 'mean', **kwargs)

Bases: pytorch_forecasting.metrics.MultiHorizonMetric

Cross entropy loss for classification.

Initialize metric

Parameters

• name (str) – metric name. Defaults to class name.

• quantiles (List[float], optional) – quantiles for probability range. Defaults to None.

• reduction (str, optional) – Reduction, “none”, “mean” or “sqrt-mean”. Defaults to “mean”.

Methods

loss(y_pred, target)	Calculate loss without reduction.
to_prediction(y_pred)	Convert network prediction into a point prediction.

loss(y_pred, target)

Calculate loss without reduction. Override in derived classes

Parameters

• y_pred – network output

• y_actual – actual values

Returns

loss/metric as a single number for backpropagation

Return type

torch.Tensor

to_prediction(y_pred: torch.Tensor) → torch.Tensor

Convert network prediction into a point prediction.

Returns best label

Parameters

y_pred – prediction output of network

Returns

point prediction

Return type

torch.Tensor

CompositeMetric DistributionLoss