CrossEntropy#
- class pytorch_forecasting.metrics.point.CrossEntropy(reduction: str = 'mean', **kwargs)[source]#
Bases:
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.
to_quantiles
(y_pred[, quantiles])Convert network prediction into a quantile prediction.
- loss(y_pred, target)[source]#
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: Tensor) Tensor [source]#
Convert network prediction into a point prediction.
Returns best label
- Parameters:
y_pred – prediction output of network
- Returns:
point prediction
- Return type:
torch.Tensor
- to_quantiles(y_pred: Tensor, quantiles: List[float] | None = None) Tensor [source]#
Convert network prediction into a quantile prediction.
- Parameters:
y_pred – prediction output of network
quantiles (List[float], optional) – quantiles for probability range. Defaults to quantiles as as defined in the class initialization.
- Returns:
prediction quantiles
- Return type:
torch.Tensor