CrossEntropy¶
- class pytorch_forecasting.metrics.CrossEntropy(reduction: str = 'mean', **kwargs)[source]¶
Bases:
pytorch_forecasting.metrics.MultiHorizonMetricCross 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: torch.Tensor) torch.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: torch.Tensor, quantiles: Optional[List[float]] = None) torch.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