Task-specific Metrics 描述:针对不同任务的指标,如分类、回归、推荐系统等。示例: torchmetrics.classification: 包含分类任务的指标,如 Accuracy, Precision, Recall, F1Score 等。 torchmetrics.regression: 包含回归任务的指标,如 MeanSquaredError, MeanAbsoluteError, R2Score 等。 torchmetrics.retrieval: 包含推荐...
importtorch# import our libraryimporttorchmetrics# initialize metricmetric=torchmetrics.classification.Accuracy(task="multiclass",num_classes=5)# move the metric to device you want computations to take placedevice="cuda"iftorch.cuda.is_available()else"cpu"metric.to(device)n_batches=10foriinrange(n...
Other metrics such as accuracy and specifity also accept zero_division because of inheritance, but complain when it is set to NaN. I think that the ideal situation would be for absent and ignored classes to be assigned a NaN score or dropped, but this may not be that straightforward to ...
Next we’ll modify our training and validation loops to log theF1 scoreandArea Under the Receiver Operator Characteristic Curve (AUROC)as well as accuracy. We’ll remove the (deprecated) accuracy frompytorch_lightning.metricsand the similar sklearn function from thevalidation_epoch_endcallback in ...
importtorch# import our libraryimporttorchmetrics# initialize metricmetric=torchmetrics.classification.Accuracy(task="multiclass",num_classes=5)# move the metric to device you want computations to take placedevice="cuda"iftorch.cuda.is_available()else"cpu"metric.to(device)n_batches=10foriinrange(n...