计算F1、准确率(Accuracy)、召回率(Recall)、精确率(Precision)、敏感性(Sensitivity)、特异性(Specificity)需要用到的包(PS:还有一些如AUC等后面再加上用法。) fromsklearn.metricsimportprecision_recall_curve,average_precision_score,roc_curve,auc,precision_score,recall_score,f1_score,confusion_matrix,accuracy_...
"accuracy": Accuracy(task="multiclass", num_classes=1000), "precision": Precision(task="multiclass", num_classes=1000), "recall": Recall(task="multiclass", num_classes=1000), "f1_score": F1Score(task="multiclass", num_classes=1000), } ## Move all metrics to the device ## 将所有...
"accuracy": Accuracy(task="multiclass", num_classes=1000), "precision": Precision(task="multiclass", num_classes=1000), "recall": Recall(task="multiclass", num_classes=1000), "f1_score": F1Score(task="multiclass", num_classes=1000), } # Move all metrics to the device # 将所有指标...
# 切换以启用/禁用指标收集capture_metrics=Falseifcapture_metrics: metrics= {"avg_loss": MeanMetric(),"accuracy": Accuracy(task="multiclass",num_classes=1000),"precision": Precision(task="multiclass",num_classes=1000),"recall": Recall(task="multiclass",num_classes=1000),"f1_score": F1Score...
pytorch 语义分割的TorchMetrics MultiClass准确性看起来你遇到了一个定义上的问题。多类分类的准确性，（...
"accuracy": Accuracy(task="multiclass", num_classes=1000), "precision": Precision(task="multiclass", num_classes=1000), "recall": Recall(task="multiclass", num_classes=1000), "f1_score": F1Score(task="multiclass", num_classes=1000), ...
"accuracy": Accuracy(task="multiclass", num_classes=1000), "precision": Precision(task="multiclass", num_classes=1000), "recall": Recall(task="multiclass", num_classes=1000), "f1_score": F1Score(task="multiclass", num_classes=1000), ...
def pixelwise_accuracy(num_classes, output_transform=lambda x: x, device=None): """Calculates class accuracy Args: num_classes (int): number of classes output_transform (callable, optional): a callable that is used to transform the output into the form expected by the metric. Returns: Metr...
)self.criterion=nn.CrossEntropyLoss()self.accuracy_score=torchmetrics.Accuracy(task="multiclass",num_classes=10)defforward(self,x):returnself.model(x)defcommon_step(self,x,y,stage):logits=self.model(x)loss=self.criterion(logits,y)acc=self.accuracy_score(logits,y)self.log(f"{stage}_loss"...
def create_pytorch_multiclass_classifier(X, y): # Get unique number of classes numClasses = np.unique(y).shape[0] # create simple (dummy) Pytorch DNN model for multiclass classification epochs = 12 torch_X = torch.Tensor(X).float() torch_y = torch.Tensor(y).long() # Create network...