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我在做 CNN 量化，我使用下面的代码来计算一些指标。

        FP = confusion_matrix.sum(axis=0) - np.diag(confusion_matrix) 
        FN = confusion_matrix.sum(axis=1)  - np.diag(confusion_matrix)
        TP = np.diag(confusion_matrix)
        TN = confusion_matrix.sum() - (FP + FN + TP)
        
        FP = torch.Tensor(FP)
        FN = torch.Tensor(FN)
        TP = torch.Tensor(TP)
        TN = torch.Tensor(TN)

        # Sensitivity, hit rate, recall, or true positive rate
        TPR = TP/(TP+FN)
        # Specificity or true negative rate
        TNR = TN/(TN+FP) 
        # Precision or positive predictive value
        PPV = TP/(TP+FP)
        # # Negative predictive value
        # NPV = TN/(TN+FN)
        # # Fall out or false positive rate
        # FPR = FP/(FP+TN)
        # # False negative rate
        # FNR = FN/(TP+FN)
        # # False discovery rate
        # FDR = FP/(TP+FP)
        F1 = 2 * (TPR * PPV) / (TPR+PPV)

        # Overall accuracy
        ACC = (TP+TN)/(TP+FP+FN+TN)
        # print('Sensitivity for each class',confusion_matrix.diag()/confusion_matrix.sum(1))
        print('Acc',ACC)
        print('Sensitivity',TPR)
        print('Specificity',TNR)
        print('Precision',PPV)
        print('F1 score',F1)

但我得到了这样的结果：

Test_Quantization Epoch #0: 100% 129/129 [00:02<00:00, 57.30it/s, Acc=0.0785, F1 score=0.1589, Loss=3.7637, Sensitivity(recall)=1.0000, Specificity=0.0000, precision=0.0870]
/content/drive/My Drive/ECG_quantization/trainer.py:219: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /pytorch/torch/csrc/utils/tensor_numpy.cpp:141.)
  TP = torch.Tensor(TP)
Acc tensor([0.1743, 0.9743, 0.9350, 0.9924, 0.0760])
Sensitivity tensor([0., 0., 0., 0., 1.])
Specificity tensor([1., 1., 1., 1., 0.])
Precision tensor([   nan,    nan,    nan,    nan, 0.0760])
F1 score tensor([   nan,    nan,    nan,    nan, 0.1412])
Normalized confusion matrix

我不知道为什么除了准确性之外的所有指标都如此奇怪。有谁知道这是怎么回事？

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