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_score(labels, prediction) f1 = f1_score(labels, prediction, average='weighted', zero_division=0) precision = precision_score(labels, prediction, average='weighted', zero_division=0) recall = recall_score(labels, prediction, average='weighted', zero_division=0) if loss: re...
accuracy_score(train_y, final_pred) # get validation accuracy x, y = Variable(torch.from_numpy(preproc(val_x))), Variable(torch.from_numpy(val_y), requires_grad=False) pred = model(x) final_pred = np.argmax(pred.data.numpy(), axis=1) accuracy_score(val_y, final_pred) 训练分数...
"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...
importosimporttimeimportonnximporttorchimportnumpyasnpimportpandasaspdimporttorch.nnasnnimportonnxruntimeasortimporttorch.nn.functionalasFfromsklearn.metricsimportaccuracy_scorefromtorch.utils.dataimportDataset,DataLoader 2. 参数准备 N_EPOCH =1N_BATCH =128N_BATCH_NUM =250S_DATA_PATH =r"mnist_train.csv...
F1Score, ) # 切换以启用或禁用指标收集 capture_metrics = False if capture_metrics: metrics = { "avg_loss": MeanMetric(), "accuracy": Accuracy(task="多类别", num_classes=1000), "precision": Precision(task="多类别", num_classes=1000), ...
定义了一组来自 TorchMetrics 的标准指标,以及一个控制标志,用于启用或禁用指标计算。 fromtorchmetricsimport ( MeanMetric, Accuracy, Precision, Recall, F1Score, ) ## toggle to enable/disable metric collection ## 切换以启用/禁用指标收集 capture_metrics=False ...
F1Score, ) # toggle to enable/disable metric collection # 切换以启用/禁用指标收集 capture_metrics = False if capture_metrics: metrics = { "avg_loss": MeanMetric(), "accuracy": Accuracy(task="multiclass", num_classes=1000), "precision": Precision(task="multiclass", num_classes=1000), ...
使用Torchmetrics快速进行验证指标的计算 TorchMetrics可以为我们提供一种简单、干净、高效的方式来处理验证指标。TorchMetrics提供了许多现成的指标实现，如Accuracy, Dice, F1 Score, Recall, MAE等等，几乎最常见的指标都可以在里面找到。torchmetrics目前已经包好了80+任务评价指标。TorchMetrics安装也非常简单，只需要...
TorchMetrics提供了许多现成的指标实现,如Accuracy, Dice, F1 Score, Recall, MAE等等,几乎最常见的指标都可以在里面找到。torchmetrics目前已经包好了80+任务评价指标。 TorchMetrics安装也非常简单,只需要PyPI安装最新版本: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 pip install torchmetrics 基本流程介绍...