# 导入torchmetrics中的Accuracy类 from torchmetrics import Accuracy Accuracy就是torchmetrics中用于计算分类任务准确率的类。主要参数如下:
torchmetrics的api接口类型有两种;一是MODULE,二是FUNCTIONAL。torchmetrics的api接口覆盖6类指标的计算,分别是分类、回归、检索、图像、文本、音频。同时也支持自定义指标的计算。 2.1.2 简单示例 下面是计算分类的accuracy、precision、recall、AUC的一个小栗子。 deftest_loop(dataloader,model,loss_fn):# 实例化相关...
可以使用sklearn.metrics.f1_score()函数来计算模型的精度。 三、举例 使用以下代码来评估 PyTorch 模型: # 禁用自动求导withtorch.no_grad():# 将模型设置为评估模式model.eval()# 使用模型对数据进行预测outputs=model(inputs)# 计算损失loss=criterion(outputs,labels)# 计算准确率accuracy=torch.nn.functional....
accuracy = torch.nn.functional.accuracy(outputs, labels) # 计算精度、召回率和 F1 值 precision = sklearn.metrics.precision_score(labels, outputs) recall = sklearn.metrics.recall_score(labels, outputs) f1 = sklearn.metrics.f1_score(labels, outputs) # 输出指标值 print("Loss:", loss.item())...
我们训练完一个分类模型后,会在测试(验证)集检验模型的性能,涉及到一些模型的评估指标。如:准确率(Accuracy)、混淆矩阵(confusion matrix)、F1-score、ROC曲线、PR曲线等。 我以Softmax回归二分类模型为例,展示一下如何展示模型性能的评估指标,并稍作解读。
accuracy = (targets_valid == preds).sum() / targets_valid.size(0) * 100.0 print( f"Accuracy: {accuracy:0.2f}%") ___ Accuracy: 81.50% 还可以查看每个类别预测的准确性…… class_acc = {} for c in range(5): target_idxs = (targets_valid == c) class...
def accuracy(output, target): pred = output.argmax(dim=1, keepdim=True) correct = pred.eq(target.view_as(pred)).sum() acc = correct.float() / target.size(0) return acc 复制代码 计算精确度、召回率和F1分数: from sklearn.metrics import precision_score, recall_score, f1_score def ...
accuracy = metrics.accuracy_score(val_targets, val_preds)f1_score_micro = metrics.f1_score(val_targets, val_preds, average='micro')f1_score_macro = metrics.f1_score(val_targets, val_preds, average='macro')print(f"Accuracy Score = {accuracy}")print(f"F1 Score (Micro) = {f1_score_...
accuracy:0.3174 precision:0.3174 recall:0.3174 f1:0.3174 [sklearn_metrics] Epoch:4 loss:2.1915 accuracy:0.5561 precision:0.5561 recall:0.5561 f1:0.5561 [sklearn_metrics] Epoch:5 loss:2.1438 accuracy:0.6881 precision:0.6881 recall:0.6881 f1:0.6881 [sklearn_metrics] Epoch:6 loss:2.0875 accuracy:...
from locale import normalize import numpy as np import random import torch from sklearn.datasets import load_breast_cancer from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score # batch_size表示单批次用于参数估计的样本个数 # y_pred大小为(batch_size, 1) ...