From a2a1028f47200d2d816b270d6e7a7cbbb80e85b5 Mon Sep 17 00:00:00 2001 From: wangchunlin Date: Sun, 30 Jun 2024 17:07:45 +0800 Subject: [PATCH] =?UTF-8?q?=E5=9B=BE=E7=89=87=E6=89=93=E5=8D=B0=E5=8A=A0?= =?UTF-8?q?=E4=B8=8A=E4=B8=AD=E6=96=87?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- utils/common.py | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/utils/common.py b/utils/common.py index 526219c..e09ea3e 100644 --- a/utils/common.py +++ b/utils/common.py @@ -92,11 +92,11 @@ class MLModel: fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(15, 5)) ax1.bar(np.arange(len(precision)), precision) - ax1.set_title('Precision') + ax1.set_title('Precision(精确率)') ax2.bar(np.arange(len(recall)), recall) - ax2.set_title('Recall') + ax2.set_title('Recall(召回率)') ax3.bar(np.arange(len(f1)), f1) - ax3.set_title('F1 Score') + ax3.set_title('F1 Score(F1得分)') # 保存图片 parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "..")) evaluate_result_path = os.path.join(parent_dir, self.config['evaluate_result_path']) @@ -179,18 +179,18 @@ class MLModel: # 打印训练和验证过程的可视化图片 plt.close('all') fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(15, 5)) - ax1.plot(train_loss_history, label='Train Loss') - ax1.plot(val_loss_history, label='Validation Loss') + ax1.plot(train_loss_history, label='Train Loss(训练损失)') + ax1.plot(val_loss_history, label='Validation Loss(验证损失)') ax1.set_title('Loss') ax1.legend() - ax2.plot(train_acc_history, label='Train Accuracy') - ax2.plot(val_acc_history, label='Validation Accuracy') + ax2.plot(train_acc_history, label='Train Accuracy(训练正确率)') + ax2.plot(val_acc_history, label='Validation Accuracy(验证正确率)') ax2.set_title('Accuracy') ax2.legend() - ax3.plot(val_f1_history, label='Validation F1') - ax3.plot(val_precision_history, label='Validation Precision') - ax3.plot(val_recall_history, label='Validation Recall') - ax3.set_title('Precision Recall F1-Score (Macro Mean)') + ax3.plot(val_f1_history, label='Validation F1(验证F1得分)') + ax3.plot(val_precision_history, label='Validation Precision(验证精确率)') + ax3.plot(val_recall_history, label='Validation Recall(验证召回率)') + ax3.set_title('Precision Recall F1-Score (Macro Mean)(宏平均)') ax3.legend() # 保存图片 parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))