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@ -191,16 +191,16 @@ class MLModel:
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ax1.plot(train_loss_history, label='Train Loss(训练损失)')
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ax1.plot(train_loss_history, label='Train Loss(训练损失)')
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ax1.plot(val_loss_history, label='Validation Loss(验证损失)')
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ax1.plot(val_loss_history, label='Validation Loss(验证损失)')
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ax1.set_title('Loss(损失)', fontproperties=font_prop)
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ax1.set_title('Loss(损失)', fontproperties=font_prop)
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ax1.legend()
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ax1.legend(prop=font_prop)
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ax2.plot(train_acc_history, label='Train Accuracy(训练正确率)')
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ax2.plot(train_acc_history, label='Train Accuracy(训练正确率)')
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ax2.plot(val_acc_history, label='Validation Accuracy(验证正确率)')
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ax2.plot(val_acc_history, label='Validation Accuracy(验证正确率)')
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ax2.set_title('Accuracy(正确率)', fontproperties=font_prop)
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ax2.set_title('Accuracy(正确率)', fontproperties=font_prop)
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ax2.legend()
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ax2.legend(prop=font_prop)
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ax3.plot(val_f1_history, label='Validation F1(验证F1得分)')
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ax3.plot(val_f1_history, label='Validation F1(验证F1得分)')
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ax3.plot(val_precision_history, label='Validation Precision(验证精确率)')
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ax3.plot(val_precision_history, label='Validation Precision(验证精确率)')
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ax3.plot(val_recall_history, label='Validation Recall(验证召回率)')
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ax3.plot(val_recall_history, label='Validation Recall(验证召回率)')
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ax3.set_title('Precision Recall F1-Score (Macro Mean)(宏平均)', fontproperties=font_prop)
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ax3.set_title('Precision Recall F1-Score (Macro Mean)(宏平均)', fontproperties=font_prop)
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ax3.legend()
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ax3.legend(prop=font_prop)
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# 保存图片
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# 保存图片
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parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
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parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
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train_process_path = os.path.join(parent_dir, self.config['train_process_path'])
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train_process_path = os.path.join(parent_dir, self.config['train_process_path'])
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