Thales Kitalpha Accuracy: 模型验证工具解析

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"Kitalpha-Accuracy-v0.2.pdf 是一份关于MBSE(Model-Based Systems Engineering,基于模型的系统工程)工具Capella的相关资料,重点介绍了名为'Accuracy'的验证工具及其应用。这份文档由Thales集团发布,强调了内容的保密性和版权保护。" 在MBSE领域,Capella是一款强大的开源工具,用于系统建模和系统工程的全过程管理。它基于OMG(Object Management Group)的SysML(Systems Modeling Language)标准,提供了一种图形化的方式来设计、分析和验证复杂的系统。 Accuracy是Capella配套的一个验证工具,旨在确保模型的正确性和准确性。文档首先会介绍Accuracy的基本概念,解释其是什么以及它在系统工程中的作用。Accuracy的核心在于帮助用户评估和验证模型的完整性、一致性和符合性,确保模型在设计阶段就尽可能接近实际需求。 Accuracy的工作原理可能涉及到以下几个方面: 1. **模型检查**:Accuracy通过一套规则和约束来检查模型,这些规则可以是用户自定义的,也可以是预定义的标准,以识别潜在的错误或不一致性。 2. **模拟与仿真**:Accuracy可能支持对模型进行动态仿真,以验证系统在各种条件下的行为是否符合预期。 3. **合规性评估**:工具可能会检查模型是否符合特定的设计规范、标准或者行业规定,以确保合规性。 4. **报告与可视化**:Accuracy将提供详细的报告,指出模型的问题所在,并可能通过图形化的界面帮助用户理解问题的来源。 5. **自动化验证**:Accuracy可能包含自动化工具,自动运行一系列测试用例,从而减轻手动验证的负担,提高效率。 在"Accuracy in practice"部分,文档可能会通过一个具体的例子展示如何使用Accuracy进行模型验证,包括设置验证规则、执行验证过程以及如何解读结果。这个实例可能涉及一个具体的系统工程案例,帮助读者更好地理解和应用Accuracy。 文档最后,可能会讨论在实际项目中整合Accuracy的方法,以及如何根据项目需求调整和扩展Accuracy的功能,以满足不同规模和复杂性的系统工程项目的验证需求。 "Kitalpha-Accuracy-v0.2.pdf"是系统工程师们深入理解和使用Capella及Accuracy的重要参考资料,对于提升模型质量、确保系统工程项目的成功至关重要。

60/60 [==============================] - 19s 89ms/step - loss: 229.5776 - accuracy: 0.7818 - val_loss: 75.8205 - val_accuracy: 0.2848 Epoch 2/50 60/60 [==============================] - 5s 78ms/step - loss: 59.5195 - accuracy: 0.8323 - val_loss: 52.4355 - val_accuracy: 0.7152 Epoch 3/50 60/60 [==============================] - 5s 77ms/step - loss: 47.9256 - accuracy: 0.8453 - val_loss: 47.9466 - val_accuracy: 0.2848 Epoch 4/50 60/60 [==============================] - 5s 77ms/step - loss: 41.7355 - accuracy: 0.8521 - val_loss: 37.7279 - val_accuracy: 0.2848 Epoch 5/50 60/60 [==============================] - 5s 76ms/step - loss: 40.1783 - accuracy: 0.8505 - val_loss: 40.2293 - val_accuracy: 0.7152 Epoch 6/50 60/60 [==============================] - 5s 76ms/step - loss: 37.8785 - accuracy: 0.8781 - val_loss: 38.5298 - val_accuracy: 0.2848 Epoch 7/50 60/60 [==============================] - 5s 77ms/step - loss: 37.1490 - accuracy: 0.8786 - val_loss: 37.1918 - val_accuracy: 0.2848 Epoch 8/50 60/60 [==============================] - 5s 78ms/step - loss: 34.6709 - accuracy: 0.9156 - val_loss: 34.0621 - val_accuracy: 0.2765 Epoch 9/50 60/60 [==============================] - 5s 76ms/step - loss: 35.7891 - accuracy: 0.8849 - val_loss: 37.8741 - val_accuracy: 0.7152 Epoch 10/50 60/60 [==============================] - 5s 76ms/step - loss: 34.5359 - accuracy: 0.9141 - val_loss: 35.2664 - val_accuracy: 0.7152 Epoch 11/50 60/60 [==============================] - 5s 76ms/step - loss: 34.6172 - accuracy: 0.9016 - val_loss: 34.5135 - val_accuracy: 0.6258 Epoch 12/50 60/60 [==============================] - 5s 76ms/step - loss: 34.2331 - accuracy: 0.9083 - val_loss: 34.0945 - val_accuracy: 0.9168 Epoch 13/50 60/60 [==============================] - 5s 79ms/step - loss: 37.4175 - accuracy: 0.9000 - val_loss: 37.7885 - val_accuracy: 0.7152 16/16 - 0s - loss: 34.0621 - accuracy: 0.2765 - 307ms/epoch - 19ms/step Test accuracy: 0.27650728821754456

2023-06-07 上传

2021-03-26 20:54:33,596 - Model - INFO - Epoch 1 (1/200): 2021-03-26 20:57:40,380 - Model - INFO - Train Instance Accuracy: 0.571037 2021-03-26 20:58:16,623 - Model - INFO - Test Instance Accuracy: 0.718528, Class Accuracy: 0.627357 2021-03-26 20:58:16,623 - Model - INFO - Best Instance Accuracy: 0.718528, Class Accuracy: 0.627357 2021-03-26 20:58:16,623 - Model - INFO - Save model... 2021-03-26 20:58:16,623 - Model - INFO - Saving at log/classification/pointnet2_msg_normals/checkpoints/best_model.pth 2021-03-26 20:58:16,698 - Model - INFO - Epoch 2 (2/200): 2021-03-26 21:01:26,685 - Model - INFO - Train Instance Accuracy: 0.727947 2021-03-26 21:02:03,642 - Model - INFO - Test Instance Accuracy: 0.790858, Class Accuracy: 0.702316 2021-03-26 21:02:03,642 - Model - INFO - Best Instance Accuracy: 0.790858, Class Accuracy: 0.702316 2021-03-26 21:02:03,642 - Model - INFO - Save model... 2021-03-26 21:02:03,643 - Model - INFO - Saving at log/classification/pointnet2_msg_normals/checkpoints/best_model.pth 2021-03-26 21:02:03,746 - Model - INFO - Epoch 3 (3/200): 2021-03-26 21:05:15,349 - Model - INFO - Train Instance Accuracy: 0.781606 2021-03-26 21:05:51,538 - Model - INFO - Test Instance Accuracy: 0.803641, Class Accuracy: 0.738575 2021-03-26 21:05:51,538 - Model - INFO - Best Instance Accuracy: 0.803641, Class Accuracy: 0.738575 2021-03-26 21:05:51,539 - Model - INFO - Save model... 2021-03-26 21:05:51,539 - Model - INFO - Saving at log/classification/pointnet2_msg_normals/checkpoints/best_model.pth 我有类似于这样的一段txt文件,请你帮我写一段代码来可视化这些训练结果

2023-02-06 上传