Accuracy of Train before sampling: 91.50705920980629
时间: 2024-05-22 13:10:01 浏览: 18
我理解你的问题是询问一个关于训练准确率的数据,具体来说是 "Accuracy of Train before sampling: 91.50705920980629"。这句话的意思是在进行采样之前,训练集的准确率为91.50705920980629%。需要注意的是,准确率是衡量模型性能的一项指标,它表示模型正确分类的样本数占总样本数的比例。
相关问题
1 accuracy: 100.0000000000% 2 accuracy: 100.0000000000% 3 accuracy: 98.1927710843% 4 accuracy: 98.1927710843% 5 accuracy: 91.2650602410% 6 accuracy: 92.4698795181% 7 accuracy: 87.0481927711% 8 accuracy: 88.8554216867% 9 accuracy: 86.1445783133% 10 accuracy: 88.8554216867% 11 accuracy: 87.9518072289% 12 accuracy: 89.1566265060% 13 accuracy: 87.3493975904% 14 accuracy: 88.5542168675% 15 accuracy: 86.1445783133% 16 accuracy: 87.9518072289% 17 accuracy: 84.3373493976% 18 accuracy: 84.6385542169% 19 accuracy: 82.2289156627% 20 accuracy: 84.6385542169%中的k值和准确率哪个更好
在这个数据集中,k值为2和3时准确率都达到了100%,因此这两个k值的效果都非常好。但是,k值过小容易导致过拟合,而k值过大则容易导致欠拟合。因此,需要根据具体的应用场景和数据集特征来选择合适的k值。如果需要更高的准确率并且数据集较大,则可以选择k值较小的2或3;如果需要更稳定的结果,则可以选择k值较大的12或13。所以,需要根据实际情况进行权衡和选择。
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
你好,根据你提供的信息,我可以帮助你写一个 Python 代码来可视化训练结果。首先,你需要读入这个 txt 文件,并将其转换为 Python 可以处理的数据结构,例如列表或字典。接下来,你可以使用 Python 的可视化库,例如 Matplotlib 或 Seaborn,来绘制出训练和测试的精度曲线,并将其保存到文件中。
下面是一个示例代码,你可以根据自己的需要进行修改:
```python
import re
import matplotlib.pyplot as plt
# 读取 txt 文件
with open('log.txt', 'r') as f:
log_text = f.read()
# 使用正则表达式提取训练和测试的精度数据
train_accuracies = []
test_accuracies = []
class_accuracies = []
for match in re.finditer(r'Train Instance Accuracy: (\d+\.\d+).*Test Instance Accuracy: (\d+\.\d+).*Class Accuracy: (\d+\.\d+)', log_text):
train_accuracies.append(float(match.group(1)))
test_accuracies.append(float(match.group(2)))
class_accuracies.append(float(match.group(3)))
# 绘制训练和测试精度曲线
plt.plot(train_accuracies, label='Train Accuracy')
plt.plot(test_accuracies, label='Test Accuracy')
plt.plot(class_accuracies, label='Class Accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend()
# 保存图片
plt.savefig('accuracies.png')
```
希望这个示例代码能帮助你。如果你有任何疑问,请随时追问。
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