Comments (19)
是测试集的结果吗?测试集没有ground truth,只能用验证集验证测试两用
from multi-label-sewer-classification.
是的是测试集结果,我使用Train13和Train13作为训练集和测试集,使用Xie2019模型,结果却如图,不应该是为0或者1才是对的嘛
from multi-label-sewer-classification.
模型的输出结果会经过sigmoid激活到0~1之间,0或者1是真值,模型好的预测只会很靠近0或者1,几乎不会为0或者1
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那怎样的结果才是模型好的预测呢
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可以看看博主的测试结果吗
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Multi-label-Sewer-Classification/metrics.py
Lines 124 to 126 in cd4bd6b
需要先用inference.py进行推理,然后用calculate_results.py计算推理结果,从而判断模型的好坏,metrics.py里计算指标的时候会把高于阈值的算为正样本,低于的算为负样本
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from multi-label-sewer-classification.
感谢博主分享,请问怎样在计算指标之前就用阈值将结果置为0或者1,还有就是阈值一般设置为多少
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Multi-label-Sewer-Classification/metrics.py
Lines 99 to 129 in cd4bd6b
第125行,Np[k]是第k类的Total number of Predictions,这里就是在取预测值大于阈值(一般为0.5)的结果
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感谢博主的回答,但我想像博主一样将[metrics.py]中的评估函数运用到inference.py中进行推理,需要怎样修改代码呢
from multi-label-sewer-classification.
运行inference.py就会得到推理结果,再运行calculate_results.py就能得到推理结果的指标,里面会调用metrics.py
from multi-label-sewer-classification.
会不会出现inference.py就会得到推理结果很差,但是运行calculate_results.py得到的结果也还行。我想知道calculate_results.py的具体计算公式,论文里有吗
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请问博主是使用了所有数据集进行训练吗
from multi-label-sewer-classification.
calculate_results.py是基于inference.py的结果计算的,calculate_results.py的计算公式都在metrics.py里,论文的附录里有
from multi-label-sewer-classification.
我是用的整个数据集训练的
from multi-label-sewer-classification.
请问博主试过只使用一个文件train13训练吗,我使用一个文件训练的结果好像不行
from multi-label-sewer-classification.
请问博主执行calculate_results.py时遇到这个报错是结果有问题吗E:\Multi-label-Sewer-Classification-main\metrics.py:173: RuntimeWarning: invalid value encountered in scalar divide
F2_normal = (5 * precision_k[-1] * recall_k[-1])/(4*precision_k[-1] + recall_k[-1])
from multi-label-sewer-classification.
这种看上去像除了0,可以试着print分母的precision和recall
from multi-label-sewer-classification.
train13是我从原始数据集中摘出来的子集,可能因为数量少导致效果不好
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Related Issues (10)
- 数据集问题 HOT 2
- # EMAIL HOT 1
- test questions HOT 1
- lear-gist-python HOT 3
- datasets HOT 3
- 复现问题 HOT 1
- 测试集标注问题 HOT 2
- 验证问题 HOT 13
- 加载模型的状态字典时报错 HOT 1
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