4 train loss 趋于不变,test loss 不断下降,说明数据集100%有问题 5 train loss 不断上升,test loss 不断上升(最终变为NaN),可能是网络结构设计不当,训练超参数设置不当,程序bug等某个问题引起
可能是验证集和训练集的分布不一致，或者验证集里面有些样本噪声很大。可以尝试用二分法找一下验证集里面...
loss不断下降,说明网络仍然在学习。trainloss不断下降,testloss趋于不变,说明网络过拟合。trainloss趋于不变,testloss区域不变,说明学习曲线遇到瓶颈,需减小学习速率或批量数据尺寸。trainloss趋于不变,testloss不断下降,说明数据集100%有问题。trainloss不断上升,testloss不断上升(最终变为NaN ...
train loss 不断下降,test loss趋于不变,说明网络过拟合; train loss 趋于不变,test loss不断下降,说明数据集100%有问题; train loss 趋于不变,test loss趋于不变,说明学习遇到瓶颈,需要减小学习率或批量数目; train loss 不断上升,test loss不断上升,说明网络结构设计不当,训练超参数设置不当,数据集经过清洗...
train loss 趋于不变,test loss趋于不变,说明学习遇到瓶颈,需要减小学习率或批量数目; train loss 不断上升,test loss不断上升,说明网络结构设计不当,训练超参数设置不当,数据集经过清洗等问题。 二, 这个比较长,比较完整 Loss和神经网络训练 https://blog.csdn.net/u011534057/article/details/51452564 ...
有进度条,每次模型保存也会输出这个train loss 和 test loss 请问,我训练的类别是3类,原数据量是1573,做了四个方向的数据增强后,数据量为六千多,但是训练到后面,train loss和test loss基本趋向于稳定,大概是12-15,有什么方法把loss下降,是数据量不够还是为什么?
Search before asking I have searched the YOLOv8 issues and discussions and found no similar questions. Question all loss is NAN and P/R/map is 0 when the user-defined data set GPU is trained! CUDA Change from 11.7 to 11.6 still can't tra...
断点续训：模型训练到一般突然爆显存了，loss突然nan了，需要从之前保存的权重接着训练 半精度训练：半...
I see that you are not able to train your UNET model due to Nan loss. There are a few troubleshooting methods you can try: Try to fiddle around with the learning rate on a smaller dataset to ensure if this is or isnt the root cause. If the image size is huge, you may need a bi...
For classification, use cross-entropy loss. Get net = trainnet(imdsTrain,layers,"crossentropy",options); Test the network using the labeled test set. Extract the image data and labels from the test datastore. Get XTest = readall(imdsTest); TTest = imdsTest.Labels; classNames = ...