如何判断哪种Keras模型更好? [英] How to tell which Keras model is better?

问题描述

我不知道该使用哪种输出精度来比较我的2个Keras模型以查看哪个更好.

I don't understand which accuracy in the output to use to compare my 2 Keras models to see which one is better.

我是使用"acc"(来自训练数据?)还是"val acc"(来自验证数据?)?

Do I use the "acc" (from the training data?) one or the "val acc" (from the validation data?) one?

每个时期都有不同的acc和val acc.我怎么知道整个模型的acc或val acc?我是否要对所有时期的acc或val acc求平均值，以找到整个模型的acc或val acc?

There are different accs and val accs for each epoch. How do I know the acc or val acc for my model as a whole? Do I average all of the epochs accs or val accs to find the acc or val acc of the model as a whole?

模型1输出

Train on 970 samples, validate on 243 samples
Epoch 1/20
0s - loss: 0.1708 - acc: 0.7990 - val_loss: 0.2143 - val_acc: 0.7325
Epoch 2/20
0s - loss: 0.1633 - acc: 0.8021 - val_loss: 0.2295 - val_acc: 0.7325
Epoch 3/20
0s - loss: 0.1657 - acc: 0.7938 - val_loss: 0.2243 - val_acc: 0.7737
Epoch 4/20
0s - loss: 0.1847 - acc: 0.7969 - val_loss: 0.2253 - val_acc: 0.7490
Epoch 5/20
0s - loss: 0.1771 - acc: 0.8062 - val_loss: 0.2402 - val_acc: 0.7407
Epoch 6/20
0s - loss: 0.1789 - acc: 0.8021 - val_loss: 0.2431 - val_acc: 0.7407
Epoch 7/20
0s - loss: 0.1789 - acc: 0.8031 - val_loss: 0.2227 - val_acc: 0.7778
Epoch 8/20
0s - loss: 0.1810 - acc: 0.8010 - val_loss: 0.2438 - val_acc: 0.7449
Epoch 9/20
0s - loss: 0.1711 - acc: 0.8134 - val_loss: 0.2365 - val_acc: 0.7490
Epoch 10/20
0s - loss: 0.1852 - acc: 0.7959 - val_loss: 0.2423 - val_acc: 0.7449
Epoch 11/20
0s - loss: 0.1889 - acc: 0.7866 - val_loss: 0.2523 - val_acc: 0.7366
Epoch 12/20
0s - loss: 0.1838 - acc: 0.8021 - val_loss: 0.2563 - val_acc: 0.7407
Epoch 13/20
0s - loss: 0.1835 - acc: 0.8041 - val_loss: 0.2560 - val_acc: 0.7325
Epoch 14/20
0s - loss: 0.1868 - acc: 0.8031 - val_loss: 0.2573 - val_acc: 0.7407
Epoch 15/20
0s - loss: 0.1829 - acc: 0.8072 - val_loss: 0.2581 - val_acc: 0.7407
Epoch 16/20
0s - loss: 0.1878 - acc: 0.8062 - val_loss: 0.2589 - val_acc: 0.7407
Epoch 17/20
0s - loss: 0.1833 - acc: 0.8072 - val_loss: 0.2613 - val_acc: 0.7366
Epoch 18/20
0s - loss: 0.1837 - acc: 0.8113 - val_loss: 0.2605 - val_acc: 0.7325
Epoch 19/20
0s - loss: 0.1906 - acc: 0.8010 - val_loss: 0.2555 - val_acc: 0.7407
Epoch 20/20
0s - loss: 0.1884 - acc: 0.8062 - val_loss: 0.2542 - val_acc: 0.7449

模型2输出

Train on 970 samples, validate on 243 samples
Epoch 1/20
0s - loss: 0.1735 - acc: 0.7876 - val_loss: 0.2386 - val_acc: 0.6667
Epoch 2/20
0s - loss: 0.1733 - acc: 0.7825 - val_loss: 0.1894 - val_acc: 0.7449
Epoch 3/20
0s - loss: 0.1781 - acc: 0.7856 - val_loss: 0.2028 - val_acc: 0.7407
Epoch 4/20
0s - loss: 0.1717 - acc: 0.8021 - val_loss: 0.2545 - val_acc: 0.7119
Epoch 5/20
0s - loss: 0.1757 - acc: 0.8052 - val_loss: 0.2252 - val_acc: 0.7202
Epoch 6/20
0s - loss: 0.1776 - acc: 0.8093 - val_loss: 0.2449 - val_acc: 0.7490
Epoch 7/20
0s - loss: 0.1833 - acc: 0.7897 - val_loss: 0.2272 - val_acc: 0.7572
Epoch 8/20
0s - loss: 0.1827 - acc: 0.7928 - val_loss: 0.2376 - val_acc: 0.7531
Epoch 9/20
0s - loss: 0.1795 - acc: 0.8062 - val_loss: 0.2445 - val_acc: 0.7490
Epoch 10/20
0s - loss: 0.1746 - acc: 0.8103 - val_loss: 0.2491 - val_acc: 0.7449
Epoch 11/20
0s - loss: 0.1831 - acc: 0.8082 - val_loss: 0.2477 - val_acc: 0.7449
Epoch 12/20
0s - loss: 0.1831 - acc: 0.8113 - val_loss: 0.2496 - val_acc: 0.7490
Epoch 13/20
0s - loss: 0.1920 - acc: 0.8000 - val_loss: 0.2459 - val_acc: 0.7449
Epoch 14/20
0s - loss: 0.1945 - acc: 0.7928 - val_loss: 0.2446 - val_acc: 0.7490
Epoch 15/20
0s - loss: 0.1852 - acc: 0.7990 - val_loss: 0.2459 - val_acc: 0.7449
Epoch 16/20
0s - loss: 0.1800 - acc: 0.8062 - val_loss: 0.2495 - val_acc: 0.7449
Epoch 17/20
0s - loss: 0.1891 - acc: 0.8000 - val_loss: 0.2469 - val_acc: 0.7449
Epoch 18/20
0s - loss: 0.1891 - acc: 0.8041 - val_loss: 0.2467 - val_acc: 0.7531
Epoch 19/20
0s - loss: 0.1853 - acc: 0.8072 - val_loss: 0.2511 - val_acc: 0.7449
Epoch 20/20
0s - loss: 0.1905 - acc: 0.8062 - val_loss: 0.2460 - val_acc: 0.7531

推荐答案

我是使用"acc"(来自训练数据?)还是"val acc"(来自验证数据?)?

Do I use the "acc" (from the training data?) one or the "val acc" (from the validation data?) one?

如果您要估计模型对新数据进行泛化的能力(这可能是您想要做的)，那么您将查看验证准确性，因为验证拆分仅包含模型在执行过程中从未看到的数据培训并因此不能仅仅记住.

If you want to estimate the ability of your model to generalize to new data (which is probably what you want to do), then you look at the validation accuracy, because the validation split contains only data that the model never sees during the training and therefor cannot just memorize.

如果您的训练数据准确性("acc")不断提高，而验证数据准确性("val_acc")却越来越差，那么您很可能在

If your training data accuracy ("acc") keeps improving while your validation data accuracy ("val_acc") gets worse, you are likely in an overfitting situation, i.e. your model starts to basically just memorize the data.

每个时期都有不同的acc和val acc.我怎么知道整个模型的acc或val acc?我是否要对所有时期的acc或val acc求平均值，以找到整个模型的acc或val acc?

There are different accs and val accs for each epoch. How do I know the acc or val acc for my model as a whole? Do I average all of the epochs accs or val accs to find the acc or val acc of the model as a whole?

每个纪元都是对您所有数据的一次训练.在运行期间，将根据损失函数调整模型的参数.结果是一组参数，这些参数具有一定的泛化为新数据的能力.验证准确性反映了该能力.因此，将每个纪元都视为自己的模型，如果针对另一个纪元进行训练，则该模型可能会变得更好或更坏.验证准确度的变化(更好=验证准确度提高)可以判断它的好坏.因此，请选择验证精度最高的时期模型.不要平均不同时期的精度，这没有多大意义.您可以使用Keras回调ModelCheckpoint自动以最高的验证准确性保存模型(请参见回调文档).

Each epoch is a training run over all of your data. During that run the parameters of your model are adjusted according to your loss function. The result is a set of parameters which have a certain ability to generalize to new data. That ability is reflected by the validation accuracy. So think of every epoch as its own model, which can get better or worse if it is trained for another epoch. Whether it got better or worse is judged by the change in validation accuracy (better = validation accuracy increased). Therefore pick the model of the epoch with the highest validation accuracy. Don't average the accuracies over different epochs, that wouldn't make much sense. You can use the Keras callback ModelCheckpoint to automatically save the model with the highest validation accuracy (see callbacks documentation).

模型1中的最高精度为0.7737，模型2中的最高精度为0.7572.因此，您应该更好地查看模型1(在时期3).尽管0.7737可能只是一个随机的异常值.

The highest accuracy in model 1 is 0.7737 and the highest one in model 2 is 0.7572. Therefore you should view model 1 (at epoch 3) as better. Though it is possible that the 0.7737 was just a random outlier.

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