Keras:验证错误是衡量终止标准或验证准确性的好方法吗? [英] Keras: Validation error is a good measure for stopping criteria or validation accuracy?

问题描述

我正在使用Keras进行多类分类问题.我将EarlyStopping(monitor='val_loss', patience=4)用作学习的停止标准，也就是说，如果4个纪元的验证损失没有减少，则训练停止.最好使用val_acc作为停止条件或val_loss?因为我看到val_loss增加了，但是val_acc也增加了.考虑以下第8和第13阶段的输出.

I am using Keras for multi-class classification problem. I am using EarlyStopping(monitor='val_loss', patience=4) as stopping criteria for learning, that is if validation loss does not decrease for 4 epochs, training stops. Is it better to use val_acc as stopping criteria or val_loss? Because I see val_loss increasing but also val_acc. Considering following output of Epoch 8 and Epoch 13.

Epoch 1/200
240703/240703 [==============================] - 4831s - loss: 0.8581 - acc: 0.7603 - val_loss: 0.6247 - val_acc: 0.8160
Epoch 2/200
240703/240703 [==============================] - 4855s - loss: 0.6099 - acc: 0.8166 - val_loss: 0.5742 - val_acc: 0.8300
Epoch 3/200
240703/240703 [==============================] - 4627s - loss: 0.5573 - acc: 0.8308 - val_loss: 0.5600 - val_acc: 0.8337
Epoch 4/200
240703/240703 [==============================] - 4624s - loss: 0.5265 - acc: 0.8395 - val_loss: 0.5550 - val_acc: 0.8347
Epoch 5/200
240703/240703 [==============================] - 4623s - loss: 0.5042 - acc: 0.8452 - val_loss: 0.5529 - val_acc: 0.8377
Epoch 6/200
240703/240703 [==============================] - 4624s - loss: 0.4879 - acc: 0.8507 - val_loss: 0.5521 - val_acc: 0.8378
Epoch 7/200
240703/240703 [==============================] - 4625s - loss: 0.4726 - acc: 0.8555 - val_loss: 0.5554 - val_acc: 0.8383
Epoch 8/200
240703/240703 [==============================] - 4621s - loss: 0.4604 - acc: 0.8585 - val_loss: 0.5513 - val_acc: 0.8383
Epoch 9/200
240703/240703 [==============================] - 4716s - loss: 0.4508 - acc: 0.8606 - val_loss: 0.5649 - val_acc: 0.8366
Epoch 10/200
240703/240703 [==============================] - 4602s - loss: 0.4409 - acc: 0.8637 - val_loss: 0.5626 - val_acc: 0.8389
Epoch 11/200
240703/240703 [==============================] - 4651s - loss: 0.4318 - acc: 0.8662 - val_loss: 0.5710 - val_acc: 0.8387
Epoch 12/200
240703/240703 [==============================] - 4706s - loss: 0.4239 - acc: 0.8687 - val_loss: 0.5737 - val_acc: 0.8384
Epoch 13/200
240703/240703 [==============================] - 4706s - loss: 0.4190 - acc: 0.8698 - val_loss: 0.5730 - val_acc: 0.8391

推荐答案

通常，损失比准确度更好，因为它具有更高的精确度.准确性与验证集中的样本数量一样多.另一方面，损失具有连续的可能值，因此您可以更好地跟踪发生的情况.另一方面，由于准确度是可以解释的(仅是一个百分比)，因此更易于分析，因此，如果没有该领域的专业知识，基于损失的判据将更难使用，但可能会稍微更精确(如果正确使用).

In general loss is a better measure than accuracy because it has higher precision. Accuracy has as many possible values as many samples you have in the validation set. Loss, on the other hand has continuum possible values so you can track what is happening with better precision. On the other hand, accuracy is easier to analyse since it is interpretable (it is just a percentage), so without an expertise in the field - loss based criterion will be harder to use, but probably slightly more precise (if used correctly).

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