损失，准确性，验证损失，验证准确性之间有什么区别? [英] What is the difference between Loss, accuracy, validation loss, Validation accuracy?
问题描述
在每个时代结束时，我得到例如以下输出:
At the end of each epoch, I am getting for example the following output:
Epoch 1/25
2018-08-06 14:54:12.555511:
2/2 [==============================] - 86s 43s/step - loss: 6.0767 - acc: 0.0469 - val_loss: 4.1037 - val_acc: 0.2000
Epoch 2/25
2/2 [==============================] - 26s 13s/step - loss: 3.6901 - acc: 0.0938 - val_loss: 2.5610 - val_acc: 0.0000e+00
Epoch 3/25
2/2 [==============================] - 66s 33s/step - loss: 3.1491 - acc: 0.1406 - val_loss: 2.4793 - val_acc: 0.0500
Epoch 4/25
2/2 [==============================] - 44s 22s/step - loss: 3.0686 - acc: 0.0694 - val_loss: 2.3159 - val_acc: 0.0500
Epoch 5/25
2/2 [==============================] - 62s 31s/step - loss: 2.5884 - acc: 0.1094 - val_loss: 2.4601 - val_acc: 0.1500
Epoch 6/25
2/2 [==============================] - 41s 20s/step - loss: 2.7708 - acc: 0.1493 - val_loss: 2.2542 - val_acc: 0.4000
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有人可以解释我，损失，准确性，验证损失和验证准确性之间有什么区别吗?
Can anyone explain me what's the difference between loss, accuracy, validation loss and validation accuracy?
推荐答案
当我们在拟合DL模型时提到validation_split作为拟合参数时，它针对每个时期将数据分为两部分，即训练数据和验证数据. 它通过训练数据对模型进行训练，并通过检查模型的损失和准确性来对模型进行验证.
When we mention validation_split as fit parameter while fitting DL model, it splits data into two parts for every epoch i.e. training data and validation data. It trains the model on training data and validate the model on validation data by checking its loss and accuracy.
通常，随着每个时期的增加，损耗会降低，而精度会更高.但是使用val_loss和val_acc，可能会出现许多情况:
Usually with every epoch increasing, loss goes lower and accuracy goes higher. But with val_loss and val_acc, many cases can be possible:
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val_loss开始增加，val_acc开始减少(意味着模型填满了无法学习的值)
val_loss starts increasing, val_acc starts decreasing(means model is cramming values not learning)
val_loss开始增加，val_acc也增加.(在输出层中使用softmax的情况下，可能是过度拟合或概率值不同)
val_loss starts increasing, val_acc also increases.(could be case of overfitting or diverse probability values in cases softmax is used in output layer)
val_loss开始减少，val_acc开始增加(正确，意味着模型构建正在学习并且可以正常工作)
val_loss starts decreasing, val_acc starts increasing(Correct, means model build is learning and working fine)
这也是一个引用的链接，其中提供了更多描述.谢谢. 如何解释损失"和准确性"机器学习模型
This is a link to refer as well in which there is more description given. Thanks. How to interpret "loss" and "accuracy" for a machine learning model
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