如何解释损失和准确性的增加 [英] How to interpret increase in both loss and accuracy

问题描述

我已经使用张量流运行了深度学习模型（CNN）。在这个时期，我多次观察到损失和准确性都增加了，或者都减少了。我的理解是，两者总是成反比的。

解决方案

损失随着训练过程的进行而减少，除了一些波动之外

如果损失减少，则说明训练过程进展顺利。

（我想是验证）准确性，它是对模型预测的良好程度的度量。

如果模型正在学习，则准确性会提高。相反，如果模型过度拟合，则精度将停止增加，甚至可能开始降低。

如果损失减少且精度降低，则表明模型过度拟合。 / p>

如果损失增加并且准确性也增加，是因为您的正则化技术运行良好，并且您正在解决过度拟合的问题。仅当损失开始减少而精度继续增加时，这才是正确的。
否则，如果损失持续增长，您的模型就会出现分歧，您应该寻找原因（通常您使用的学习率值过高）。

I have run deep learning models(CNN's) using tensorflow. Many times during the epoch, i have observed that both loss and accuracy have increased, or both have decreased. My understanding was that both are always inversely related. What could be scenario where both increase or decrease simultaneously.

解决方案

The loss decreases as the training process goes on, except for some fluctuation introduced by the mini-batch gradient descent and/or regularization techniques like dropout (that introduces random noise).

If the loss decreases, the training process is going well.

The (validation I suppose) accuracy, instead, it's a measure of how good the predictions of your model are.

If the model is learning, the accuracy increases. If the model is overfitting, instead, the accuracy stops to increase and can even start to decrease.

If the loss decreases and the accuracy decreases, your model is overfitting.

If the loss increases and the accuracy increase too is because your regularization techniques are working well and you're fighting the overfitting problem. This is true only if the loss, then, starts to decrease whilst the accuracy continues to increase. Otherwise, if the loss keep growing your model is diverging and you should look for the cause (usually you're using a too high learning rate value).

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