神经网络中的Early Stop

神经网络中的Early Stop

神经网络中具体的做法如下：
1. 首先将训练数据划分为训练集和验证集（划分比例为2:1）；
2. 在训练集上进行训练，并且在验证集上获取测试结果（比如每隔５个epoch测试一下），随着epoch的增加，如果在验证集上发现测试误差上升，则停止训练；
3. 将停止之后的权重作为网络的最终参数。
注：Early Stop能够防止过拟合。

1. Split the training data into a training set and a validation set, e.g. in a 2-to-1 proportion.
2. Train only on the training set and evaluate the per-example error on the validation set once in a while, e.g. after every fifth epoch.
3. Stop training as soon as the error on the validation set is higher than it was the last time it was checked.
4. Use the weights the network had in that previous step as the result of the training run.

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