The training set data and test data were input into model for training and simulation;

将训练集数据和测试集数据先后输入网络模型进行学习训练和仿真测试；

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This gives you a much larger training set for each trial, meaning that your algorithm will have enough data to learn from, but it also gives a fairly large number of tests (20 instead of 5 or 10).

对每次尝试来说，训练集都非常大，这意味着你的算法有足够的数据进行学习，而且这样一来也提供了足够多的测试次数(20次，而不是5次或10次)。

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Nevertheless, if you're in a bind for data, this can yield passable results with lower variance than simply using one test set and one training set.

尽管如此，比起只是简单的使用一个测试集和一个训练集，这种方法可以产生比较低的差异，还算是一个可以说得过去的结果。

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