python - Retrain a pybrain neural network after adding to the dataset

Question

I have a pybrain NN up and running, and it seems to be working rather well. Ideally, I would like to train the network and obtain a prediction after each data point (the previous weeks figures, in this case) has been added to the dataset.

At the moment I'm doing this by rebuilding the network each time, but it takes an increasingly long time to train the network as each example is added (+2 minutes for each example, in a dataset of 1000s of examples).

Is there a way to speed up the process by adding the new example to an already trained NN and updating it, or am I overcomplicating the matter, and would be better served by training on a single set of examples (say last years data) and then testing on all of the new examples (this year)?

Answer 1

这取决于你的目标是什么。如果您需要更新的 NN 模型，您可以执行在线培训，即从您在时间 $t-1$ 获得的网络开始，使用在时间 $t$ 获取的样本执行单步反向传播。或者，也许您可​​以丢弃较旧的样本以获得固定数量的训练样本，或者您可以减少执行某种聚类的训练集的大小（即将相似的样本合并为一个样本）。

如果您更好地解释您的应用程序，那么建议解决方案会更简单。