本文探讨了深度学习网络的调试过程，强调了试错在寻找最佳网络配置中的重要性。DL4J提供了监听器设施，如ScoreIterationListener和HistogramIterationListener，用于监控网络性能并提供调整参数所需的数据。通过理解和应用这些工具，可以更有效地优化神经网络的表现。
Troubleshooting a Neural Net Model
Building neural networks to solve problems is an empirical process. That is, it requires trial and error. So you will have to try different settings and architectures in order to find a neural net configuration that performs well.
DL4J provides a listener facility help you monitor your network’s performance visually. You can set up listeners for your model that will be called after each mini-batch is processed. One of most often used listeners that DL4J ships out of the box is ScoreIterationListener. Check out all Listeners for more.
While ScoreIterationListener will simply print the current error score for your network, HistogramIterationListener will start up a web UI that to provide you with a host of different information that you can use to fine tune your network configuration. See Visualize, Monitor and Debug Network Learningon how to interpret that data.
See Troubleshooting neural nets for more information on how to improve results.
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