In the previous sections of this chapter, we have learned how to train and use auto-encoder models. This last section explores how to optimize and fine-tune an auto-encoder model, examining issues such as how to pick the number of hidden neurons or the number of layers.
Sometimes, there may be conceptual reasons to assume certain structures about the data. However, if there are not, we may vary the values of these parameters to obtain the best model. One dilemma that is exacerbated when trying several models and choosing the best one is that, even if several models are equivalent, by chance in a given sample one may outperform the others. To combat this, we can use techniques such as cross-validation during training in order to optimize the parameter values while only using the training data, and then only this final model needs to be validated using the holdout or testing data. Currently, H2O does not support cross-validation for auto-encoder models. If we...