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For optimizing an unsupervised neural network with 1 hidden layer, I use the training set for training and the validation set for optimizing the number of neurons in the hidden layer (for example by running a grid search of many options and comparing the resulting errors each architecture returns). Having obtained the optimal architecture, how do I approach the final evaluation step?

1) do I train the optimal model on the training set alone, followed by evaluation on the test set

2) do I train the optimal model on the training AND validation set combined, followed by evaluation on the test set

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Since you tuned your model, you don’t need to (and you shouldn’t) separate the validation set because it means you are throwing your data away. Moreover, consider you do cross-validation, which fold(s) would you choose to ignore and what is your training set really?

Note: How do you optimize and calculate errors in an unsupervised problem?

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  • $\begingroup$ Thanks, your first part is what I was looking for. Regardless of supervised/unsupervised settings, I was more generally interested in whether the validation set is at the end combined with the training set to jointly train the final model. $\endgroup$ Jan 23, 2019 at 8:49

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