How to check a trained neural network

I am writing a little bit about googles deepdream. It's possible to check with deepdream learned networks, see research blog google the examplbe with the dumbbells.
In the example a network is trained to recognize a dumbbell. Then they use deepdream to see what the network has learned and the result is the network was trained bad. Because it recognize a dumbbell plus an arm as a dumbbell.

My question is, how will networks check in practice? With deepdream or which other method?

Best greetings

Generally in machine learning you validate your learned network on a dataset you did not use in the training process (a test set). So in this case, you would have a set of examples with and without dumbbells that was used to train the model, as well as a set (also consisting of dumbbells and without) that were not seen during the training procedure.

When you have your model, you let it predict the labels of the withheld set. You then compare these predicted labels to the actual ones:

• Every time you predict a dumbbell correctly, you increment the amount of True Positives,
• in case it correctly predicts the absence of a dumbbell, you increment the amount of True Negatives
• when it predicted a dumbbell, but it should not be one, increment the amount of False Positives
• Finally if it predicted no dumbbell, but there is one, you increment the amount of False Negatives

Based on these four, you can then calculate measures such as F1 score or accuracy to calculate the performance of the model. (Have a look at the following wiki: https://en.wikipedia.org/wiki/F1_score )