Applied Natural Language Processing with Python by Taweh Beysolow II

Author:Taweh Beysolow II
Language: eng
Format: epub
ISBN: 9781484237335
Publisher: Apress

In these lines, we are performing weight regularization, as discussed earlier in this chapter with the logistic regression L2 and L1 loss parameters. Those of you who wish to apply this in TensorFlow can rest assured that these are the only modifications needed to add a weight penalty to your neural network. While developing this solution, I tried weight regularization utilizing L1 and L2 loss penalties, and I experimented with dropout. Weight regularization is the process of limiting the scope to which the weights can grow when utilizing different vector norms. The two most referenced norms for weight regularization are L1 and L2 norms. The following are their respective equations, which are also illustrated in Figure 3-5.

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