Java Deep Learning Cookbook

How it works...

In step 1, we specify the default seed values, the initial default weights (weightInit), the weight updater, and so on. We set the gradient normalization strategy to ClipElementWiseAbsoluteValue. We have also set the gradient threshold to 0.5 as an input to the gradientNormalization strategy.

The neural network calculates the gradients across neurons at each layer. We normalized the input data earlier in the Normalizing training data recipe, using a normalizer. It makes sense to mention that we need to normalize the gradient values to achieve data preparation goals. As we can see in step 1, we have used ClipElementWiseAbsoluteValue gradient normalization. It works in such a way that the absolute value of the gradient cannot ...