Java Deep Learning Cookbook

How it works...

We saw a sample fine-tuning implementation in step 1. Fine-tuning configurations are intended for default/global changes that are applicable across layers. So, if we want to remove specific layers from being considered for fine-tuning configuration, then we need to make those layers frozen. Unless we do that, all the current values for the specified modification type (gradients, activation, and so on) will be overridden in the new model.

All the fine-tuning configurations mentioned above will be applied to all unfrozen layers, including output layers. So, you might get errors due to the addition of the activation() and dropOut() methods. Dropouts are relevant to hidden layers and we may have a different value range for output ...