October 2017
Intermediate to advanced
330 pages
7h 7m
English
Content preview from Python Deep Learning Cookbook
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Freezing layers
Sometimes (for example, when using pretrained networks), it is desirable to freeze some of the layers. We can do this when we're sure that some of the layers most of the time the first couple of layers, also known as the bottom of the network have proven to be of value as feature extractors. In the following recipe, we will demonstrate how to freeze a part of the network after training and only train the remaining subset of the network.
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