October 2018
Intermediate to advanced
368 pages
9h 20m
English
Content preview from Advanced Deep Learning with KerasBecome an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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GAN implementation in Keras
In the previous section, we learned that the principles behind GANs are straightforward. We also learned how GANs could be implemented by familiar network layers such as CNNs and RNNs. What differentiates GANs from other networks is they are notoriously difficult to train. Something as simple as a minor change in the layers can drive the network to training instability.
In this section, we'll examine one of the early successful implementations of GANs using deep CNNs. It is called DCGAN [3].
Figure 4.2.1 shows DCGAN that is used to generate fake MNIST images. DCGAN recommends the following design principles:
- Use of strides > 1 convolution instead of
MaxPooling2DorUpSampling2D. With strides > 1, the CNN learns how to ...
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