April 2020
Intermediate to advanced
330 pages
7h 44m
English
Content preview from Hands-On Deep Learning with R
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Training and evaluating the model
Now that the data is in the proper format and we have our discriminator and generator defined, we can put it all together to train our GAN. The final GAN model takes input from our target image dataset and the output is the probability that this is a real image after the real image data and the fake image data have been passed as input to the discriminator. We train our GAN model by running the following sections.
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