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Thank you for the explanation on the kernel size. I have been experimenting with the sample Generative Adversarial Network (GAN) code from the book on Deep learning with Python by François Chollet, Section 8.5.3 Page 308. I noticed that when I happen to increase the kernel size in the keras.layers.Conv2D function, the output of the generator model degrades while decreasing the kernel size improves the performance of the generator. Could you help me in better understanding this?

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