So, let's try to better understand how the different parts of the GAN work together to generate synthetic data. Consider the parameterized function (G) (you know, the kind we usually approximate using a neural network). This will be our generator, which samples its input vectors (z) from some latent probability distribution, and transforms them into synthetic images. Our discriminator network (D), will then be presented with some synthetic images produced by our generator, mixed among real images, and attempt to classify real from forgery. Hence, our discriminator network is simply a binary classifier, equipped with something like a sigmoid activation function. Ideally, we want the discriminator to output high values when presented with real images, and low values when presented with generated fakes. Conversely, we want our generator network to try...
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You're reading from Hands-On Neural Networks with Keras
Niloy Purkait is a technology and strategy consultant by profession. He currently resides in the Netherlands, where he offers his consulting services to local and international companies alike. He specializes in integrated solutions involving artificial intelligence, and takes pride in navigating his clients through dynamic and disruptive business environments. He has a masters in Strategic Management from Tilburg University, and a full specialization in data science from Michigan University. He has advanced industry grade certifications from IBM, in subjects like signal processing, cloud computing, machine and deep learning. He is also perusing advanced academic degrees in several related fields, and is a self-proclaimed lifelong learner.
Read more about Niloy Purkait
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Niloy Purkait is a technology and strategy consultant by profession. He currently resides in the Netherlands, where he offers his consulting services to local and international companies alike. He specializes in integrated solutions involving artificial intelligence, and takes pride in navigating his clients through dynamic and disruptive business environments. He has a masters in Strategic Management from Tilburg University, and a full specialization in data science from Michigan University. He has advanced industry grade certifications from IBM, in subjects like signal processing, cloud computing, machine and deep learning. He is also perusing advanced academic degrees in several related fields, and is a self-proclaimed lifelong learner.
Read more about Niloy Purkait