C-RNN_GAN is a recurrent neural network with adversarial training. The adversaries are two different deep recurrent neural models, a generator (G) and a discriminator (D). The generator is trained to generate data that is indistinguishable from real data, while the discriminator is trained to identify the generated data. The training becomes a zero-sum game for which the Nash equilibrium is when the generator produces data that the discriminator cannot tell from real data. We define the following loss functions LD and LG:
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