Generative Adversarial Networks Projects

Training the adversarial network

To train the adversarial network, we need both input values and ground truth values. The input values to the network are batchA and batchB. The ground truth values are real_labels, real_labels, batchA, batchB, batchA, batchB, as follows:

        g_loss = adversarial_model.train_on_batch([batchA, batchB],                                          [real_labels, real_labels, batchA, batchB, batchA, batchB])

This step will train the generator network without training the generating networks.

After the completion of a single iteration (loop) over each mini-batch, store the losses in lists called dis_losses and gen_losses, as follows:

        dis_losses.append(d_loss)        gen_losses.append(g_loss)

After every 10 epochs, use the generator networks to generate a set of images: ...