Finally, let's go ahead and kick off the training process after putting it all together:
class GAN: def __init__(self, real_size, z_size, learning_rate, num_classes=10, alpha=0.2, beta1=0.5): tf.reset_default_graph() self.learning_rate = tf.Variable(learning_rate, trainable=False) model_inputs = inputs(real_size, z_size) self.input_actual, self.input_latent_z, self.target, self.label_mask = model_inputs self.drop_out_rate = tf.placeholder_with_default(.5, (), "drop_out_rate") losses_results = model_losses(self.input_actual, self.input_latent_z, real_size[2], self.target, num_classes, label_mask=self.label_mask, leaky_alpha=0.2, drop_out_rate=self.drop_out_rate) self.disc_loss, self.gen_loss, self.correct, self.masked_correct ...
