GAN.py 文件源码

def _define_loss(self):
        """Define loss function that will be used to optimize model params"""

        # define generator loss
        with tf.variable_scope('generator'):
            self.loss_gen = tf.reduce_mean(
                tf.nn.sigmoid_cross_entropy_with_logits(
                    logits=self.disc_gen,
                    labels=tf.ones_like(self.disc_gen)))

        # define discriminator loss
        with tf.variable_scope('discriminator'):
            self.loss_disc = tf.reduce_mean(
                tf.nn.sigmoid_cross_entropy_with_logits(
                    logits=self.disc_real,
                    labels=tf.ones_like(self.disc_real)) +
                tf.nn.sigmoid_cross_entropy_with_logits(
                    logits=self.disc_gen,
                    labels=tf.zeros_like(self.disc_gen)))

        # save summaries of losses
        tf.summary.scalar('loss_gen', self.loss_gen)
        tf.summary.scalar('loss_disc', self.loss_disc)