trainer.py 文件源码

def compute_losses(self, images, wrong_images, fake_images, embeddings):
        real_logit = self.model.get_discriminator(images, embeddings)
        wrong_logit = self.model.get_discriminator(wrong_images, embeddings)
        fake_logit = self.model.get_discriminator(fake_images, embeddings)

        real_d_loss =\
            tf.nn.sigmoid_cross_entropy_with_logits(real_logit,
                                                    tf.ones_like(real_logit))
        real_d_loss = tf.reduce_mean(real_d_loss)
        wrong_d_loss =\
            tf.nn.sigmoid_cross_entropy_with_logits(wrong_logit,
                                                    tf.zeros_like(wrong_logit))
        wrong_d_loss = tf.reduce_mean(wrong_d_loss)
        fake_d_loss =\
            tf.nn.sigmoid_cross_entropy_with_logits(fake_logit,
                                                    tf.zeros_like(fake_logit))
        fake_d_loss = tf.reduce_mean(fake_d_loss)
        if cfg.TRAIN.B_WRONG:
            discriminator_loss =\
                real_d_loss + (wrong_d_loss + fake_d_loss) / 2.
            self.log_vars.append(("d_loss_wrong", wrong_d_loss))
        else:
            discriminator_loss = real_d_loss + fake_d_loss
        self.log_vars.append(("d_loss_real", real_d_loss))
        self.log_vars.append(("d_loss_fake", fake_d_loss))

        generator_loss = \
            tf.nn.sigmoid_cross_entropy_with_logits(fake_logit,
                                                    tf.ones_like(fake_logit))
        generator_loss = tf.reduce_mean(generator_loss)

        return discriminator_loss, generator_loss