trainer.py 文件源码

def build_model(self):
        Gen=GeneratorTypes[self.gan_type]
        config=self.config
        self.gen=Gen(config.batch_size,config.gen_hidden_size,config.gen_z_dim)

        with tf.variable_scope('Disc') as scope:
            self.D1 = Discriminator(self.data.X, config.disc_hidden_size)
            scope.reuse_variables()
            self.D2 = Discriminator(self.gen.X, config.disc_hidden_size)
            d_var = tf.contrib.framework.get_variables(scope)

        d_loss_real=tf.reduce_mean( sxe(self.D1,1) )
        d_loss_fake=tf.reduce_mean( sxe(self.D2,0) )
        self.loss_d =  d_loss_real  +  d_loss_fake
        self.loss_g = tf.reduce_mean( sxe(self.D2,1) )

        optimizer=tf.train.AdamOptimizer
        g_optimizer=optimizer(self.config.lr_gen)
        d_optimizer=optimizer(self.config.lr_disc)
        self.opt_d = d_optimizer.minimize(self.loss_d,var_list= d_var)
        self.opt_g = g_optimizer.minimize(self.loss_g,var_list= self.gen.tr_var,
                               global_step=self.gen.step)

        with tf.control_dependencies([self.inc_step]):
            self.train_op=tf.group(self.opt_d,self.opt_g)