network.py 文件源码

def __build_graph(self):
        self.plhold_img, self.plhold_greylevel, self.plhold_latent, self.plhold_is_training, \
        self.plhold_keep_prob, self.plhold_kl_weight, self.plhold_lossweights \
          = self.model.inputs()

        #inference graph
        self.op_mean, self.op_stddev, self.op_vae, \
        self.op_mean_test, self.op_stddev_test, self.op_vae_test, \
        self.op_vae_condinference \
            = self.model.inference(self.plhold_img, self.plhold_greylevel, \
                self.plhold_latent, self.plhold_is_training, self.plhold_keep_prob)

        #loss function and gd step for vae
        self.loss = self.model.loss(self.plhold_img, self.op_vae, self.op_mean, \
            self.op_stddev, self.plhold_kl_weight, self.plhold_lossweights)
        self.train_step = self.model.optimize(self.loss, epsilon=1e-6)

        #standard steps
        self.check_nan_op = tf.add_check_numerics_ops()
        self.init = tf.global_variables_initializer()
        self.saver = tf.train.Saver(max_to_keep=0)
        self.summary_op = tf.summary.merge_all()