import tensorflow as tf

            truthoutput_z_ = lrelu(linear(tgtimg_z, self.gf_dim*8*s_h16*s_w16, 'd_h0_lin'))
            truthoutput_h0 = tf.reshape(truthoutput_z_, [-1, s_h16, s_w16, self.gf_dim * 8])
            truthoutput_h1 = lrelu(deconv2d(tf.concat([truthoutput_h0, tgtctx_h3], 3),
                    [self.batch_size, s_h8, s_w8, self.gf_dim*4], name='d_h1'))
            truthoutput_h2 = lrelu(deconv2d(tf.concat([truthoutput_h1, tgtctx_h2], 3),
                [self.batch_size, s_h4, s_w4, self.gf_dim*2], name='d_h2'))
            truthoutput_h3 = lrelu(deconv2d(tf.concat([truthoutput_h2, tgtctx_h1], 3),
                [self.batch_size, s_h2, s_w2, self.gf_dim*1], name='d_h3'))
            truthoutput_h4 = deconv2d(tf.concat([truthoutput_h3, tgtctx_h0], 3),
                [self.batch_size, s_h, s_w, self.c_dim], name='d_h4')

        self.simloss = tf.reduce_mean((trans_z - tgtimg_z) ** 2) * 1e3
        mean, var = tf.nn.moments(tgtimg_z, axes=[0])
        print(var.get_shape())
#         self.simloss /= tf.reduce_mean(var)
        print(tgtimg_z.get_shape())
