Tensorflow更改输出图像大小与训练输入大小不同

我正在尝试从GitHub修改tensorflow项目，以便我可以使用64x64图像进行训练，然后创建更大的输出图像，例如384x384输出图像 . 当我更改输出图像大小（通过命令行）时，我收到此错误：

尺寸必须相等，但对于'Mul'（op：'Mul'），输入形状为384和64：[？，384,384,3]，[21,64,64,3] .

它与代码中的这一行有关：

self.contextual_loss = tf.reduce_sum(
        tf.contrib.layers.flatten(
            tf.abs(tf.mul(self.mask, self.G) - tf.mul(self.mask, self.images))), 1)

我已经尝试将tf.mul（）更改为tf.matmul（）并仍然得到类似的错误 . 我也试过tf.reshape（）给了我一个不同的错误 . 谢谢你的帮助 .

def build_model(self):
    self.images = tf.placeholder(
        tf.float32, [None] + self.image_shape, name='real_images')

    self.sample_images= tf.placeholder(
        tf.float32, [None] + self.image_shape, name='sample_images')
    self.z = tf.placeholder(tf.float32, [None, self.z_dim], name='z')
    self.z_sum = tf.histogram_summary("z", self.z)

    self.G = self.generator(self.z)
    self.D, self.D_logits = self.discriminator(self.images)

    self.sampler = self.sampler(self.z)
    self.D_, self.D_logits_ = self.discriminator(self.G, reuse=False)

    self.d_sum = tf.histogram_summary("d", self.D)
    self.d__sum = tf.histogram_summary("d_", self.D_)
    self.G_sum = tf.image_summary("G", self.G)

    self.d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(self.D_logits,
                                                tf.ones_like(self.D)))
    self.d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(self.D_logits_,
                                                tf.zeros_like(self.D_)))
    self.g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(self.D_logits_,
                                                tf.ones_like(self.D_)))

    self.d_loss_real_sum = tf.scalar_summary("d_loss_real", self.d_loss_real)
    self.d_loss_fake_sum = tf.scalar_summary("d_loss_fake", self.d_loss_fake)

    self.d_loss = self.d_loss_real + self.d_loss_fake

    self.g_loss_sum = tf.scalar_summary("g_loss", self.g_loss)
    self.d_loss_sum = tf.scalar_summary("d_loss", self.d_loss)

    t_vars = tf.trainable_variables()

    self.d_vars = [var for var in t_vars if 'd_' in var.name]
    self.g_vars = [var for var in t_vars if 'g_' in var.name]

    self.saver = tf.train.Saver(max_to_keep=1)

    # Completion.
    self.mask = tf.placeholder(tf.float32, [None] + self.image_shape, name='mask')
    self.contextual_loss = tf.reduce_sum(
        tf.contrib.layers.flatten(
            tf.abs(tf.mul(self.mask, self.G) - tf.mul(self.mask, self.images))), 1)
    self.perceptual_loss = self.g_loss
    self.complete_loss = self.contextual_loss + self.lam*self.perceptual_loss
    self.grad_complete_loss = tf.gradients(self.complete_loss, self.z)