//  [python]
def build(self, rgb, train=False, num_classes=20, random_init_fc8=False,
              debug=False):
        """
        Build the VGG model using loaded weights
        Parameters
        ----------
        rgb: image batch tensor
            Image in rgb shap. Scaled to Intervall [0, 255]
        train: bool
            Whether to build train or inference graph
        num_classes: int
            How many classes should be predicted (by fc8)
        random_init_fc8 : bool
            Whether to initialize fc8 layer randomly.
            Finetuning is required in this case.
        debug: bool
            Whether to print additional Debug Information.
        """
        # Convert RGB to BGR

        with tf.name_scope('Processing'):
            # rgb = tf.image.convert_image_dtype(rgb, tf.float32)
            red, green, blue = tf.split(rgb, 3, 3)
            # assert red.get_shape().as_list()[1:] == [224, 224, 1]
            # assert green.get_shape().as_list()[1:] == [224, 224, 1]
            # assert blue.get_shape().as_list()[1:] == [224, 224, 1]
            bgr = tf.concat([
                blue - VGG_MEAN[0],
                green - VGG_MEAN[1],
                red - VGG_MEAN[2]], axis=3)

            if debug:
                bgr = tf.print(bgr, [tf.shape(bgr)],
                               message='Shape of input image: ',
                               summarize=4, first_n=1)

        self.conv1_1 = self._conv_layer(bgr, "conv1_1")
        self.conv1_2 = self._conv_layer(self.conv1_1, "conv1_2")
        self.pool1 = self._max_pool(self.conv1_2, 'pool1', debug)

        self.conv2_1 = self._conv_layer(self.pool1, "conv2_1")
        self.conv2_2 = self._conv_layer(self.conv2_1, "conv2_2")
        self.pool2 = self._max_pool(self.conv2_2, 'pool2', debug)

        self.conv3_1 = self._conv_layer(self.pool2, "conv3_1")
        self.conv3_2 = self._conv_layer(self.conv3_1, "conv3_2")
        self.conv3_3 = self._conv_layer(self.conv3_2, "conv3_3")
        self.pool3 = self._max_pool(self.conv3_3, 'pool3', debug)

        self.conv4_1 = self._conv_layer(self.pool3, "conv4_1")
        self.conv4_2 = self._conv_layer(self.conv4_1, "conv4_2")
        self.conv4_3 = self._conv_layer(self.conv4_2, "conv4_3")
        self.pool4 = self._max_pool(self.conv4_3, 'pool4', debug)

        self.conv5_1 = self._conv_layer(self.pool4, "conv5_1")
        self.conv5_2 = self._conv_layer(self.conv5_1, "conv5_2")

        self.conv5_3 = self._conv_layer(self.conv5_2, "conv5_3")
        self.pool5 = self._max_pool(self.conv5_3, 'pool5', debug)

        self.fc6 = self._fc_layer(self.pool5, "fc6")

        if train:
            self.fc6 = tf.nn.dropout(self.fc6, 0.5)

        self.fc7 = self._fc_layer(self.fc6, "fc7")
        if train:
            self.fc7 = tf.nn.dropout(self.fc7, 0.5)

        if random_init_fc8:
            self.score_fr = self._score_layer(self.fc7, "score_fr",
                                              num_classes)
        else:
            self.score_fr = self._fc_layer(self.fc7, "score_fr",
                                           num_classes=num_classes,
                                           relu=False)

        self.pred = tf.argmax(self.score_fr, dimension=3)

        self.upscore2 = self._upscore_layer(self.score_fr,
                                            shape=tf.shape(self.pool4),
                                            num_classes=num_classes,
                                            debug=debug, name='upscore2',
                                            ksize=4, stride=2)

        self.score_pool4 = self._score_layer(self.pool4, "score_pool4",
                                             num_classes=num_classes)

        self.fuse_pool4 = tf.add(self.upscore2, self.score_pool4)

        self.upscore32 = self._upscore_layer(self.fuse_pool4,
                                             shape=tf.shape(bgr),
                                             num_classes=num_classes,
                                             debug=debug, name='upscore32',
                                             ksize=32, stride=16)

        self.pred_up = tf.argmax(self.upscore32, dimension=3)