恢复预先训练的模型

Question

我正在TensorFlow中恢复预先训练的模型，并想知道我的代码是否按预期工作。这是下面的代码。看起来它没有使用预训练的模型，因为来自下面的代码的损失高于训练误差，例如200个东西。请告诉我如何修改它。提前谢谢。

def main(_):

    with tf.Graph().as_default() as g:

        x, img, rows, cols = load_image(FLAGS.input, FLAGS.image_size)

        with slim.arg_scope(resnet_v2.resnet_arg_scope(weight_decay=0.0005)):
            logits, endpoints = resnet_v2.resnet_v2_152(x, nb_classes, is_training=False)

        predictions = tf.argmax(logits, 1)

        saver = tf.train.Saver()

    with tf.Session(graph=g) as sess:

        sess.run(tf.global_variables_initializer())

        # Load a pretrained model.
        print("\nLoading a model...")
        saver.restore(sess, checkpoint_path)

        print("\nFeedforwarding...")
        pred, loss_out, output, grads_val = sess.run([predictions, loss, conv_layer, norm_grads])