在连续预测时仅在Keras中实例化一次VGG模型？

有许多在线教程指导如何使用预训练模型在Keras中预测一次图像 . 对于我的情况，我在Keras中使用VGG16模型，我需要连续预测图像，所以我使用for循环来加载图像，然后将其传递给预测函数，它运行良好，但是一个预测时间太长（我的约800ms）机器，仅限CPU），这里是代码：

# one full prediction function cost 800ms
    def predict(image):
        image = img_to_array(image)
        image = image / 255
        image = np.expand_dims(image, axis=0)
        # build the VGG16 network, this line code cost 400~500ms
        model = keras.applications.VGG16(include_top=True, weights='imagenet')
        # Do predication
        prediction = model.predict(image)
        '''
        Process predication results
        '''

    '''
    some preprocess
    '''
    for img in imgs_list:
        predict(img)

上面的代码可以很好地工作，但每次预测都花费太多时间，整个功能需要800ms，而构建VGG网络需要500ms，成本太高 . 我想为连续预测模式的每个预测删除这500ms .

我尝试将“model = keras.applications.VGG16（include_top = True，weights ='imagenet'）”此行代码放到预测函数的外部，全局定义或传递“model”作为函数的参数，但程序将返回错误并在第一次成功预测后结束 .

Traceback (most recent call last):
  File "/home/zi/venv/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1075, in _run
    subfeed, allow_tensor=True, allow_operation=False)
  File "/home/zi/venv/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 3590, in as_graph_element
    return self._as_graph_element_locked(obj, allow_tensor, allow_operation)
  File "/home/zi/venv/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 3669, in _as_graph_element_locked
    raise ValueError("Tensor %s is not an element of this graph." % obj)
ValueError: Tensor Tensor("input_1:0", shape=(?, ?, ?, 3), dtype=float32) is not an element of this graph.
During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "multi_classifier.py", line 256, in 
    predict(current_file_path)
  File "multi_classifier.py", line 184, in predict
    bottleneck_prediction = model_1.predict(image)
  File "/home/zi/venv/lib/python3.5/site-packages/keras/engine/training.py", line 1835, in predict
    verbose=verbose, steps=steps)
  File "/home/zi/venv/lib/python3.5/site-packages/keras/engine/training.py", line 1331, in _predict_loop
    batch_outs = f(ins_batch)
  File "/home/zi/venv/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py", line 2482, in __call__
    **self.session_kwargs)
  File "/home/zi/venv/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 900, in run
    run_metadata_ptr)
  File "/home/zi/venv/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1078, in _run
    'Cannot interpret feed_dict key as Tensor: ' + e.args[0])
TypeError: Cannot interpret feed_dict key as Tensor: Tensor Tensor("input_1:0", shape=(?, ?, ?, 3), dtype=float32) is not an element of this graph.

看起来我需要为每个预测实例化一个VGG模型，如何更改代码以节省模型构建时间？谢谢 .