我正在构建一个小的Flask应用程序，该应用程序在后台使用卷积神经网络对用户上传的图像进行预测。如果我这样加载它，它将起作用：

@app.route("/uploader", methods=["GET","POST"])
def get_image():
    if request.method == 'POST':
        f = request.files['file']
        sfname = 'static/'+str(secure_filename(f.filename))
        f.save(sfname)
        clf = catdog.classifier()
        return render_template('result.html', pred = clf.predict(sfname), imgpath = sfname)

但是，这要求在用户添加图像之后加载分类器（clf）。这需要一段时间，因为它需要根据一个pickle文件为200层以上的神经网络设置所有权重。

我想要做的是在生成应用程序时加载所有权重。为此，我尝试了此操作（为HTML模板/导入/应用启动切出了不相关的代码）：

# put model into memory on spawn
clf = catdog.classifier()
# Initialize the app
app = flask.Flask(__name__)

@app.route("/uploader", methods=["GET","POST"])
def get_image():
    if request.method == 'POST':
        f = request.files['file']
        sfname = 'static/'+str(secure_filename(f.filename))
        f.save(sfname)
        return render_template('result.html', pred = clf.predict(sfname), imgpath = sfname)

当我这样做时，我得到了这个回溯（跳过所有烧瓶特定的跟踪在顶部）：

 File "/Users/zachariahmiller/Documents/Metis/test_area/flask_catdog/flask_backend.py", line 26, in get_image
    return render_template('result.html', pred = clf.predict(sfname), imgpath = sfname)
  File "/Users/zachariahmiller/Documents/Metis/test_area/flask_catdog/catdog.py", line 56, in predict
    prediction = self.model.predict(img_to_predict, batch_size=1, verbose=1)
  File "/Users/zachariahmiller/anaconda/lib/python2.7/site-packages/keras/engine/training.py", line 1569, in predict
    self._make_predict_function()
  File "/Users/zachariahmiller/anaconda/lib/python2.7/site-packages/keras/engine/training.py", line 1037, in _make_predict_function
    **kwargs)
  File "/Users/zachariahmiller/anaconda/lib/python2.7/site-packages/keras/backend/tensorflow_backend.py", line 2095, in function
    return Function(inputs, outputs, updates=updates)
  File "/Users/zachariahmiller/anaconda/lib/python2.7/site-packages/keras/backend/tensorflow_backend.py", line 2049, in __init__
    with tf.control_dependencies(self.outputs):
  File "/Users/zachariahmiller/anaconda/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 3583, in control_dependencies
    return get_default_graph().control_dependencies(control_inputs)
  File "/Users/zachariahmiller/anaconda/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 3314, in control_dependencies
    c = self.as_graph_element(c)
  File "/Users/zachariahmiller/anaconda/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2405, in as_graph_element
    return self._as_graph_element_locked(obj, allow_tensor, allow_operation)
  File "/Users/zachariahmiller/anaconda/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2484, in _as_graph_element_locked
    raise ValueError("Tensor %s is not an element of this graph." % obj)
ValueError: Tensor Tensor("dense_2/Softmax:0", shape=(?, 2), dtype=float32) is not an element of this graph.

我不确定为什么将特定调用之外的分类器作为全局对象加载到应用程序会导致失败。它应该在内存中，并且我已经看到其他人使用SKLearn分类器执行此操作的示例。关于为什么会导致此错误的任何想法？