我正在构建一个小型的Flask应用程序,它在后台使用卷积神经网络对用户上传的图像进行预测。如果我像这样加载它,它就可以正常工作:
@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)
然而,这需要在用户添加图像后加载分类器(clf)。这需要一段时间,因为它需要从pickle文件中设置200多个层神经网络的所有权重。
我想要做的是在应用程序生成时加载所有权重。为此,我尝试了以下方法(剔除HTML模板/导入/应用程序启动的不相关代码):
# 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)
当我执行此操作时,我会得到以下回溯信息(跳过顶部所有特定于 Flask 的跟踪信息):
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.
我不确定为什么将分类器作为全局对象加载到应用程序之外会导致它失败。它应该在内存中,我已经看到其他人使用SKLearn分类器进行这样的操作的示例。有关此问题的任何想法?