当我在单独的文件中运行我的模型时，一切正常，但当我用Flask代码运行我的模型时，它会报错，我不知道为什么会遇到这个问题。我已经尝试了一些来自StackOverflow的解决方案，这些解决方案建议在加载模型和预测后分别添加以下几行代码

graph = tf.get_default_graph()

和

global graphwith graph.as_default():

但我仍然得到类似这样的错误 tensorflow.python.framework.errors_impl.FailedPreconditionError

这是我的Flask的app.py文件

# 导入机器学习库from keras.models import load_modelfrom time import sleepimport tensorflow as tffrom keras.preprocessing.image import img_to_arrayfrom keras.preprocessing import imageimport cv2import numpy as np# 机器学习初始化face_classifier = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')classifier =load_model('Emotion_little_vgg.h5')global graphgraph = tf.get_default_graph() class_labels = ['Angry','Happy','Neutral','Sad','Surprise']# 情感检测函数def get_emotion():    with graph.as_default():        cap = cv2.VideoCapture(0)        ret, frame = cap.read()        labels = []        gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)        faces = face_classifier.detectMultiScale(gray,1.3,5)        for (x,y,w,h) in faces:            # cv2.rectangle(frame,(x,y),(x+w,y+h),(255,0,0),2)            roi_gray = gray[y:y+h,x:x+w]            roi_gray = cv2.resize(roi_gray,(48,48),interpolation=cv2.INTER_AREA)        # rect,face,image = face_detector(frame)            if np.sum([roi_gray])!=0:                roi = roi_gray.astype('float')/255.0                roi = img_to_array(roi)                roi = np.expand_dims(roi,axis=0)                preds = classifier.predict(roi)[0]                label=class_labels[preds.argmax()]                labels.append(label)                print(label)                return label                # label_position = (x,y)                # cv2.putText(frame,label,label_position,cv2.FONT_HERSHEY_SIMPLEX,2,(0,255,0),3)            else:                # cv2.putText(frame,'No Face Found',(20,60),cv2.FONT_HERSHEY_SIMPLEX,2,(0,255,0),3)                label = 404                return label        # cv2.imshow('Emotion Detector',frame)# Flask初始化app = Flask(__name__)@app.route('/', methods=['POST','GET'])def index():    labels = get_emotion()    return labels[0]if __name__== "__main__":    app.run(debug=True)

这是我独立运行没有问题的单独机器学习文件，但与Flask结合使用时会出现问题

from keras.models import load_modelfrom time import sleepfrom keras.preprocessing.image import img_to_arrayfrom keras.preprocessing import imageimport cv2import numpy as npface_classifier = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')classifier =load_model('Emotion_little_vgg.h5')class_labels = ['Angry','Happy','Neutral','Sad','Surprise']cap = cv2.VideoCapture(0)while True:    # 抓取视频的一帧    ret, frame = cap.read()    labels = []    gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)    faces = face_classifier.detectMultiScale(gray,1.3,5)    for (x,y,w,h) in faces:        cv2.rectangle(frame,(x,y),(x+w,y+h),(255,0,0),2)        roi_gray = gray[y:y+h,x:x+w]        roi_gray = cv2.resize(roi_gray,(48,48),interpolation=cv2.INTER_AREA)    # rect,face,image = face_detector(frame)        if np.sum([roi_gray])!=0:            roi = roi_gray.astype('float')/255.0            roi = img_to_array(roi)            roi = np.expand_dims(roi,axis=0)        # 对ROI进行预测，然后查找类别            preds = classifier.predict(roi)[0]            label=class_labels[preds.argmax()]            label_position = (x,y)            print(label)            cv2.putText(frame,label,label_position,cv2.FONT_HERSHEY_SIMPLEX,2,(0,255,0),3)        else:            print("未找到面部")            cv2.putText(frame,'No Face Found',(20,60),cv2.FONT_HERSHEY_SIMPLEX,2,(0,255,0),3)    cv2.imshow('Emotion Detector',frame)    if cv2.waitKey(1) & 0xFF == ord('q'):        breakcap.release()cv2.destroyAllWindows()

这是我的完整错误消息

Traceback (most recent call last)File "C:\Users\MAULI\Miniconda3\envs\my_flask_env\lib\site-packages\flask\app.py", line 2463, in __call__return self.wsgi_app(environ, start_response)File "C:\Users\MAULI\Miniconda3\envs\my_flask_env\lib\site-packages\flask\app.py", line 2449, in wsgi_appresponse = self.handle_exception(e)File "C:\Users\MAULI\Miniconda3\envs\my_flask_env\lib\site-packages\flask\app.py", line 1866, in handle_exceptionreraise(exc_type, exc_value, tb)File "C:\Users\MAULI\Miniconda3\envs\my_flask_env\lib\site-packages\flask\_compat.py", line 39, in reraiseraise valueFile "C:\Users\MAULI\Miniconda3\envs\my_flask_env\lib\site-packages\flask\app.py", line 2446, in wsgi_appresponse = self.full_dispatch_request()File "C:\Users\MAULI\Miniconda3\envs\my_flask_env\lib\site-packages\flask\app.py", line 1951, in full_dispatch_requestrv = self.handle_user_exception(e)File "C:\Users\MAULI\Miniconda3\envs\my_flask_env\lib\site-packages\flask\app.py", line 1820, in handle_user_exceptionreraise(exc_type, exc_value, tb)File "C:\Users\MAULI\Miniconda3\envs\my_flask_env\lib\site-packages\flask\_compat.py", line 39, in reraiseraise valueFile "C:\Users\MAULI\Miniconda3\envs\my_flask_env\lib\site-packages\flask\app.py", line 1949, in full_dispatch_requestrv = self.dispatch_request()File "C:\Users\MAULI\Miniconda3\envs\my_flask_env\lib\site-packages\flask\app.py", line 1935, in dispatch_requestreturn self.view_functions[rule.endpoint](**req.view_args)File "C:\Users\MAULI\Desktop\MOM\app.py", line 55, in indexlabels = get_emotion()File "C:\Users\MAULI\Desktop\MOM\app.py", line 38, in get_emotionpreds = classifier.predict(roi)[0]File "C:\Users\MAULI\Miniconda3\envs\my_flask_env\lib\site-packages\keras\engine\training.py", line 1456, in predictself._make_predict_function()File "C:\Users\MAULI\Miniconda3\envs\my_flask_env\lib\site-packages\keras\engine\training.py", line 378, in _make_predict_function**kwargs)File "C:\Users\MAULI\Miniconda3\envs\my_flask_env\lib\site-packages\keras\backend\tensorflow_backend.py", line 3009, in function**kwargs)File "C:\Users\MAULI\Miniconda3\envs\my_flask_env\lib\site-packages\tensorflow\python\keras\backend.py", line 3201, in functionreturn GraphExecutionFunction(inputs, outputs, updates=updates, **kwargs)File "C:\Users\MAULI\Miniconda3\envs\my_flask_env\lib\site-packages\tensorflow\python\keras\backend.py", line 2939, in __init__with ops.control_dependencies(self.outputs):File "C:\Users\MAULI\Miniconda3\envs\my_flask_env\lib\site-packages\tensorflow\python\framework\ops.py", line 5028, in control_dependenciesreturn get_default_graph().control_dependencies(control_inputs)File "C:\Users\MAULI\Miniconda3\envs\my_flask_env\lib\site-packages\tensorflow\python\framework\ops.py", line 4528, in control_dependenciesc = self.as_graph_element(c)File "C:\Users\MAULI\Miniconda3\envs\my_flask_env\lib\site-packages\tensorflow\python\framework\ops.py", line 3478, in as_graph_elementreturn self._as_graph_element_locked(obj, allow_tensor, allow_operation)File "C:\Users\MAULI\Miniconda3\envs\my_flask_env\lib\site-packages\tensorflow\python\framework\ops.py", line 3557, in _as_graph_element_lockedraise ValueError("Tensor %s is not an element of this graph." % obj)ValueError: Tensor Tensor("activation_11/Softmax:0", shape=(?, 5), dtype=float32) is not an element of this graph.

回答：