我有一个有效的 Keras 模型，可以在 repl 中做出很好的预测，但在多次访问时无法加载到 Flask 应用程序中。

K.clear_session()并graph = tf.get_default_graph()没有为我工作。

model1 = Sequential()

model1.add(Conv2D(32, 3, padding='same', input_shape=(256,256,3), activation='relu'))
model1.add(MaxPooling2D(pool_size=(2, 2), data_format="channels_first"))

#print("First layer...")
model1.add(Conv2D(32, 3, padding='same', activation='relu'))
model1.add(MaxPooling2D(pool_size=(2, 2), data_format="channels_first"))

#print("Second layer...")
model1.add(Conv2D(64, 3, padding='same', activation='relu'))
model1.add(MaxPooling2D(pool_size=(2, 2), data_format="channels_first"))

model1.add(Flatten()) 
model1.add(Dense(64))
model1.add(Activation('relu'))
model1.add(Dropout(0.5))
model1.add(Dense(1))
model1.add(Activation('sigmoid'))

model1.load_weights("Weights_model1.h5")

model2 = Sequential()

model2.add(Conv2D(32, 3, padding='same', input_shape=(256,256,3), activation='relu'))
model2.add(MaxPooling2D(pool_size=(2, 2), data_format="channels_first"))

#print("First layer...")
model2.add(Conv2D(32, 3, padding='same', activation='relu'))
model2.add(MaxPooling2D(pool_size=(2, 2), data_format="channels_first"))

#print("Second layer...")
model2.add(Conv2D(64, 3, padding='same', activation='relu'))
model2.add(MaxPooling2D(pool_size=(2, 2), data_format="channels_first"))

model2.add(Flatten())  # this converts our 3D feature maps to 1D feature vectors
model2.add(Dense(64))
model2.add(Activation('relu'))
model2.add(Dropout(0.5))
model2.add(Dense(1))
model2.add(Activation('sigmoid'))

model2.load_weights("Weights_model2.h5")

Training_water_data = 'C:\\Users\\ysmehta\\'+folder+'\\'
training_water_data = [x for x in sorted(os.listdir(Training_water_data))]
x_train_water_data = np.empty((len(training_water_data),256,256,3),dtype = 'float32')
for i,name  in enumerate(training_water_data):
    im = cv2.imread(Training_water_data + name).astype('int16').astype('float32')/255.
    im = im[:,:,:]
    im = cv2.resize(im,dsize = (256,256),interpolation = cv2.INTER_NEAREST)
    x_train_water_data[i] = im

x_train_water_data = x_train_water_data.reshape(x_train_water_data.shape[0],256,256,3)

for i in range(0,x_train_water_data.shape[0]):
    A = x_train_water_data[i]
    B = A.reshape(1,256,256,3)
    prediction1 = model1.predict(B,verbose=0)
    if (prediction1 >= 0.7):
    prediction2 = model2.predict(B, verbose=0)
else:
    returnstring="No manhole found"
    prediction2 = -1
if(prediction2 >= 0.6):
    returnstring="Water Leakage"

else:
    if(prediction1 >= 0.7):
        returnstring="Not a Leakage"
return returnstring;

if __name__ == "__main__":
    app.run(host='131.163.144.196',port=5000)`