我在keras中的CNN代码如下：

from keras.models import Sequential
from keras.layers import Convolution2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
from keras.layers import Dropout

classifier = Sequential()
#1st Conv layer
classifier.add(Convolution2D(64, (9, 9), input_shape=(64, 64, 3), activation='relu'))
classifier.add(MaxPooling2D(pool_size=(4,4)))
#2nd Conv layer
classifier.add(Convolution2D(32, (3, 3), activation='relu'))
classifier.add(MaxPooling2D(pool_size=(2,2)))

#Flattening
classifier.add(Flatten())

# Step 4 - Full connection
classifier.add(Dense(units = 128, activation = 'relu'))
classifier.add(Dropout(0.1))
classifier.add(Dense(units = 128, activation = 'relu'))
classifier.add(Dropout(0.2))
classifier.add(Dense(units = 128, activation = 'relu'))
classifier.add(Dense(units = 2, activation = 'softmax'))

classifier.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])

#Fitting dataset

from keras.preprocessing.image import ImageDataGenerator

train_datagen = ImageDataGenerator(rescale = 1./255,
                                   shear_range = 0.2,
                                   zoom_range = 0.2,
                                   horizontal_flip = True)

test_datagen = ImageDataGenerator(rescale = 1./255)

training_set = train_datagen.flow_from_directory('dataset/training_set',
                                                 target_size = (64, 64),
                                                 batch_size = 32,
                                                 class_mode = 'categorical')

test_set = test_datagen.flow_from_directory('dataset/test_set',
                                            target_size = (64, 64),
                                            batch_size = 32,
                                            class_mode = 'categorical')

classifier.fit_generator(
        training_set,
        steps_per_epoch=(1341+3875)/32,
        epochs=15,
        validation_data=test_set,
        validation_steps=(234+390)/32)

无论在哪里看到sklearn.metrics中使用roc_curve的地方，它都带有x_train，y_train，x_test，y_test之类的参数，我知道它们可以是pandas DataFrames，但就我而言并非如此。我如何绘制ROC曲线并获得AUC分数，用于像这样的CNN模型训练？