如何根据这些数据绘制ROC曲线?

我已经使用Keras训练了一个卷积神经网络(CNN),并做了以下工作,以便找到测试数据集的准确性:

for root, dirs, files in os.walk(test_directory):
    for file in files:
        img = cv2.imread(root + '/' + file)
        img = cv2.resize(img,(512,512),interpolation=cv2.INTER_AREA)
        img = np.expand_dims(img, axis=0)
        img = img/255.0
        if os.path.basename(root) == 'nevus':
            label = 1
        elif os.path.basename(root) == 'melanoma':
            label = 0
        img_class = model.predict_classes(img)
        prediction = img_class[0]
        if prediction == label:
            correct_classification = correct_classification + 1
        print 'This is the prediction: '
        print prediction
        number_of_test_images = number_of_test_images + 1

print 'correct results:'
print correct_classification

print 'number of test images'
print number_of_test_images

print 'Accuray:'
print number_of_test_images/correct_classification * 100

在测试数据集上测试模型,有没有办法找到ROC曲线?

谢谢。