始终获得AUC = 0.5
始终获得AUC = 0.5
提问于 2019-01-06 12:45:28
我正在尝试使用卷积神经网络(CNN)来预测测试图像的类别，如下所示：
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
labels.append(label)
img_class = model.predict_classes(img)
img_class_probability = model.predict(img)
prediction_probability = img_class_probability[0]
prediction_probabilities.append(prediction_probability)
prediction = img_class[0]
if prediction == label:
correct_classification = correct_classification + 1
number_of_test_images = number_of_test_images + 1
fpr, tpr, thresholds = roc_curve(labels, prediction_probabilities)
auc_value = auc(fpr, tpr)我想问一下为什么我的AUC总是= 0.5？也许我做错了什么？
谢谢。
回答 1
Stack Overflow用户
发布于 2020-06-11 16:08:10
在这种情况下，可能是AUC的阈值设置不正确，这导致所有模型输出值(得分/概率)在所有阈值选择下始终是同一类别，因此AUC将始终为0.5
以下示例说明了阈值对AUC的影响
import tensorflow as tf # tf 2.0+
y_true = [0, 0, 1, 1, 1, 1]
y_pred = [0.001, 0.002, 0.003, 0.004, 0.005, 0.006]
#--------------------------------------------------------------
m1 = tf.keras.metrics.AUC(thresholds=[0.0, 0.5, 1.0])
m1.update_state(y_true, y_pred)
# when threshold=0.0 , y_class always be 1
# when threshold=0.5 , y_class always be 0
# when threshold=1.0 , y_class always be 0
print('AUC={}'.format(m1.result().numpy()))
# output: AUC=0.5
#--------------------------------------------------------------
m2 = tf.keras.metrics.AUC(thresholds=[0.0, 0.0045, 1.0])
m2.update_state(y_true, y_pred)
# when threshold=0.0 , y_class always be 1
# when threshold=0.0045 , y_class will be [0, 0, 0, 0, 1, 1]
# when threshold=1.0 , y_class always be 0
print('AUC={}'.format(m2.result().numpy()))
# output: AUC=0.75
#--------------------------------------------------------------
m3 = tf.keras.metrics.AUC(num_thresholds=300)
# print(m3.thresholds)
m3.update_state(y_true, y_pred)
print('AUC={}'.format(m3.result().numpy()))
# output: AUC=0.875页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/54058703
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