使用ImageDataGenerator时如何产生ROC曲线

时间:2019-04-19 09:32:15

标签: python tensorflow keras roc

我想生成训练有素的模型的ROC曲线,但是我不知道如何使用ImageDataGenerator进行此操作。

我看到了该链接How can I plot AUC and ROC while using fit_generator and evaluate_generator to train my network?,但这仅回答了如何获取AUC的问题。

我还通过以下方式进行了尝试:

y_pred =  model.predict_generator(test_generator, steps= step_size_test)
fpr, tpr, tresholds = roc_curve(y_pred, test_generator.classes)

这给了我一个错误

这是我的代码的一部分


model.compile(loss="binary_crossentropy", optimizer= 'Adam', metrics=['accuracy', auc])


train_datagen = ImageDataGenerator(rescale=1.0 / 255.0)
train_generator = train_datagen.flow_from_directory(
    directory=f'./data/train/',
    target_size=(Preprocess.image_resolution, Preprocess.image_resolution),
    color_mode="grayscale",
    batch_size=64,
    classes=['a', 'b'],
    class_mode="binary",
    shuffle=True,
    seed=42
)

valid_datagen = ImageDataGenerator(rescale=1.0 / 255.0)
valid_generator = valid_datagen.flow_from_directory(
    directory=f'./data/valid/',
    target_size=(Preprocess.image_resolution, Preprocess.image_resolution),
    color_mode="grayscale",
    batch_size=8,
    classes=['a', 'b'],
    class_mode="binary",
    shuffle=True,
    seed=42
)

test_datagen = ImageDataGenerator()
test_generator = test_datagen.flow_from_directory(
    directory=f'./data/test/',
    target_size=(Preprocess.image_resolution, Preprocess.image_resolution),
    color_mode="grayscale",
    batch_size=1,
    classes=['a', 'b'],
    class_mode='binary',
    shuffle=False,
    seed=42
)

step_size_train = train_generator.n // train_generator.batch_size
step_size_valid = valid_generator.n // valid_generator.batch_size
step_size_test = test_generator.n // test_generator.batch_size

model = build_three_layer_cnn_model()

history = model.fit_generator(generator=train_generator, 
                    steps_per_epoch=step_size_train,
                    validation_data=valid_generator,
                    validation_steps=step_size_valid,
                    epochs=10)

1 个答案:

答案 0 :(得分:0)

您的代码存在问题:

roc_curve(y_pred, test_generator.classes)

根据scikit-learn的文档,您需要传递分数(概率),而不是将类作为第二个参数

另外,请注意,您的第一个参数是y_pred而不是y_true。

尝试调用roc_curve(y_true,y_scores),其中y_true是您的基本事实,y_scores是模型的输出概率(即model.predict(X_test))

ROC曲线的文档:https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html#sklearn.metrics.roc_curve