找不到我的CNN模型的Accuracy和ROC曲线

Question

示例CNN代码如下:

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)))
classifier.add(Flatten())
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'])
from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(rescale = 1./255,
shear_range = 0.2,
zoom_range = 0.2,
horizontal_flip = True)
training_set = train_datagen.flow_from_directory('D:/regionGrowing_MLT/png_orig_imgs/Training',
target_size = (64, 64),
batch_size = 32,
class_mode = 'categorical')
test_datagen = ImageDataGenerator(rescale = 1./255)
test_set = test_datagen.flow_from_directory('D:/regionGrowing_MLT/png_orig_imgs/Test',
target_size = (64, 64),
batch_size = 32,
class_mode = 'categorical'
)
probs=classifier.fit(x = training_set, validation_data = test_set, epochs = 50)

我尝试以下行来查找ROC曲线，但我得到一个错误消息:

predictions = classifier.predict(test_set)
fpr, tpr,threshold = roc_curve(test_set,predictions)

显示如下错误信息:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-52-2ea53b1ba7f1> in <module>
----> 1 fpr, tpr,threshold = roc_curve(test_set,predictions)
ValueError: Expected array-like (array or non-string sequence), got <keras.preprocessing.image.DirectoryIterator object at 0x000002D21D1B61C0>

如有任何建议，不胜感激。

Answer 1

从错误来看，我认为你必须更改keras。处理图像对象到数组。试试这个，我想这会对你有帮助。

精度

fil_acc_orig = accuracy_score(y_test, predictions.to_array())

ROC曲线

fil_acc_orig = roc_curve(y_test, predictions.to_array())