如何找到此CNN模型(keras)的ROC曲线和AUC分数 [英] How to find the ROC curve and AUC score of this CNN model (keras)

问题描述

我在keras中的CNN代码如下:

My CNN code in keras is as follows:

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)

无论我在sklearn.metrics中看到roc_curve的用法如何，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，用于像此处这样的CNN模型训练?

Wherever I see the use of roc_curve from sklearn.metrics, it takes parameters like x_train, y_train, x_test, y_test which I know can be pandas DataFrames but in my case it is not the case. How do I plot the ROC curve and get AUC score for model training for CNNs like here?

推荐答案

我知道了.我要做的就是将从preds = classifier.predict(test_set)获得的preds的数据类型与从labels = test_set获得的true_labels匹配. Preds基本上是一个numpy.ndarray，包含具有np.float32值的单个元素列表.将标签转换为相同的格式和形状后，即可完成roc_curve的工作.

I got it working. All I had to do was match the datatype of preds obtained from preds = classifier.predict(test_set) with the true_labels I got from labels = test_set. Preds is basically a numpy.ndarray containing single element lists which have np.float32 values. Conversion of labels to that same format and shape got the roc_curve working.

此外，我必须在fpr, tpr, threshold = roc_curve(true_labels, preds)中添加第三个变量阈值，以免出现ValueError:弹出太多无法解包错误的值.

Also, I had to add a third variable threshold in fpr, tpr, threshold = roc_curve(true_labels, preds) so no ValueError: too many values to unpack error popped up.

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