下面的代码片段如何绘制ROC曲线？

Question

我是深度学习新手。我正在尝试为以下代码生成 ROC 曲线。我正在使用喀拉拉。 类大小为 10，图像为大小为 1001003 的 RGB 图像。

target_size=(100,100,3)

train_generator = train_datagen.flow_from_directory('path',
    target_size=target_size[:-1],
    batch_size=16,
    class_mode='categorical',
    subset='training',
    seed=random_seed)

valid_generator = ...

test_generator = ...
n_classes = len(set(train_generator.classes))

print(n_classes)

input_layer = keras.layers.Input(shape=target_size)

conv2d_1 = keras.layers.Conv2D(filters=64, kernel_size=(3,3), strides=1, padding='same', 
activation='relu',
                           kernel_initializer='he_normal')(input_layer)

batchnorm_1 = keras.layers.BatchNormalization()(conv2d_1)
maxpool1=keras.layers.MaxPool2D(pool_size=(2,2))(batchnorm_1)


conv2d_2 = keras.layers.Conv2D(filters=32, kernel_size=(3,3), strides=1, padding='same', 
activation='relu',
                           kernel_initializer='he_normal')(maxpool1)
batchnorm_2 = keras.layers.BatchNormalization()(conv2d_2)

maxpool2=keras.layers.MaxPool2D(pool_size=(2,2))(batchnorm_2)


flatten = keras.layers.Flatten()(maxpool2)
dense_1 = keras.layers.Dense(256, activation='relu')(flatten)

dense_2 = keras.layers.Dense(n_classes, activation='softmax')(dense_1)



model = keras.models.Model(input_layer, dense_3)

model.compile(optimizer=keras.optimizers.Adam(0.001),
          loss='categorical_crossentropy',
          metrics=['acc'])
model.summary()

model.fit_generator(generator=train_generator, validation_data=valid_generator,
                epochs=200)
                
score = model.evaluate_generator(test_generator)

print(score)

我想看到曲线并生成 ROC 曲线。请帮忙。

Answer 1

在您的代码中添加以下代码。

import numpy as np
from sklearn import metrics

x, y = test_generator.next()
prediction = model.predict(x)

predict_label1 = np.argmax(prediction, axis=-1)
true_label1 = np.argmax(y, axis=-1)

y = np.array(true_label1)

scores = np.array(predict_label1)
fpr, tpr, thresholds = metrics.roc_curve(y, scores, pos_label=9)
roc_auc = metrics.auc(fpr, tpr)


plt.figure()
lw = 2
plt.plot(fpr, tpr, color='darkorange',
     lw=lw, label='ROC curve (area = %0.2f)' % roc_auc)
plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic (ROC)')
plt.legend(loc="lower right")
plt.show()

希望这有效。

Answer 2

导入系统 导入操作系统 从操作员导入 itemgetter 将 matplotlib.pyplot 导入为 plt 从 sklearn.metrics 导入 auc

def main(argv=[name]):

if len(sys.argv) != 4:
    print("Usage: <actives> <scores> <image>")
    return 1

afname = sys.argv[1]
sfname = sys.argv[2]
ofname = sys.argv[3]

f, ext = os.path.splitext(ofname)
if not is_supported_image_type(ext):
    print("Format \"%s\" is not supported!" % ext)
    return 1

# read id of actives

actives = load_actives(afname)
print("Loaded %d actives from %s" % (len(actives), afname))

# read molecule id - score pairs