[英]Confusion matrix on images in CNN keras
我已经使用 keras 训练了我的 CNN 模型(多类分类),现在我想在我的测试图像集上评估模型。
除了准确率、准确率和召回率之外,还有哪些可能的选项来评估我的模型? 我知道如何从自定义脚本中获得精度和召回率。 但是我找不到一种方法来获得我的 12 类图像的混淆矩阵。 Scikit-learn 展示了一种方式,但不适用于图像。 我正在使用model.fit_generator()
有没有办法为我的所有类创建混淆矩阵或找到我的类的分类置信度? 我正在使用 Google Colab,不过我可以下载模型并在本地运行它。
任何帮助,将不胜感激。
代码:
train_data_path = 'dataset_cfps/train'
validation_data_path = 'dataset_cfps/validation'
#Parametres
img_width, img_height = 224, 224
vggface = VGGFace(model='resnet50', include_top=False, input_shape=(img_width, img_height, 3))
#vgg_model = VGGFace(include_top=False, input_shape=(224, 224, 3))
last_layer = vggface.get_layer('avg_pool').output
x = Flatten(name='flatten')(last_layer)
xx = Dense(256, activation = 'sigmoid')(x)
x1 = BatchNormalization()(xx)
x2 = Dropout(0.3)(x1)
y = Dense(256, activation = 'sigmoid')(x2)
yy = BatchNormalization()(y)
y1 = Dropout(0.6)(yy)
x3 = Dense(12, activation='sigmoid', name='classifier')(y1)
custom_vgg_model = Model(vggface.input, x3)
# Create the model
model = models.Sequential()
# Add the convolutional base model
model.add(custom_vgg_model)
model.summary()
#model = load_model('facenet_resnet_lr3_SGD_sameas1.h5')
def recall(y_true, y_pred):
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
recall = true_positives / (possible_positives + K.epsilon())
return recall
def precision(y_true, y_pred):
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
precision = true_positives / (predicted_positives + K.epsilon())
return precision
train_datagen = ImageDataGenerator(
rescale=1./255,
rotation_range=20,
width_shift_range=0.2,
height_shift_range=0.2,
horizontal_flip=True,
fill_mode='nearest')
validation_datagen = ImageDataGenerator(rescale=1./255)
# Change the batchsize according to your system RAM
train_batchsize = 32
val_batchsize = 32
train_generator = train_datagen.flow_from_directory(
train_data_path,
target_size=(img_width, img_height),
batch_size=train_batchsize,
class_mode='categorical')
validation_generator = validation_datagen.flow_from_directory(
validation_data_path,
target_size=(img_width, img_height),
batch_size=val_batchsize,
class_mode='categorical',
shuffle=True)
# Compile the model
model.compile(loss='categorical_crossentropy',
optimizer=optimizers.SGD(lr=1e-3),
metrics=['acc', recall, precision])
# Train the model
history = model.fit_generator(
train_generator,
steps_per_epoch=train_generator.samples/train_generator.batch_size ,
epochs=100,
validation_data=validation_generator,
validation_steps=validation_generator.samples/validation_generator.batch_size,
verbose=1)
# Save the model
model.save('facenet_resnet_lr3_SGD_new_FC.h5')
以下是获取所有类的混淆矩阵(或者可能使用 scikit-learn 进行统计)的方法:
1.预测类
test_generator = ImageDataGenerator()
test_data_generator = test_generator.flow_from_directory(
test_data_path, # Put your path here
target_size=(img_width, img_height),
batch_size=32,
shuffle=False)
test_steps_per_epoch = numpy.math.ceil(test_data_generator.samples / test_data_generator.batch_size)
predictions = model.predict_generator(test_data_generator, steps=test_steps_per_epoch)
# Get most likely class
predicted_classes = numpy.argmax(predictions, axis=1)
2.获取真值类和类标签
true_classes = test_data_generator.classes
class_labels = list(test_data_generator.class_indices.keys())
3.使用scikit-learn获取统计信息
report = metrics.classification_report(true_classes, predicted_classes, target_names=class_labels)
print(report)
你可以在这里阅读更多
编辑:如果上述方法不起作用,请观看此视频Create Confusion matrix for predictions from Keras model 。 如果您有问题,可能会查看评论。 或者使用 Keras CNN 图像分类器进行预测
为什么 scikit-learn 函数不能完成这项工作? 您直传在火车/测试集所有样本(图片),编码一个热到标签编码转换(见链接),并将其传递到sklearn.metrics.confusion_matrix
为y_pred
。 您以类似的方式使用y_true
(one-hot to label)。
示例代码:
import sklearn.metrics as metrics
y_pred_ohe = KerasClassifier.predict(X) # shape=(n_samples, 12)
y_pred_labels = np.argmax(y_pred_ohe, axis=1) # only necessary if output has one-hot-encoding, shape=(n_samples)
confusion_matrix = metrics.confusion_matrix(y_true=y_true_labels, y_pred=y_pred_labels) # shape=(12, 12)
这里cats和dogs是类标签:
#Confusion Matrix and Classification Report
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
Y_pred = model.predict_generator(validation_generator, nb_validation_samples //
batch_size+1)
y_pred = np.argmax(Y_pred, axis=1)
print('Confusion Matrix')
print(confusion_matrix(validation_generator.classes, y_pred))
print('Classification Report')
target_names = ['Cats', 'Dogs']
print(classification_report(validation_generator.classes, y_pred, target_names=target_names))
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