CNN keras中图像的混淆矩阵

Question

我已经使用keras训练了CNN的模型（多类分类），现在我想在图像测试集上评估该模型。

除了准确性，准确性和召回率外，还有什么可能的评估模型的选项？我知道如何从自定义脚本获得精度和召回率。但是我找不到一种方法来获取我的12类图像的混淆矩阵。Scikit学习显示了一种方法，但不适用于图像。我正在使用model.fit_generator（）

有没有一种方法可以为我所有的班级创建混淆矩阵，或者为我的班级找到分类信心？尽管我可以下载模型并在本地运行，但我正在使用Google Colab。

任何帮助，将不胜感激。

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')

Answer 1

这是所有类的混淆矩阵（或使用scikit-learn的统计信息）的获取方法：

1.预测班级

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，获取真实的类和类标签

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，创建混淆矩阵以根据Keras模型进行预测。如果您有任何问题，请仔细阅读评论。或使用Keras CNN图像分类器进行预测

Answer 2

为何scikit-learn功能无法完成任务？您向前传递训练/测试集中的所有样本（图像），将一键编码转换为标签编码（请参阅链接），然后将其传递为sklearn.metrics.confusion_matrixas y_pred。您将以类似的方式进行操作y_true（单打标签）。

样例代码：

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)