Question

我使用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。

除了准确性，精确度和召回率之外，评估我的模型的可能选项有哪些？我知道如何从自定义脚本中获得精确度和召回率。但我无法找到一种方法来获取我的12类图像的混淆矩阵。 Scikit-learn显示way，但不显示图像。我正在使用model.fit_generator（）

有没有办法为我的所有课程创建混淆矩阵或在我的课程中找到分类信心？我正在使用Google Colab，但我可以下载该模型并在本地运行。

任何帮助都将不胜感激。

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')

Answer 1

以下是如何为所有类获取混淆矩阵（或者使用scikit-learn进行统计）：

1.预测课程

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.获取真实课程和课程标签

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)

您可以阅读更多here

编辑：如果以上操作不起作用，请查看此视频Create confusion matrix for predictions from Keras model。如果您有问题，可能会查看评论。或Make predictions with a Keras CNN Image Classifier

Answer 2

为什么scikit-learn功能不起作用？您转发传递列车/测试集中的所有样本（图像），将单热编码转换为标签编码（请参阅link）并将其作为sklearn.metrics.confusion_matrix传递到y_pred。您以类似的方式继续y_true（一热到标签）。

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)

CNN keras中图像的混淆矩阵

2 个答案: