使用keras实现densenet和Xception的模型融合

下面我将为您详细讲解使用keras实现densenet和Xception的模型融合的完整攻略。

1. 数据准备
   首先我们需要准备训练数据和验证数据。可以使用Keras中的ImageDataGenerator读入图像数据。这里我们以CIFAR-10数据集为例，代码如下：

from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
(X_train, y_train), (X_test, y_test) = cifar10.load_data()
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255

# 将标签转化为one-hot编码
from keras.utils import np_utils
num_classes = 10
Y_train = np_utils.to_categorical(y_train, num_classes)
Y_test = np_utils.to_categorical(y_test, num_classes)

# 定义数据增强
datagen = ImageDataGenerator(
        featurewise_center=False,
        samplewise_center=False,
        featurewise_std_normalization=False,
        samplewise_std_normalization=False,
        zca_whitening=False,
        rotation_range=0,
        width_shift_range=0.1,
        height_shift_range=0.1,
        horizontal_flip=True,
        vertical_flip=False)
datagen.fit(X_train)

1. 构建densenet和Xception模型
   我们可以使用已经在Keras中实现的densenet和Xception模型。代码如下：

from keras.applications.densenet import DenseNet121
from keras.applications.xception import Xception

# densenet
def densenet_model():
    base_model = DenseNet121(include_top=False, weights='imagenet', input_shape=(32,32,3))
    for layer in base_model.layers:
        layer.trainable = False
    x = base_model.output
    x = GlobalAveragePooling2D()(x)
    predictions = Dense(num_classes, activation='softmax')(x)
    model = Model(inputs=base_model.input, outputs=predictions)
    return model

# Xception
def xception_model():
    base_model = Xception(include_top=False, weights='imagenet', input_shape=(32,32,3))
    for layer in base_model.layers:
        layer.trainable = False
    x = base_model.output
    x = GlobalAveragePooling2D()(x)
    predictions = Dense(num_classes, activation='softmax')(x)
    model = Model(inputs=base_model.input, outputs=predictions)
    return model

1. 模型融合
   接下来我们使用融合策略将两个模型进行融合。

2. 平均策略融合
   代码如下：

def ensemble(models, model_input):
    outputs = [model.outputs[0] for model in models]
    y = Average()(outputs)
    model = Model(inputs=model_input, outputs=y, name='ensemble')
    return model

model_input = Input(shape=X_train.shape[1:])
md1 = densenet_model()
md2 = xception_model()
model1 = md1(model_input)
model2 = md2(model_input)
model = ensemble([model1, model2], model_input)

model.summary()

model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
history = model.fit_generator(datagen.flow(X_train, Y_train, batch_size=batch_size), 
                              validation_data=(X_test, Y_test), epochs=epochs)

• 加权平均策略融合
  代码如下：

def ensemble(models, model_input):
    outputs = [model.outputs[0] for model in models]
    y = Average()(outputs)
    model = Model(inputs=model_input, outputs=y, name='ensemble')
    return model

model_input = Input(shape=X_train.shape[1:])
md1 = densenet_model()
md2 = xception_model()
model1 = md1(model_input)
model2 = md2(model_input)
models = [model1, model2]
model = ensemble(models, model_input)

alpha = 0.5  # 第一个模型的权重
outputs = [model.outputs[i] * alpha + models[1].outputs[i] * (1 - alpha)
            for i in range(len(models[0].outputs))]
model = Model(inputs=model_input, outputs=outputs, name='ensemble')

model.summary()

model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
history = model.fit_generator(datagen.flow(X_train, Y_train, batch_size=batch_size), 
                              validation_data=(X_test, Y_test), epochs=epochs)

至此，我们已经完成了使用keras实现densenet和Xception的模型融合的完整攻略。以上是两条示例说明，希望对您有所帮助。