在Keras中将模型合并为一个模型

from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Dense, Concatenate
from tensorflow.keras.applications import VGG16, ResNet50

# 定义两个基础模型
base_model1 = VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
base_model2 = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))

# 冻结基础模型的权重
for layer in base_model1.layers:
    layer.trainable = False
for layer in base_model2.layers:
    layer.trainable = False

# 定义输入层
input_layer = Input(shape=(224, 224, 3))

# 获取基础模型的输出
output1 = base_model1(input_layer)
output2 = base_model2(input_layer)

# 合并两个输出
merged_output = Concatenate()([output1, output2])

# 添加全连接层进行最终预测
x = Dense(256, activation='relu')(merged_output)
predictions = Dense(10, activation='softmax')(x)

# 创建组合模型
combined_model = Model(inputs=input_layer, outputs=predictions)

# 编译模型
combined_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# 打印模型结构
combined_model.summary()