神经网络|使用Keras对多个模型进行拼接 python|人工智能

【神经网络|使用Keras对多个模型进行拼接】在训练模型时候，常常需要把多个模型拼接起来，常用的方式主要有以下几种：
代码引自于，感谢原作者默盒
1. 添在末尾:

base_model = InceptionV3(weights='imagenet', include_top=False) x = base_model.output x = GlobalAveragePooling2D()(x) x = Dense(1, activation='relu')(x)model = Model(inputs=base_model.input, outputs=x) model.summary()

2. 添在开头和末尾:

# 在开头加1x1卷积层, 使4通道降为3通道, 再传入InceptionV3 def head_model(input_shape=(150, 150, 4)): input_tensor = Input(input_shape) x = Conv2D(128, (1, 1), activation='relu')(input_tensor) x = Conv2D(3, (1, 1), activation='relu')(x) model = Model(inputs=input_tensor, outputs=x, name='head') return modelhead_model = head_model() body_model = InceptionV3(weights='imagenet', include_top=False) base_model = Model(head_model.input, body_model(head_model.output)) x = base_model.output x = GlobalAveragePooling2D()(x) x = Dense(1, activation='relu')(x)model = Model(inputs=base_model.inputs, outputs=x, name='net') base_model.summary()

3. 两数据输入流合并于末尾:

base_model = InceptionV3(weights='imagenet', include_top=False, input_shape=(150, 150, 3)) flat = Flatten()(base_model.output) input_K = Input((100, ))# another_input K_flow = Activation(activation='linear')(input_K) x = concatenate([flat, K_flow])# 合流 x = Dense(1024, activation='relu')(x) x = Dense(512, activation='relu')(x) x = Dense(1, activation='relu')(x) model = Model(inputs=[*base_model.inputs, input_K], outputs=x)# 数据生成器那里也以这种形式生成([x_0, x_1], y)即可. model.summary()