def MEAN_Spot(opt): # channel 1 inputs1 = layers.Input(shape=(42,42,1)) conv1 = layers.Conv2D(3, (5,5), padding='same', activation='relu', kernel_regularizer=l2(0.001))(inputs1) bn1 = layers.BatchNormalization()(conv1) pool1 = layers.MaxPooling2D(pool_size=(3, 3), padding='same', strides=(3,3))(bn1) do1 = layers.Dropout(0.3)(pool1) # channel 2 inputs2 = layers.Input(shape=(42,42,1)) conv2 = layers.Conv2D(3, (5,5), padding='same', activation='relu', kernel_regularizer=l2(0.001))(inputs2) bn2 = layers.BatchNormalization()(conv2) pool2 = layers.MaxPooling2D(pool_size=(3, 3), padding='same', strides=(3,3))(bn2) do2 = layers.Dropout(0.3)(pool2) # channel 3 inputs3 = layers.Input(shape=(42,42,1)) conv3 = layers.Conv2D(8, (5,5), padding='same', activation='relu', kernel_regularizer=l2(0.001))(inputs3) bn3 = layers.BatchNormalization()(conv3) pool3 = layers.MaxPooling2D(pool_size=(3, 3), padding='same', strides=(3,3))(bn3) do3 = layers.Dropout(0.3)(pool3) # merge 1 merged = layers.Concatenate()([do1, do2, do3]) # interpretation 1 merged_conv = layers.Conv2D(8, (5,5), padding='same', activation='relu', kernel_regularizer=l2(0.1))(merged) merged_pool = layers.MaxPooling2D(pool_size=(2, 2), padding='same', strides=(2,2))(merged_conv) flat = layers.Flatten()(merged_pool) flat_do = layers.Dropout(0.2)(flat) # outputs outputs = layers.Dense(1, activation='linear', name='spot')(flat_do) #Takes input u, v, os model = keras.models.Model(inputs=[inputs1, inputs2, inputs3], outputs=[outputs]) model.compile( loss={'spot':'mse'}, optimizer=opt, metrics={'spot':tf.keras.metrics.MeanAbsoluteError()}, ) return model 如何加入CBAM-ResNet模块，加在哪里

要加入CBAM-ResNet模块，可以参考以下步骤：

1. 导入CBAM模块

from cbam import cbam_block

2. 在每个ResNet块中加入CBAM模块，可以在每个ResNet块的末尾添加CBAM块，如下所示：

def resnet_block(input_layer, filters, conv_num=3, activation='relu'):
    # ...
    shortcut = input_layer
    for i in range(conv_num - 1):
        x = layers.Conv2D(filters, (3, 3), padding='same')(x)
        x = layers.BatchNormalization()(x)
        x = layers.Activation(activation)(x)
    x = layers.Conv2D(filters, (3, 3), padding='same')(x)
    x = layers.BatchNormalization()(x)
    # 添加CBAM模块
    x = cbam_block(x)
    output_layer = layers.add([x, shortcut])
    output_layer = layers.Activation(activation)(output_layer)
    return output_layer

3. 修改模型构建部分，加入CBAM-ResNet模块，如下所示：

def CBAM_ResNet(input_shape, num_classes, filters):
    inputs = layers.Input(shape=input_shape)
    x = layers.Conv2D(filters, (3, 3), padding='same')(inputs)
    x = layers.BatchNormalization()(x)
    x = layers.Activation('relu')(x)

    # 添加CBAM模块
    x = cbam_block(x)

    x = resnet_block(x, filters)
    x = resnet_block(x, filters)
    x = layers.GlobalAveragePooling2D()(x)
    x = layers.Dense(num_classes, activation='softmax')(x)
    model = models.Model(inputs, x)
    return model

这样就可以在CBAM-ResNet模块中加入CBAM模块了。