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

要加入CBAM-ResNet模块，可以按照以下步骤进行：

1.导入需要的库和模块：

from tensorflow.keras import layers
from tensorflow.keras.regularizers import l2

2.定义CBAM模块：

def cbam_block(cbam_feature, ratio=8):
    # channel attention
    cbam_feature_shape = cbam_feature.shape
    avg_pool = layers.GlobalAveragePooling2D()(cbam_feature) # shape=(batch_size, channels)
    dense1 = layers.Dense(cbam_feature_shape[-1]//ratio, activation='relu', kernel_initializer='he_normal', use_bias=True, bias_initializer='zeros')(avg_pool)
    dense2 = layers.Dense(cbam_feature_shape[-1], activation='sigmoid', kernel_initializer='he_normal', use_bias=True, bias_initializer='zeros')(dense1)
    reshaped_dense = layers.Reshape((1,1,cbam_feature_shape[-1]))(dense2)
    cbam_feature = layers.multiply([cbam_feature, reshaped_dense])

    # spatial attention
    max_pool = layers.GlobalMaxPooling2D()(cbam_feature)
    max_pool = layers.Reshape((1,1,cbam_feature_shape[-1]))(max_pool)
    avg_pool = layers.GlobalAveragePooling2D()(cbam_feature)
    avg_pool = layers.Reshape((1,1,cbam_feature_shape[-1]))(avg_pool)
    concat = layers.Concatenate(axis=1)([max_pool, avg_pool])
    cbam_feature = layers.Conv2D(filters=1, kernel_size=3, strides=1, padding='same', activation='sigmoid', kernel_initializer='he_normal', use_bias=False)(concat)
    cbam_feature = layers.multiply([cbam_feature, cbam_feature])

    return cbam_feature

3.在ResNet模型中加入CBAM模块：

def CBAM_ResNet(opt):
    # define input tensor
    input_tensor = layers.Input(shape=(224, 224, 3))

    # conv1
    x = layers.Conv2D(filters=64, kernel_size=7, strides=2, padding='same', kernel_initializer='he_normal', use_bias=False)(input_tensor)
    x = layers.BatchNormalization()(x)
    x = layers.Activation('relu')(x)
    x = layers.MaxPooling2D(pool_size=3, strides=2, padding='same')(x)

    # conv2_x
    x = conv_block(x, filters=[64, 64, 256], stage=2, block='a', strides=1)
    x = cbam_block(x) # add CBAM module
    x = identity_block(x, filters=[64, 64, 256], stage=2, block='b')
    x = cbam_block(x) # add CBAM module
    x = identity_block(x, filters=[64, 64, 256], stage=2, block='c')
    x = cbam_block(x) # add CBAM module

    # conv3_x
    x = conv_block(x, filters=[128, 128, 512], stage=3, block='a')
    x = cbam_block(x) # add CBAM module
    x = identity_block(x, filters=[128, 128, 512], stage=3, block='b')
    x = cbam_block(x) # add CBAM module
    x = identity_block(x, filters=[128, 128, 512], stage=3, block='c')
    x = cbam_block(x) # add CBAM module
    x = identity_block(x, filters=[128, 128, 512], stage=3, block='d')
    x = cbam_block(x) # add CBAM module

    # conv4_x
    x = conv_block(x, filters=[256, 256, 1024], stage=4, block='a')
    x = cbam_block(x) # add CBAM module
    x = identity_block(x, filters=[256, 256, 1024], stage=4, block='b')
    x = cbam_block(x) # add CBAM module
    x = identity_block(x, filters=[256, 256, 1024], stage=4, block='c')
    x = cbam_block(x) # add CBAM module
    x = identity_block(x, filters=[256, 256, 1024], stage=4, block='d')
    x = cbam_block(x) # add CBAM module
    x = identity_block(x, filters=[256, 256, 1024], stage=4, block='e')
    x = cbam_block(x) # add CBAM module
    x = identity_block(x, filters=[256, 256, 1024], stage=4, block='f')
    x = cbam_block(x) # add CBAM module

    # conv5_x
    x = conv_block(x, filters=[512, 512, 2048], stage=5, block='a')
    x = cbam_block(x) # add CBAM module
    x = identity_block(x, filters=[512, 512, 2048], stage=5, block='b')
    x = cbam_block(x) # add CBAM module
    x = identity_block(x, filters=[512, 512, 2048], stage=5, block='c')
    x = cbam_block(x) # add CBAM module

    # average pool
    x = layers.GlobalAveragePooling2D()(x)

    # output layer
    outputs = layers.Dense(units=1000, activation='softmax')(x)

    # create model
    model = keras.models.Model(inputs=input_tensor, outputs=outputs)

    # compile model
    model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])

    return model

以上就是如何加入CBAM-ResNet模块的步骤，具体实现还需要根据自己的需求进行调整。