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 更改模型加入CBAM模块

要将CBAM模块添加到该模型中，您需要对每个通道的特征图执行以下操作：

1. 通过使用Conv2D层，将通道中的特征图降维到较低的维度。
2. 通过使用CAM和PAM模块，对特征图执行注意力机制。
3. 将注意力机制后的特征图升维回原始维度。
4. 将三个通道的特征图合并，以形成一个新的特征图。
5. 经过一些卷积和池化操作，最后输出预测结果。

以下是您可能会用到的CBAM实现示例：

def channel_attention(inputs, ratio=8):
    channels = inputs.shape[-1]
    gap = layers.GlobalAveragePooling2D()(inputs)
    fc1 = layers.Dense(channels // ratio, activation='relu')(gap)
    fc2 = layers.Dense(channels, activation='sigmoid')(fc1)
    return layers.Reshape((1, 1, channels))(fc2) * inputs

def spatial_attention(inputs):
    conv = layers.Conv2D(filters=1, kernel_size=(7, 7), padding='same', activation='sigmoid')(inputs)
    return layers.multiply([inputs, conv])

def CBAM_block(inputs):
    channels_att = channel_attention(inputs)
    spatial_att = spatial_attention(inputs)
    outputs = layers.concatenate([channels_att, spatial_att], axis=-1)
    return outputs

您可以在每个通道的特征图上应用CBAM块，然后将它们合并以形成新的特征图。以下是您可能会用到的修改后的模型示例：

def MEAN_Spot_CBAM(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)
    cbam1 = CBAM_block(bn1)
    pool1 = layers.MaxPooling2D(pool_size=(3, 3), padding='same', strides=(3, 3))(cbam1)
    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)
    cbam2 = CBAM_block(bn2)
    pool2 = layers.MaxPooling2D(pool_size=(3, 3), padding='same', strides=(3, 3))(cbam2)
    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)
    cbam3 = CBAM_block(bn3)
    pool3 = layers.MaxPooling2D(pool_size=(3, 3), padding='same', strides=(3, 3))(cbam3)
    do3 = layers.Dropout(0.3)(pool3)

    # merge 1
    merged = layers.concatenate([do1, do2, do3], axis=-1)

    # 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

请注意，这只是一个可能的实现示例，您可以根据您的需求进行修改。