def MEAN_Spot(opt): inputs1 = layers.Input(shape=(42, 42, 1)) inputs2 = layers.Input(shape=(42, 42, 1)) inputs3 = layers.Input(shape=(42, 42, 1)) inputs = layers.Concatenate()([inputs1, inputs2, inputs3]) conv1 = layers.Conv2D(3, (7,7), padding='same', activation='relu', kernel_regularizer=l2(0.001))(inputs) ba1 = BasicBlock(3, 16)(conv1) ba2 = BasicBlock(16, 32, stride=2)(ba1) att = BasicBlock1(32, 64, stride=2)(ba2) # interpretation 1 merged_conv = layers.Conv2D(8, (5,5), padding='same', activation='relu', kernel_regularizer=l2(0.1))(att) 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 如何加入多头自注意力机制

要在这个模型中加入多头自注意力机制，可以使用 `MultiHeadAttention` 层。这个层将输入张量拆分成多个头部，然后对每个头部分别进行注意力计算。可以通过 `tf.keras.layers.MultiHeadAttention` 类来创建这个层，具体的实现方式如下所示：

from tensorflow.keras.layers import MultiHeadAttention

def MEAN_Spot(opt):
    inputs1 = layers.Input(shape=(42, 42, 1))
    inputs2 = layers.Input(shape=(42, 42, 1))
    inputs3 = layers.Input(shape=(42, 42, 1))
    
    # concatenate inputs
    inputs = layers.Concatenate()([inputs1, inputs2, inputs3])
    
    # convolutional layers
    conv1 = layers.Conv2D(3, (7,7), padding='same', activation='relu', kernel_regularizer=l2(0.001))(inputs)
    ba1 = BasicBlock(3, 16)(conv1)
    ba2 = BasicBlock(16, 32, stride=2)(ba1)
    
    # apply multi-head self-attention
    att = MultiHeadAttention(num_heads=4, key_dim=32)(ba2, ba2)
    
    # interpret output
    merged_conv = layers.Conv2D(8, (5,5), padding='same', activation='relu', kernel_regularizer=l2(0.1))(att)
    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)

    # create model
    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

在这个修改后的代码中，我们使用 `tf.keras.layers.MultiHeadAttention` 类，将 `ba2` 张量作为输入。`num_heads` 参数指定了头部的数量，`key_dim` 参数指定了每个头部的维度。这个层的输出张量与输入张量的形状相同，但是其中的每个位置都会根据周围位置的信息进行加权。