tensorflow用class定义的Basic Block如何调用到网络def MEAN_Spot(opt): # channel 1 inputs1 = layers.Input(shape=(42, 42, 1)) inputs2 = layers.Input(shape=(42, 42, 1)) inputs3 = layers.Input(shape=(42, 42, 1)) # merge 1 inputs = layers.Concatenate()([inputs1, inputs2, inputs3]) conv1 = layers.Conv2D(3, (7,7), padding='same', activation='relu', kernel_regularizer=l2(0.001))(inputs) 的后面

要将 BasicBlock 类应用到整个网络中，你可以定义一个包含多个 BasicBlock 的自定义网络类，并在其中按照需要连接这些 BasicBlock。以下是一个示例代码，仅供参考：

import tensorflow as tf
from tensorflow.keras import layers

class BasicBlock(tf.keras.Model):
    def __init__(self, filters):
        super(BasicBlock, self).__init__()
        self.conv1 = layers.Conv2D(filters, (3, 3), padding='same')
        self.conv2 = layers.Conv2D(filters, (3, 3), padding='same')
        self.relu = layers.ReLU()
        self.add = layers.Add()
        self.lam = layers.Lambda(MEAN_Spot)
        self.conv3 = layers.Conv2D(filters, (3, 3), padding='same')
        self.bn = layers.BatchNormalization()

    def call(self, inputs):
        x = self.conv1(inputs)
        x = self.relu(x)
        x = self.conv2(x)
        x = self.lam(x)
        x = self.conv3(x)
        x = self.bn(x)
        x = self.add([inputs, x])
        x = self.relu(x)
        return x

class MyNet(tf.keras.Model):
    def __init__(self):
        super(MyNet, self).__init__()
        self.block1 = BasicBlock(64)
        self.block2 = BasicBlock(128)
        self.block3 = BasicBlock(256)
        self.avgpool = layers.GlobalAveragePooling2D()
        self.fc = layers.Dense(10, activation='softmax')

    def call(self, inputs):
        x = self.block1(inputs)
        x = self.block2(x)
        x = self.block3(x)
        x = self.avgpool(x)
        x = self.fc(x)
        return x

在上述代码中，我们定义了一个名为 `MyNet` 的自定义网络类，其中包含了三个 BasicBlock，一个全局平均池化层和一个全连接层。在 `call` 方法中，我们依次调用了三个 BasicBlock，将它们的输出作为下一个 BasicBlock 的输入，最终得到全局平均池化层的输出，并通过全连接层得到最终的分类结果。