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(8, 18)(conv1) ba2 = BasicBlock(18, 28, stride=2)(ba1) att = BasicBlock1(28, 8, 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 各层通道数

在这段代码中，第一个 Conv2D 层的通道数为 3，第一个 BasicBlock 层的输入通道数为 3，输出通道数为 8，第二个 BasicBlock 层的输入通道数为 8，输出通道数为 28，第三个 BasicBlock1 层的输入通道数为 28，输出通道数为 8，最后一个 Conv2D 层的输入通道数为 8，输出通道数为 1（即输出一个数值）。

相关问题

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)改为class定义形式

你可以将这段代码转化为一个类的形式，例如：

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

class MeanSpot:
    def __init__(self):
        self.inputs1 = layers.Input(shape=(42, 42, 1))
        self.inputs2 = layers.Input(shape=(42, 42, 1))
        self.inputs3 = layers.Input(shape=(42, 42, 1))
    
    def build(self):
        # merge 1
        inputs = layers.Concatenate()([self.inputs1, self.inputs2, self.inputs3])
        conv1 = layers.Conv2D(3, (7,7), padding='same', activation='relu', kernel_regularizer=l2(0.001))(inputs)
        return conv1

这样，你就可以使用 `MeanSpot` 类来创建一个 `MEAN_Spot` 对象，然后调用 `build()` 方法来构建模型。例如：

mean_spot = MeanSpot()
model = mean_spot.build()

这个模型与原来的 `MEAN_Spot` 函数是等价的。

class BasicBlock(layers.Layer): expansion = 1 def __init__(self, in_channels, out_channels, stride=1):定义的basicblock模块之后如何放到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)后面

可以使用`BasicBlock`类来定义一个基本块模块，然后在`MEAN_Spot`函数中调用该模块。具体实现方法如下：

class BasicBlock(layers.Layer):
    expansion = 1
    def __init__(self, in_channels, out_channels, stride=1):
        super(BasicBlock, self).__init__()
        self.conv1 = layers.Conv2D(out_channels, kernel_size=3, strides=stride, padding='same', use_bias=False)
        self.bn1 = layers.BatchNormalization()
        self.relu = layers.ReLU()
        self.conv2 = layers.Conv2D(out_channels * self.expansion, kernel_size=3, strides=1, padding='same', use_bias=False)
        self.bn2 = layers.BatchNormalization()
        if stride != 1 or in_channels != out_channels * self.expansion:
            self.shortcut = keras.Sequential([
                layers.Conv2D(out_channels * self.expansion, kernel_size=1, strides=stride, use_bias=False),
                layers.BatchNormalization()
            ])
        else:
            self.shortcut = lambda x: x

    def call(self, inputs):
        x = self.conv1(inputs)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.conv2(x)
        x = self.bn2(x)
        x += self.shortcut(inputs)
        x = self.relu(x)
        return x

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])
    x = layers.Conv2D(3, (7,7), padding='same', activation='relu', kernel_regularizer=l2(0.001))(inputs)

    # basic block
    x = BasicBlock(3, 16)(x)
    x = BasicBlock(16, 32, stride=2)(x)
    x = BasicBlock(32, 64, stride=2)(x)

    # global average pooling
    x = layers.GlobalAveragePooling2D()(x)

    # output
    outputs = layers.Dense(1, activation='sigmoid')(x)

    # define model
    model = keras.Model(inputs=[inputs1, inputs2, inputs3], outputs=outputs)
    model.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])
    return model

这里我们在`MEAN_Spot`函数中使用`BasicBlock`类来构建基本块模块，并且将该模块的输出作为下一个模块的输入。最后使用全局平均池化层和全连接层得到最终的输出。