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 模型文件报错 alueError: Layer weight shape (16,) not compatible with provided weight shape (3, 3, 16, 16)

这个报错是因为模型中的某一层的权重形状与提供的权重形状不匹配。你可以通过查看模型中的每一层的权重形状，并检查提供的权重形状是否与它们匹配来定位问题。

根据你提供的代码，可能是 BasicBlock 层中的某个权重形状不匹配。你可以通过在 BasicBlock 中添加打印语句或使用调试器来查看每个层的权重形状。如果发现某个权重形状不匹配，可以尝试更改模型的权重文件或更改该层的权重形状。

另外，你可以尝试重新训练模型，看看是否可以解决问题。如果你无法解决问题，请提供更多的上下文和代码，以便我可以更好地理解你的问题并给出更具体的建议。

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定义basic block(self, in_channels, out_channels, stride=1)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

可以在`def MEAN_Spot(opt)`中直接调用定义好的`BasicBlock`类，具体方法是在`def MEAN_Spot(opt)`中实例化`BasicBlock`类，然后将实例化的对象作为一个层连接到`conv1`之后。具体代码实现如下：

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

    def call(self, inputs, training=False):
        identity = inputs

        x = self.conv1(inputs)
        x = self.bn1(x, training=training)
        x = self.relu(x)

        x = self.conv2(x)
        x = self.bn2(x, training=training)

        identity = self.downsample(identity)

        x += identity
        x = self.relu(x)

        return x


def MEAN_Spot(opt):
    inputs1 = keras.layers.Input(shape=(42, 42, 1))
    inputs2 = keras.layers.Input(shape=(42, 42, 1))
    inputs3 = keras.layers.Input(shape=(42, 42, 1))
    inputs = keras.layers.Concatenate()([inputs1, inputs2, inputs3])
    conv1 = keras.layers.Conv2D(3, (7, 7), padding='same', activation='relu', kernel_regularizer=l2(0.001))(inputs)
    ba1 = BasicBlock(out_channels=64, kernel_size=3, strides=1)(conv1)
    ba2 = BasicBlock(out_channels=64, kernel_size=3, strides=1)(ba1)
    att = BasicBlock(out_channels=64, kernel_size=3, strides=1)(ba2)

    merged_conv = keras.layers.Conv2D(8, (5, 5), padding='same', activation='relu', kernel_regularizer=l2(0.1))(att)
    merged_pool = keras.layers.MaxPooling2D(pool_size=(2, 2), padding='same', strides=(2, 2))(merged_conv)
    flat = keras.layers.Flatten()(merged_pool)
    flat_do = keras.layers.Dropout(0.2)(flat)
    outputs = keras.layers.Dense(1, activation='linear', name='spot')(flat_do)

    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