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 代码报错ValueError: Layer weight shape (3, 3, 16, 16) not compatible with provided weight shape (16,) 如何修改

这个错误是由于某一层的权重形状不兼容而引起的。可能是由于您的模型中有一些层的参数形状与先前训练的模型不匹配，或者是由于您的代码中存在错误。

根据代码，似乎是BasicBlock中的某一层权重形状不匹配。您可以检查BasicBlock类中的权重形状，并确保它们与您的模型中的相应层的权重形状匹配。

如果还有问题，请提供更多关于您的代码和错误的信息，以便更好地帮助您解决问题。

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。