我想用Keras制作ResNet50模型。数据集为CIFAR10。在我的猜测中，错误是因为跳过层的维度。

class ResidualUnit50(keras.layers.Layer): # ResNet-50
  def __init__(self, filters, strides=1, activation="relu", **kwargs):
    super().__init__(**kwargs)
    self.activation = keras.activations.get(activation) # relu activation in this example

    self.main_layers = [
          keras.layers.Conv2D(filters, 1, strides=strides, padding="same", use_bias=False),
          keras.layers.BatchNormalization(),
          keras.layers.Activation(self.activation),
          keras.layers.Conv2D(filters, 3, strides = 1, padding="same", use_bias=False),
          keras.layers.BatchNormalization(),
          keras.layers.Activation(self.activation),
          keras.layers.Conv2D(4 * filters, 1,  strides = 1, padding="same", use_bias=False),
          keras.layers.BatchNormalization(),
      ]

    self.skip_layers = [] # to make short cut
    if strides > 1:
      self.skip_layers = [
        keras. layers.Conv2D(4 * filters, 1, strides=strides, padding="same", use_bias=False),
      keras. layers.BatchNormalization()]
  def call (self, inputs):
    Z = inputs
    for layer in self.main_layers:
      Z = layer(Z)
    skip_Z = inputs
    for layer in self.skip_layers: # adding short-cut
      skip_Z = layer(skip_Z)
    return self.activation(Z + skip_Z)

model = keras.models.Sequential()
model.add(keras.layers.Conv2D(64, 7, strides=2, input_shape= [32, 32, 3], padding="same", use_bias = False))

model.add(keras.layers.BatchNormalization())
model.add(keras.layers.Activation("relu"))
model.add(keras.layers.MaxPool2D(pool_size=3, strides=2, padding="same"))
prev_filters = 64

for filters in [64] * 3 + [128] * 4 + [256] * 6 + [512] * 3:
# [64, 64, 64, 128, 128, 128, 128, 256, 256, 256, 256, 256, 256, 512, 512, 512]
  strides = 1 if filters == prev_filters else 2
  model.add(ResidualUnit50(filters, strides=strides))
  prev_filters = filters

model.add(keras.layers. GlobalAvgPool2D()) 
model.add(keras.layers.Flatten())
model.add(keras.layers.Dense (10, activation="softmax"))

但是

返回如下错误：
请让我知道为什么会发生这个错误以及如何修复它。

**添加更多详细信息

那么我如何修改我的代码呢？

if strides > 1 or prev_filters != 4*filters: 
      print()
      self.skip_layers = [
        keras.layers.Conv2D(4 * filters, 1, strides=strides, padding="same", use_bias=False),
        keras.layers.BatchNormalization()
      ]

for filters in [64] * 3 + [128] * 4 + [256] * 6 + [512] * 3:
  strides = 1 if filters == prev_filters else 2
  model.add(ResidualUnit50(filters, strides=strides))
  prev_filters = 4 * filters

我只是修改了一下，但是看起来不对。这段代码工作，但验证损失是如此之大（超过正常范围）