用于CIFAR100数据集的Resnet18的低精度为1％。可能是什么原因？

Question

我正在尝试为CIFAR100数据制作一个resnet18，如下所示：

class MyModel(Model):
  def __init__(self):
    super(MyModel, self).__init__()
    self.conv1 = Conv2D(64, (3,3), strides = (1,1), padding = 'same', activation='linear',input_shape=x_train.shape[1:])
    self.bn1 = BatchNormalization()
    self.RL = ReLU()
    self.avg = AveragePooling2D((4,4))
    self.flatten = Flatten()
    self.FC = Dense(100, activation='softmax')

  def make_BB(self, x,num_filter, size_decrease):
    if size_decrease == True:
      C1 = Conv2D(num_filter, (3,3),padding = 'same',strides = (2,2),activation = 'linear')
      B1 = BatchNormalization()
      R1 = ReLU()
      C2 = Conv2D(num_filter, (3,3),padding = 'same',strides = (1,1),activation = 'linear')
      B2 = BatchNormalization()
      C3 = Conv2D(num_filter, (1,1),padding= 'same', strides = (2,2),activation = 'linear')
      B3 = BatchNormalization()
      forward = B2(C2(R1(B1(C1(x))))) + B3(C3(x))
      return forward
    else:
      C1 = Conv2D(num_filter, (3,3),padding = 'same',strides = (1,1),activation = 'linear')
      B1 = BatchNormalization()
      R1 = ReLU()
      C2 = Conv2D(num_filter, (3,3),padding = 'same',strides = (1,1),activation = 'linear')
      B2 = BatchNormalization()
      forward = B2(C2(R1(B1(C1(x))))) + x
      return forward


  def call(self, x):
    x = self.conv1(x)
    x = self.bn1(x)
    x = self.RL(x)

    # BasicBlock1
    x = self.make_BB(x,64,False)
    x = self.make_BB(x,64,False)
    # BasicBlock2
    x = self.make_BB(x,128,True)
    x = self.make_BB(x,128,False)
    # BasicBlock3
    x = self.make_BB(x,256,True)
    x = self.make_BB(x,256,False)
    # BasicBlock4
    x = self.make_BB(x,512,True)
    x = self.make_BB(x,512,False)

    x = self.avg(x)
    x = self.flatten(x)
    return self.FC(x)

  def func(self):
    x = tf.keras.layers.Input(shape=(32, 32, 3))
    return Model(inputs=[x], outputs=self.call(x))

model = MyModel()


optimizer = tf.keras.optimizers.Adam(0.01)
model.compile(optimizer= optimizer,
          loss='sparse_categorical_crossentropy',
          metrics=['sparse_categorical_accuracy'])
model.fit(x_train,y_train, epochs = 50)

但是此模型记录了1％的准确度，这意味着它什么都不学。

我尝试了更简单的模型，并且奏效了。

我还尝试将学习率更改为0.1、0.001、0.0005等，但所有结果都相同。

我想make_BB部分做错了，但我不知道自己做错了什么，甚至找不到我的错误。

我做错了什么？