使用tensorflow 2在模型子类中的ValueError

如何解决使用tensorflow 2在模型子类中的ValueError

我正在尝试使用keras中的Model子类实现WideResnet。我不明白我的代码有什么问题：

class ResidualBlock(layers.Layer):
  def __init__(self,filters,kernel_size,dropout,dropout_percentage,strides=1,**kwargs):
    super(ResidualBlock,self).__init__(**kwargs)
          
    self.conv_1 = layers.Conv2D(filters,(1,1),strides=strides)
    self.bn_1 = layers.BatchNormalization()
    self.rel_1 = layers.ReLU()
    self.conv_2 = layers.Conv2D(filters,padding="same",strides=strides)
    self.dropout = layers.Dropout(dropout_percentage)
    self.bn_2 = layers.BatchNormalization()
    self.rel_2 = layers.ReLU()
    self.conv_3 = layers.Conv2D(filters,padding="same")
    
    self.add = layers.Add()
    self.dropout = dropout
    self.strides = strides

  def call(self,inputs):
    x = inputs

    if self.strides > 1:
      x = self.conv_1(x)
    res_x = self.bn_1(x)
    res_x = self.rel_1(x)
    res_x = self.conv_2(x)
    if self.dropout:
      res_x = self.dropout(x)
    res_x = self.bn_2(x)
    res_x = self.rel_2(x)
    res_x = self.conv_3(x)
    inputs = self.add([x,res_x])
    return inputs

class WideResidualNetwork(models.Model):
  def __init__(self,input_shape,n_classes,d,k,kernel_size=(3,3),dropout=False,dropout_percentage=0.3,**kwargs):
    
    super(WideResidualNetwork,self).__init__(**kwargs)

    if (d-4)%6 != 0:
      raise ValueError('Please choose a correct depth!')

    self.rel_1 = layers.ReLU()
    self.conv_1 = layers.Conv2D(16,(3,padding='same')
    self.conv_2 = layers.Conv2D(16*k,1))
    self.dense = layers.Dense(n_classes)

    self.dropout = dropout
    self.dropout_percentage = dropout_percentage
    self.N = int((d - 4) / 6)
    self.k = k
    self.d = d
    self.kernel_size = kernel_size

  def build(self,input_shape):
    self.bn_1 = layers.BatchNormalization(input_shape=input_shape)

  def call(self,inputs):
    x = self.bn_1(inputs)
    x = self.rel_1(x)
    x = self.conv_1(x)
    x = self.conv_2(x)

    for _ in range(self.N):
      x = ResidualBlock(16*self.k,self.kernel_size,self.dropout,self.dropout_percentage)(x)
    
    x = ResidualBlock( 32*self.k,self.dropout_percentage,strides=2)(x)

    for _ in range(self.N-1):
      x = ResidualBlock( 32*self.k,self.dropout_percentage)(x)

    x = ResidualBlock( 64*self.k,strides=2)(x)
    
    for _ in range(self.N-1):
      x = ResidualBlock( 64*self.k,self.dropout_percentage)(x)
    
    x = layers.GlobalAveragePooling2D()(x)
    x = self.dense(x)
    x = layers.Activation("softmax")(x)

    return x

当我尝试以这种方式拟合模型时：

(x_train,y_train),(x_test,y_test) = tf.keras.datasets.cifar10.load_data()
model = WideResidualNetwork(x_train[0].shape,10,28,1)
x_train,x_test = x_train/255.,x_test/255.
model = WideResidualNetwork(x_train[0].shape,1)
model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])

epochs = 40
batch_size = 64
validation_split = 0.2
h = model.fit(x_train,y_train,epochs=epochs,batch_size=batch_size,validation_split=validation_split)

我遇到以下错误：

...
 <ipython-input-26-61c1bdb3546c>:31 call  *
        x = ResidualBlock(16*self.k,self.dropout_percentage)(x)
    <ipython-input-9-3fea1e77cb6e>:23 call  *
        res_x = self.bn_1(x)
...
ValueError: tf.function-decorated function tried to create variables on non-first call.

所以我不明白问题出在哪里，我也尝试将初始化移到构建中，但是没有结果，错误仍然存​​在。我的知识可能有些不足 预先谢谢你

解决方法

您正在将ResidualBlocks，GlobalAveragePooling2D和Activation层初始化为call方法。像将它们移到其他层一样，尝试将它们移到init中，它不应该给您该错误。