使用 tensorflow 2 进行模型子类化时的 ValueError

Question

我正在尝试使用 keras 中的模型子类化来实现 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, kernel_size, 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, kernel_size, 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, strides=1, **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, 3), padding='same')
    self.conv_2 = layers.Conv2D(16*k, (1, 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.kernel_size, self.dropout, self.dropout_percentage, strides=2)(x)

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

    x = ResidualBlock( 64*self.k, self.kernel_size, self.dropout, self.dropout_percentage, strides=2)(x)
    
    for _ in range(self.N-1):
      x = ResidualBlock( 64*self.k, self.kernel_size, self.dropout, 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, 10, 28, 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)

我收到以下错误：

...
 <ipython-input-26-61c1bdb3546c>:31 call  *
        x = ResidualBlock(16*self.k, self.kernel_size, self.dropout, 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.

所以一直没搞明白是哪里出了问题，我也试过将初始化移到构建中，但是没有结果，错误依旧。可能我的知识有一些差距提前谢谢你

Answer 1

您正在将 ResidualBlocks、GlobalAveragePooling2D 和 Activation 层初始化到调用方法中。尝试将它们移动到 init 中，就像你对其他层所做的那样，它不应该给你那个错误。

使用 tensorflow 2 进行模型子类化时的 ValueError

ValueError in model subclassing with tensorflow 2

python

tensorflow

keras

keras-layer

tf.keras