keras - 启用急切执行时，AttributeError : Tensor. op 无意义

Question

我正在尝试从头开始实现 RESNET 50。累积所有层后，我调用 tf.keras.Model。但是，它给出了一个错误:

AttributeError: Tensor.op is meaningless when eager execution is enabled.

为了测试，我输入了一个 4-D 张量。 conv_diff_size 和 conv_same_size 是两个具有 con2d 和批归一化层的自定义 block 。我在 Google Colab 上使用 TensorFlow 2.0。

def ResNet50(inputs, classes):
  X = tf.keras.layers.Conv2D(64, kernel_size = (7,7), strides=2, padding='valid', data_format='channels_last', input_shape = inputs.shape)(inputs)
  X = tf.keras.layers.BatchNormalization(axis=-1, momentum=0.9)(X)
  X = tf.keras.layers.MaxPool2D(pool_size=(3, 3), strides=2)(X)

  X = conv_diff_size(X, [64, 64, 256])
  X = conv_same_size(X, [64, 64, 256])
  X = conv_same_size(X, [64, 64, 256])
  
  X = conv_diff_size(X, [128, 128, 512])
  X = conv_same_size(X, [128, 128, 512])
  X = conv_same_size(X, [128, 128, 512])
  X = conv_same_size(X, [128, 128, 512])
  
  X = conv_diff_size(X, [256, 256, 1024])
  X = conv_same_size(X, [256, 256, 1024])
  X = conv_same_size(X, [256, 256, 1024])
  X = conv_same_size(X, [256, 256, 1024])
  X = conv_same_size(X, [256, 256, 1024])
  
  X = conv_diff_size(X, [512, 512, 2048])
  X = conv_same_size(X, [512, 512, 2048])
  X = conv_same_size(X, [512, 512, 2048])
  X = conv_same_size(X, [512, 512, 2048])
  X = conv_same_size(X, [512, 512, 2048])
  X = conv_same_size(X, [512, 512, 2048])
              
  X = tf.keras.layers.AveragePooling2D(pool_size=(2, 2), name = 'avg_pool')(X)
  X = tf.keras.layers.Flatten()(X)
  X = tf.keras.layers.Dense(classes, activation='relu')(X)
  
  model = tf.keras.Model(inputs=X, outputs = X)
  return model

Answer 1

如果您忘记先编译您的模型，有时会发生这种情况。在开始训练之前，请确保您正在运行 model.compile(...)。