我正在使用tfx管道来训练和评估自动编码器。我拥有的数据基本上是5个大小为（15，1）的数组，我将它们连接并放在一起并传递给模型。
为了跟踪训练数据，我在ExampleGen组件中定义了这些参数的平均值。因此，我的输入特征中有feature1和feature1_mean，但是，在Transform组件之后，我从数据中删除了*_mean特征。
现在，在我训练我的模型并希望将其传递给Evaluator之后，出现了这个错误：

unable to prepare inputs for evaluation: input_specs={
'feature1': TensorSpec(shape=(None, 15, 1), dtype=tf.float32, name=None),
'feature2': TensorSpec(shape=(None, 15, 1), dtype=tf.float32, name=None),
'feature3': TensorSpec(shape=(None, 15, 1), dtype=tf.float32, name=None),
'feature4': TensorSpec(shape=(None, 15, 1), dtype=tf.float32, name=None)}, 
features={
'feature1_mean': array([3.4559317, 3.528199 , 3.3727243, 3.0274842, 3.2321723, 3.339905 , 3.3501785, 2.987716 , 3.236495 , 3.5900073, 3.1439974, 3.1659212, ...], dtype=float32), 
'feature2_mean': array([1.5840595 , 1.6105878 , 1.5401138 , 1.2408142 , 1.2962327 ,], dtype=float32), 
'feature3_mean': array([1.5840595 , 1.6105878 , 1.5401138 , 1.2408142 , 1.2962327 ,....]}
[while running 'ExtractEvaluateAndWriteResults/ExtractAndEvaluate/EvaluateMetricsAndPlots/ComputeMetricsAndPlots()/CombineMetricsPerSlice/WindowIntoDiscarding']

下面是我在eval_config中使用的配置：

eval_config = tfma.EvalConfig(
    model_specs=[
        tfma.ModelSpec(
            signature_name='serving_default',
            label_key='feature1_mean',
            preprocessing_function_names=['transform_features'],
            )
        ],
    metrics_specs=[
        tfma.MetricsSpec(
            metrics=[
                tfma.MetricConfig(class_name='ExampleCount'),
            ]
        )
    ],
    slicing_specs=[
        tfma.SlicingSpec()
    ])

我只是在这里传递feature1_mean作为一个虚拟参数名，因为实际上我没有标签键，因为它是一个无监督学习模型。
我保存的签名是：

def _get_tf_examples_serving_signature(model, tf_transform_output):
    """Returns a serving signature that accepts `tensorflow.Example`."""

    # We need to track the layers in the model in order to save it.
    # TODO(b/162357359): Revise once the bug is resolved.
    model.tft_layer_inference = tf_transform_output.transform_features_layer()

    @tf.function(input_signature=[
      tf.TensorSpec(shape=[None], dtype=tf.string, name='examples')
    ])
    def serve_tf_examples_fn(serialized_tf_example):
        """Returns the output to be used in the serving signature."""
        raw_feature_spec = tf_transform_output.raw_feature_spec()
        # Remove label feature since these will not be present at serving time.
        raw_features = tf.io.parse_example(serialized_tf_example, raw_feature_spec)
        
        raw_features.pop('feature1_mean')
        raw_features.pop('feature2_mean')
        raw_features.pop('feature3_mean')
        raw_features.pop('feature4_mean')
        transformed_features = model.tft_layer_inference(raw_features)
        logging.info('serve_transformed_features = %s', transformed_features)
        result = model(transformed_features)
        # TODO(b/154085620): Convert the predicted labels from the model using a
        # reverse-lookup (opposite of transform.py).
        return {'outputs': result}

    return serve_tf_examples_fn

def _get_transform_features_signature(model, tf_transform_output):
    """Returns a serving signature that applies tf.Transform to features."""

    # We need to track the layers in the model in order to save it.
    # TODO(b/162357359): Revise once the bug is resolved.
    model.tft_layer_eval = tf_transform_output.transform_features_layer()

    @tf.function(input_signature=[
      tf.TensorSpec(shape=[None], dtype=tf.string, name='examples')
    ])
    def transform_features_fn(serialized_tf_example):
        """Returns the transformed_features to be fed as input to evaluator."""
        raw_feature_spec = tf_transform_output.raw_feature_spec()
        raw_features = tf.io.parse_example(serialized_tf_example, raw_feature_spec)
        transformed_features = model.tft_layer_eval(raw_features)
        logging.info('eval_transformed_features = %s', transformed_features)
        return transformed_features

    return transform_features_fn

如果你能帮我解决这个问题，我将非常感激。
谢谢。