如何使用tf.saved_model加载模型并调用预测函数[TENSORFLOW 2.0 API]

Question

我对tensorflow尤其是2.0版本非常陌生，因为关于该API的示例不足，但它似乎比1.x方便得多到目前为止，我设法使用tf.estimator api训练线性模型，然后设法使用tf.estimator.exporter保存它。

[之后，我想使用tf.saved_model api加载此模型，并且我认为我可以成功完成此操作，但是我对我的过程有一些疑问，因此可以快速浏览一下我的代码：

所以我有一系列使用tf.feature_column api创建的功能，它看起来像这样：

feature_columns = 
[NumericColumn(key='geoaccuracy', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None),
 NumericColumn(key='longitude', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None),
 NumericColumn(key='latitude', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None),
 NumericColumn(key='bidfloor', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None),
 VocabularyListCategoricalColumn(key='adid', vocabulary_list=('115', '124', '139', '122', '121', '146', '113', '103', '123', '104', '147', '114', '149', '148'), dtype=tf.string, default_value=-1, num_oov_buckets=0),
 VocabularyListCategoricalColumn(key='campaignid', vocabulary_list=('36', '31', '33', '28'), dtype=tf.string, default_value=-1, num_oov_buckets=0),
 VocabularyListCategoricalColumn(key='exchangeid', vocabulary_list=('1241', '823', '1240', '1238'), dtype=tf.string, default_value=-1, num_oov_buckets=0),
...]

之后，我以这种方式使用要素列数组定义一个估算器，并对其进行训练。直到这里，没问题。

linear_est = tf.estimator.LinearClassifier(feature_columns=feature_columns)

在训练了模型之后，我想保存它，所以这里引起了疑问，这是我的操作方法，但不确定这是正确的方法：

serving_input_parse = tf.feature_column.make_parse_example_spec(feature_columns=feature_columns)

""" view of the variable : serving_input_parse = 
 {'adid': VarLenFeature(dtype=tf.string),
 'at': VarLenFeature(dtype=tf.string),
 'basegenres': VarLenFeature(dtype=tf.string),
 'bestkw': VarLenFeature(dtype=tf.string),
 'besttopic': VarLenFeature(dtype=tf.string),
 'bidfloor': FixedLenFeature(shape=(1,), dtype=tf.float32, default_value=None),
 'browserid': VarLenFeature(dtype=tf.string),
 'browserlanguage': VarLenFeature(dtype=tf.string)
 ...} """

# exporting the model :
linear_est.export_saved_model(export_dir_base='./saved',
 serving_input_receiver_fn=tf.estimator.export.build_parsing_serving_input_receiver_fn(serving_input_receiver_fn),
 as_text=True)

[现在我尝试加载它，并且我不知道如何使用加载的模型使用例如来自pandas数据帧的原始数据对其进行调用的预测

loaded = tf.saved_model.load('saved/1573144361/')

还有一件事，我试图看一下模型的签名，但我无法真正理解输入形状的状态

MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs:

signature_def['classification']:
  The given SavedModel SignatureDef contains the following input(s):
    inputs['inputs'] tensor_info:
        dtype: DT_STRING
        shape: (-1)
        name: input_example_tensor:0
  The given SavedModel SignatureDef contains the following output(s):
    outputs['classes'] tensor_info:
        dtype: DT_STRING
        shape: (-1, 2)
        name: head/Tile:0
    outputs['scores'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 2)
        name: head/predictions/probabilities:0
  Method name is: tensorflow/serving/classify

signature_def['predict']:
  The given SavedModel SignatureDef contains the following input(s):
    inputs['examples'] tensor_info:
        dtype: DT_STRING
        shape: (-1)
        name: input_example_tensor:0
  The given SavedModel SignatureDef contains the following output(s):
    outputs['all_class_ids'] tensor_info:
        dtype: DT_INT32
        shape: (-1, 2)
        name: head/predictions/Tile:0
    outputs['all_classes'] tensor_info:
        dtype: DT_STRING
        shape: (-1, 2)
        name: head/predictions/Tile_1:0
    outputs['class_ids'] tensor_info:
        dtype: DT_INT64
        shape: (-1, 1)
        name: head/predictions/ExpandDims:0
    outputs['classes'] tensor_info:
        dtype: DT_STRING
        shape: (-1, 1)
        name: head/predictions/str_classes:0
    outputs['logistic'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 1)
        name: head/predictions/logistic:0
    outputs['logits'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 1)
        name: linear/linear_model/linear/linear_model/linear/linear_model/weighted_sum:0
    outputs['probabilities'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 2)
        name: head/predictions/probabilities:0
  Method name is: tensorflow/serving/predict

signature_def['regression']:
  The given SavedModel SignatureDef contains the following input(s):
    inputs['inputs'] tensor_info:
        dtype: DT_STRING
        shape: (-1)
        name: input_example_tensor:0
  The given SavedModel SignatureDef contains the following output(s):
    outputs['outputs'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 1)
        name: head/predictions/logistic:0
  Method name is: tensorflow/serving/regress

signature_def['serving_default']:
  The given SavedModel SignatureDef contains the following input(s):
    inputs['inputs'] tensor_info:
        dtype: DT_STRING
        shape: (-1)
        name: input_example_tensor:0
  The given SavedModel SignatureDef contains the following output(s):
    outputs['classes'] tensor_info:
        dtype: DT_STRING
        shape: (-1, 2)
        name: head/Tile:0
    outputs['scores'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 2)
        name: head/predictions/probabilities:0
  Method name is: tensorflow/serving/classify

Answer 1

看来您在输出的最后一部分中使用了saved_model_cli命令行工具。由此，您有了一个“预测”功能，该功能显示输入类型，列等。执行此操作时，我将看到所有输入列。在您的情况下，它仅显示一个输入，该输入是一个名为examples的字符串。这看起来不正确。

这里是$ saved_model_cli show --dir /somedir/export/exporter/123456789 --all输出的摘录。在输出中，点显示删除的行，因为它们看起来很相似。

signature_def['predict']:
  The given SavedModel SignatureDef contains the following input(s):
    inputs['feature_num_1'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1)
        name: Placeholder_29:0
...
...
 The given SavedModel SignatureDef contains the following output(s):
    outputs['all_class_ids'] tensor_info:
        dtype: DT_INT32
        shape: (-1, 2)
        name: dnn/head/predictions/Tile:0
    outputs['all_classes'] tensor_info:
        dtype: DT_STRING
        shape: (-1, 2)
        name: dnn/head/predictions/Tile_1:0
...
...
  Method name is: tensorflow/serving/predict