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。
此后,我想使用tf.saved_model api加载此模型,我认为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',NumericColumn(key='latitude',NumericColumn(key='bidfloor',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'),VocabularyListCategoricalColumn(key='exchangeid',vocabulary_list=('1241','823','1240','1238'),...]
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)
在训练完模型后,我想保存它,所以这里开始产生疑问,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,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)
现在我试图加载它,而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
loaded = tf.saved_model.load('saved/1573144361/')
还有一件事,我试图看一下模型的签名,但是我真的无法理解输入形状的作用
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/predictions/probabilities:0
Method name is: tensorflow/serving/classify