Question

我已成功使用以下代码以 SavedModel 格式导出了seq2seq模型

  source_tokens_ph = tf.placeholder(dtype=tf.string, shape=(1, None))
  source_len_ph = tf.placeholder(dtype=tf.int32, shape=(1,))

  features_serve = {
    "source_tokens": source_tokens_ph,
    "source_len": source_len_ph
  }

  experiment = PatchedExperiment(
  ...

  export_strategies = [saved_model_export_utils.make_export_strategy(serving_input_fn = build_default_serving_input_fn(features_serve))]
  )

Saved_model_cli显示导出的文件中存在以下SignatureDefs

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

signature_def['default_input_alternative:default_output_alternative']:
The given SavedModel SignatureDef contains the following input(s):
inputs['source_ids'] tensor_info:
    dtype: DT_INT64
    shape: (-1, -1)
    name: model/att_seq2seq/hash_table_1_Lookup:0
inputs['source_len'] tensor_info:
    dtype: DT_INT32
    shape: (-1)
    name: model/att_seq2seq/Minimum:0
inputs['source_tokens'] tensor_info:
    dtype: DT_STRING
    shape: (-1, -1)
    name: model/att_seq2seq/strided_slice:0
The given SavedModel SignatureDef contains the following output(s):
outputs['attention_context'] tensor_info:
    dtype: DT_FLOAT
    shape: (-1, -1, 512)
    name: model/att_seq2seq/transpose_4:0
outputs['attention_scores'] tensor_info:
    dtype: DT_FLOAT
    shape: unknown_rank
    name: model/att_seq2seq/transpose_2:0
outputs['cell_output'] tensor_info:
    dtype: DT_FLOAT
    shape: (-1, -1, 256)
    name: model/att_seq2seq/transpose_1:0
outputs['features.source_ids'] tensor_info:
    dtype: DT_INT64
    shape: (-1, -1)
    name: model/att_seq2seq/hash_table_1_Lookup:0
outputs['features.source_len'] tensor_info:
    dtype: DT_INT32
    shape: (-1)
    name: model/att_seq2seq/Minimum:0
outputs['features.source_tokens'] tensor_info:
    dtype: DT_STRING
    shape: (-1, -1)
    name: model/att_seq2seq/strided_slice:0
outputs['logits'] tensor_info:
    dtype: DT_FLOAT
    shape: (-1, -1, 42)
    name: model/att_seq2seq/transpose:0
outputs['predicted_ids'] tensor_info:
    dtype: DT_INT32
    shape: (-1, -1)
    name: model/att_seq2seq/transpose_3:0
outputs['predicted_tokens'] tensor_info:
    dtype: DT_STRING
    shape: (-1, -1)
    name: model/att_seq2seq/hash_table_3_Lookup:0
Method name is: tensorflow/serving/predict

signature_def['serving_default']:
The given SavedModel SignatureDef contains the following input(s):
inputs['source_ids'] tensor_info:
    dtype: DT_INT64
    shape: (-1, -1)
    name: model/att_seq2seq/hash_table_1_Lookup:0
inputs['source_len'] tensor_info:
    dtype: DT_INT32
    shape: (-1)
    name: model/att_seq2seq/Minimum:0
inputs['source_tokens'] tensor_info:
    dtype: DT_STRING
    shape: (-1, -1)
    name: model/att_seq2seq/strided_slice:0
The given SavedModel SignatureDef contains the following output(s):
outputs['attention_context'] tensor_info:
    dtype: DT_FLOAT
    shape: (-1, -1, 512)
    name: model/att_seq2seq/transpose_4:0
outputs['attention_scores'] tensor_info:
    dtype: DT_FLOAT
    shape: unknown_rank
    name: model/att_seq2seq/transpose_2:0
outputs['cell_output'] tensor_info:
    dtype: DT_FLOAT
    shape: (-1, -1, 256)
    name: model/att_seq2seq/transpose_1:0
outputs['features.source_ids'] tensor_info:
    dtype: DT_INT64
    shape: (-1, -1)
    name: model/att_seq2seq/hash_table_1_Lookup:0
outputs['features.source_len'] tensor_info:
    dtype: DT_INT32
    shape: (-1)
    name: model/att_seq2seq/Minimum:0
outputs['features.source_tokens'] tensor_info:
    dtype: DT_STRING
    shape: (-1, -1)
    name: model/att_seq2seq/strided_slice:0
outputs['logits'] tensor_info:
    dtype: DT_FLOAT
    shape: (-1, -1, 42)
    name: model/att_seq2seq/transpose:0
outputs['predicted_ids'] tensor_info:
    dtype: DT_INT32
    shape: (-1, -1)
    name: model/att_seq2seq/transpose_3:0
outputs['predicted_tokens'] tensor_info:
    dtype: DT_STRING
    shape: (-1, -1)
    name: model/att_seq2seq/hash_table_3_Lookup:0
Method name is: tensorflow/serving/predict

我使用Tensorflow Serving连接了模型，并发送了以下请求，

{
    "inputs": {
        "source_tokens": "[['DATE','OF','BIRT']]",
        "source_ids": [],
        "source_len": [3]
    }
}

但是，它返回的结果为

{ “错误”：“模型/ att_seq2seq /最小：0被送入和获取。” }

参考该错误后，我可以看到可能由于同一张量fed and fetched而出现问题。

分析SignatureDef显示，model/att_seq2seq/Minimum:0属于inputs['source_len']和outputs['features.source_len']

我该如何解决？可以不获取outputs['features.source_len']吗？

如何为该存储库中使用的实验API手动分配 SignatureDefs ？

错误模型/ att_seq2seq / Minimum：0被同时获取和获取

0 个答案: