{'error': 'output_shape 的元素数量不正确：2 应该是：1\n\t [[{{node transform/SparseToDense_3}}]]'} 当前请求依赖项我的模型我的代码输出 (venv) C:\Users\User\PycharmProjects\mlops-on-gcp-master\serving_model\1616659120>saved_m

分类：编程问答

如何解决{'error': 'output_shape 的元素数量不正确：2 应该是：1\n\t [[{{node transform/SparseToDense_3}}]]'} 当前请求依赖项我的模型我的代码输出 (venv) C:\Users\User\PycharmProjects\mlops-on-gcp-master\serving_model\1616659120>saved_m

我尝试通过 tensorflow 请求我的模型来进行预测，但我一直遇到这个错误： {'error': 'output_shape has incorrect number of elements: 2 should be: 1\n\t [[{{node transform/SparseToDense_3}}]]'}

我尝试了所有方法，但我不明白我的请求的结构是什么

当前请求

import tensorflow as tf

# server URL
model_server_url = 'http://localhost:8501/v1/models/saved_model:predict'
import requests
import base64
import json
import numpy as np

    feature = {
        'lower_xf': tf.train.Feature(float_list=tf.train.FloatList(value=[1.0])),'upper_xf': tf.train.Feature(float_list=tf.train.FloatList(value=[2.0])),'special_xf': tf.train.Feature(float_list=tf.train.FloatList(value=[3.0])),'len_xf': tf.train.Feature(float_list=tf.train.FloatList(value=[4.0])),"label": tf.train.Feature(float_list=tf.train.FloatList(value=[1.0])),}

    example_proto = tf.train.Example(features=tf.train.Features(feature=feature))
    example_proto = example_proto.SerializeToString()

    json_data = {
        "signature_name": "serving_default","inputs": {"examples": {"b64": base64.b64encode(example_proto).decode('utf-8')}
                   }
    }
    resp = requests.post(model_server_url,json=json_data)
    print(resp.json())

依赖项

python = ">=3.8,<3.9"
notebook = "^6.2.0"
tensorflow = "^2.4.1"
tensorflow-data-validation = "^0.28.0"
tfx = "^0.28.0"
jupyter = "^1.0.0"

我的模型

1616058367.zip

我的代码

code.zip

http://localhost:8501/v1/models/saved_model/metadata 的输出

{
    "model_spec": {
        "name": "saved_model","signature_name": "","version": "1616058367"
    },"metadata": {
        "signature_def": {
            "signature_def": {
                "serving_default": {
                    "inputs": {
                        "examples": {
                            "dtype": "DT_STRING","tensor_shape": {
                                "dim": [
                                    {
                                        "size": "-1","name": ""
                                    }
                                ],"unknown_rank": false
                            },"name": "serving_default_examples:0"
                        }
                    },"outputs": {
                        "output_0": {
                            "dtype": "DT_FLOAT","name": ""
                                    },{
                                        "size": "2","name": "StatefulPartitionedCall:0"
                        }
                    },"method_name": "tensorflow/serving/predict"
                },"__saved_model_init_op": {
                    "inputs": {},"outputs": {
                        "__saved_model_init_op": {
                            "dtype": "DT_INVALID","tensor_shape": {
                                "dim": [],"unknown_rank": true
                            },"name": "NoOp"
                        }
                    },"method_name": ""
                }
            }
        }
    }
}

输出 (venv) C:\Users\User\PycharmProjects\mlops-on-gcp-master\serving_model\1616659120>saved_model_cli show --dir . --all 

2021-03-25 15:34:03.250351: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found
2021-03-25 15:34:03.250531: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.

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

signature_def['__saved_model_init_op']:
  The given SavedModel SignatureDef contains the following input(s):
  The given SavedModel SignatureDef contains the following output(s):
    outputs['__saved_model_init_op'] tensor_info:
        dtype: DT_INVALID
        shape: unknown_rank
        name: NoOp
  Method name is:

signature_def['serving_default']:
  The given SavedModel SignatureDef contains the following input(s):
    inputs['len_xf'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1)
        name: serving_default_len_xf:0
    inputs['lower_xf'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1)
        name: serving_default_lower_xf:0
    inputs['special_xf'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1)
        name: serving_default_special_xf:0
    inputs['upper_xf'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1)
        name: serving_default_upper_xf:0
  The given SavedModel SignatureDef contains the following output(s):
    outputs['dense_1'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1,2)
        name: StatefulPartitionedCall:0
  Method name is: tensorflow/serving/predict
2021-03-25 15:34:07.723640: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices,tf_xla_enable_xla_devices not set
2021-03-25 15:34:07.725820: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'nvcuda.dll'; dlerror: nvcuda.dll not found
2021-03-25 15:34:07.726446: W tensorflow/stream_executor/cuda/cuda_driver.cc:326] failed call to cuInit: UNKNOWN ERROR (303)
2021-03-25 15:34:07.729019: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: DESKTOP-KKTTVVP
2021-03-25 15:34:07.729215: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: DESKTOP-KKTTVVP

Defined Functions:
  Function Name: '__call__'
    Option #1
      Callable with:
        Argument #1
          DType: dict
          Value: {'len_xf': TensorSpec(shape=(None,),dtype=tf.float32,name='inputs/len_xf'),'lower_xf': TensorSpec(shape=(None,name='inputs/lower_xf'),'special_xf': TensorSpec(shape=(None,name='inputs/special_xf'),'upper_xf': TensorSpec(shape=(None,name='inputs/upper_xf')}
        Argument #2
          DType: bool
          Value: False
        Argument #3
          DType: NoneType
          Value: None
    Option #2
      Callable with:
        Argument #1
          DType: dict
          Value: {'len_xf': TensorSpec(shape=(None,name='len_xf'),name='lower_xf'),name='specia
l_xf'),name='upper_xf')}
        Argument #2
          DType: bool
          Value: False
        Argument #3
          DType: NoneType
          Value: None
    Option #3
      Callable with:
        Argument #1
          DType: dict
          Value: {'len_xf': TensorSpec(shape=(None,name='upper_xf')}
        Argument #2
          DType: bool
          Value: True
        Argument #3
          DType: NoneType
          Value: None
    Option #4
      Callable with:
        Argument #1
          DType: dict
          Value: {'len_xf': TensorSpec(shape=(None,name='inputs/upper_xf')}
        Argument #2
          DType: bool
          Value: True
        Argument #3
          DType: NoneType
          Value: None

  Function Name: '_default_save_signature'
    Option #1
      Callable with:
        Argument #1
          DType: dict
          Value: {'len_xf': TensorSpec(shape=(None,name='upper_xf')}

  Function Name: 'call_and_return_all_conditional_losses'
    Option #1
      Callable with:
        Argument #1
          DType: dict
          Value: {'len_xf': TensorSpec(shape=(None,name='upper_xf')}
        Argument #2
          DType: bool
          Value: False
        Argument #3
          DType: NoneType
          Value: None
    Option #2
      Callable with:
        Argument #1
          DType: dict
          Value: {'len_xf': TensorSpec(shape=(None,name='upper_xf')}
        Argument #2
          DType: bool
          Value: True
        Argument #3
          DType: NoneType
          Value: None
    Option #3
      Callable with:
        Argument #1
          DType: dict
          Value: {'len_xf': TensorSpec(shape=(None,name='inputs/upper_xf')}
        Argument #2
          DType: bool
          Value: False
        Argument #3
          DType: NoneType
          Value: None
    Option #4
      Callable with:
        Argument #1
          DType: dict
          Value: {'len_xf': TensorSpec(shape=(None,name='inputs/upper_xf')}
        Argument #2
          DType: bool
          Value: True
        Argument #3
          DType: NoneType
          Value: None

我在 Docker version 20.10.5,build 55c4c88 上部署我的模型 我不知道问题是我的请求还是我的代码，但我真的不明白

感谢您的帮助 =)

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