服务于restful请求的tensorflow如何发布列表对象

Question

我创建了一个tensorflow模型，并使用tensorflow来部署这个模型，但是当我可以构建restful请求时，模型需要

curl-d'{“实例”:[[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1]}'
-x POST http://localhost:8501/v1/models/shipping_predict:predict

它回调“error”：“instances是一个普通列表，但需要对象列表作为tensorinfo_map所需的多个输入张量”

这是我的模特

# prepare each input head
in_layers = list()
em_layers = list()
#
# customer
in_layer_customer = Input(shape=(1,))
em_layer_customer = Embedding(5000, 10)(in_layer_customer)
em_layer_customer = layers.Reshape([1 * 10])(em_layer_customer)
in_layers.append(in_layer_customer)
em_layers.append(em_layer_customer)

# salesman
in_layer_sale = Input(shape=(1,))
em_layer_sale = Embedding(500, 10)(in_layer_sale)
em_layer_sale = layers.Reshape([1 * 10])(em_layer_sale)
in_layers.append(in_layer_sale)
em_layers.append(em_layer_sale)

# business_type
in_layer_businessType = Input(shape=(1,))
em_layer_businessType = Embedding(100, 10)(in_layer_businessType)
em_layer_businessType = layers.Reshape([1 * 10])(em_layer_businessType)
in_layers.append(in_layer_businessType)
em_layers.append(em_layer_businessType)

# 20
in_layer_20 = Input(shape=(1,))
em_layer_20 = layers.Dense(16, activation='relu')(in_layer_20)
in_layers.append(in_layer_20)
em_layers.append(em_layer_20)

# 40
in_layer_40 = Input(shape=(1,))
em_layer_40 = layers.Dense(16, activation='relu')(in_layer_40)
in_layers.append(in_layer_40)
em_layers.append(em_layer_40)

# 45
in_layer_45 = Input(shape=(1,))
em_layer_45 = layers.Dense(16, activation='relu')(in_layer_45)
in_layers.append(in_layer_45)
em_layers.append(em_layer_45)

# other 
in_layer_other = Input(shape=(1,))
em_layer_other = layers.Dense(16, activation='relu')(in_layer_other)
in_layers.append(in_layer_other)
em_layers.append(em_layer_other)

# dischargingPort
in_layer_dischargingPortName = Input(shape=(1,))
em_layer_dischargingPortName = Embedding(5000, 10)(in_layer_dischargingPortName)
em_layer_dischargingPortName = layers.Reshape([1 * 10])(em_layer_dischargingPortName)
in_layers.append(in_layer_dischargingPortName)
em_layers.append(em_layer_dischargingPortName)

# MBL Method
in_layer_mbl = Input(shape=(1,))
em_layer_mbl = layers.Dense(16, activation='relu')(in_layer_mbl)
in_layers.append(in_layer_mbl)
em_layers.append(em_layer_mbl)

# atdMouth
in_layer_atdMouth = Input(shape=(1,))
em_layer_atdMouth = Embedding(100, 10)(in_layer_atdMouth)
em_layer_atdMouth = layers.Reshape([1 * 10])(em_layer_atdMouth)
in_layers.append(in_layer_atdMouth)
em_layers.append(em_layer_atdMouth)

merge = Concatenate()(em_layers)
dense = Dense(32, activation='relu')(merge)

output = Dense(1)(dense)
model = Model(inputs=in_layers, outputs=output)

Answer 1

实例是一个普通的列表，但需要将对象列表作为多个输入张量

不管出于什么原因，听起来你的模型都在期待命名张量。这不是我以前使用过的东西，但似乎有另一种方式向您的模型发送请求。

curl -X POST -i 'http://192.168.1.16:8501/v1/models/export:predict' --data '
{
  "signature_name":  "serving_default",
  "inputs": [
    {
      "tokens_0" :["text text text text text text text text text text"],
      "length_0": [1],
      "tokens_1": ["01 01 01 01 01 01 01 01 01 01"],
      "length_1": [1],
      "tokens_2": ["4 4 4 1 1 4 4 4 4 4"],
      "length_2": [1]
    }
  ]
}'

我刚刚从这里复制了这个例子（归功于@ishaan-sharma）。

您的模型不是微不足道的，所以我不会尝试为您创建准确的请求。如果不确定张量名称等，可以使用saved_model_cli签出预期的形状：

saved_model_cli show [-h] --dir DIR [--all]